Categories: Thought Leadership

Downtime is costing Global 2000 companies $400 billion a year¹. That’s not just a technical concern—it’s a direct hit to revenue, reputation, and resilience.

A major contributor to that cost is what happens after an incident begins: delayed root cause analysis, misdirected investigations, and manual recovery workflows that burn time and stall progress. Even with modern observability in place, diagnosing the issue and responding quickly remains one of the most time-consuming, error-prone tasks in IT.

That’s the big gap where autonomous response can drive the biggest impact.

The Real Cost of Manual Root Cause Analysis

Most teams today rely on four or more observability platforms² ³, yet incident diagnosis remains the top challenge for SREs. That gap between visibility and action has very real consequences:

• Downtime costs scale quickly. SLA penalties, overtime, and lost productivity add up—especially when teams are pulled into extended triage loops. 
• Teams fix symptoms, not causes. Quick patches often target surface-level issues. When the root cause goes untreated, incidents recur—or trigger entirely new ones downstream. 
• Misdirected investigations stall recovery. Infra teams may suspect application errors, while app teams chase infrastructure bugs. Entire teams can burn days debugging the wrong layer. 
• Engineering time gets swallowed by ops. Instead of building the next release, developers spend hours in postmortems and root cause hunts—delaying delivery and draining morale.

One platform team we worked with spent over a week chasing what they thought was an application issue. When they brought in Hawkeye, our AI SRE agent, it found the real cause—a misconfigured readiness probe causing cascading pod restarts—and recommended a fix in under four minutes.

This isn’t an edge case. It’s the norm in modern enterprise systems.

Modern ITOps Needs Autonomous Investigation

Today’s enterprise environments are complex—spanning cloud services, containerized applications, microservices, and legacy systems. As these systems grow and change constantly, diagnosing incidents has only become harder. Teams are overwhelmed—not by lack of data, but by the time it takes to interpret it. The alert volume is high and context is fragmented across systems.

This is why incident response must evolve—from reactive analysis to intelligent automation.

We built Hawkeye to act as an agentic AI teammate for IT operations teams. It doesn’t just summarize data. It investigates incidents from the moment they’re triggered—correlating telemetry, analyzing dependencies, and identifying the most probable root cause. It then recommends targeted remediations and proactive steps to prevent recurrence. This isn’t replacing your engineers. It’s returning their time, accelerating RCA, and removing the manual drag that slows down every release.

Building Agentic Workflows into the Stack You Already Use

Adopting autonomous response shouldn’t require ripping out your existing stack. In fact, success depends on embedding intelligent agents into the workflows your teams already trust—without creating new silos or operational overhead.

Engineering and platform leaders should prioritize solutions that: 

• Integrate natively with your observability, monitoring and incident management systems
• Are built with enterprise governance and security in mind
• Deliver insight where teams already work, whether that’s in Slack, Datadog, PagerDuty, or elsewhere

Building agentic workflows means enabling real-time diagnosis without additional dashboards and without duplicating telemetry. That’s how you create impact without disruption.

From Reactive to Resilient

Manual triage doesn’t scale. It burns hours, stalls recovery, and pulls engineers away from higher-impact work. The more incidents you resolve manually, the more velocity you lose. With autonomous investigation in place, every resolved incident becomes time returned to the roadmap.

And when you reclaim that time across your SRE and IT operations team supporting critical systems—you’re not just optimizing workflows. You’re cutting directly into the hidden costs of downtime.

The Shift to Autonomous Operations Has Begun

The old model—alert floods, manual triage, constant firefighting—can’t keep up with the speed and scale of modern IT. What teams need now is precision: Faster answers. Real root cause. Fewer distractions.

This is the shift autonomous investigation enables: From chasing symptoms to solving problems at the source. From reacting under pressure to resolving with confidence. From operational drag to engineering momentum.

The future of IT operations is autonomous—and it’s already within reach.

Sources:

¹ Splunk, The Hidden Costs of Downtime

2 Grafana Labs Observability Survey 2024

3 Catchpoint The SRE Report 2024

Part 3 of 3: The AI SRE Reality Check

Most AI SRE vendors are selling you a single-agent fantasy. Their pitch is seductive: one AI assistant that handles all your operational needs, from incident response to capacity planning to security monitoring. It’s a compelling vision of simplicity—until you encounter the messy reality of enterprise operations.

The truth is that no single agent, no matter how sophisticated, can be an expert in every domain of modern IT operations. Real enterprise environments require specialized knowledge across databases, security, networking, application performance, cost optimization, compliance, and dozens of other areas. Each domain has its own tools, data sources, protocols, and expertise requirements.

The future of enterprise AI SRE isn’t about building better single agents—it’s about building better agent ecosystems. And at Neubird, we’re not just recognizing this trend; we’re leading it.

Beyond Single-Agent Thinking: Why Enterprise SRE Requires an Ecosystem Approach

Consider what happens during a typical enterprise incident. A performance degradation is detected in your application monitoring, but the root cause could span multiple domains:

• Infrastructure layer: Resource constraints, network issues, or cloud service problems
• Application layer: Code issues, configuration problems, or dependency failures
• Data layer: Database performance, query optimization, or connection pooling issues
• Security layer: Authentication failures, certificate problems, or policy violations
• Business layer: Traffic spikes, feature rollouts, or third-party service dependencies

This is where Hawkeye’s unique value becomes clear. As the first responder, Hawkeye rapidly scans across all these systems, correlating telemetry from infrastructure monitoring, application logs, database metrics, security alerts, and business intelligence platforms. Within minutes, Hawkeye can narrow down which domains are involved and identify the most likely root cause areas—something that would take human engineers hours of manual investigation across multiple tools.

But even Hawkeye, with its superior data integration and correlation capabilities, can’t be a deep specialist in every domain. Database optimization requires different expertise than network troubleshooting. Security incident response follows different protocols than performance tuning. And here’s the key insight: enterprises already have specialized teams—database administrators, security engineers, network specialists—each with their own domain expertise and tools.

This is why the most successful enterprise AI deployments are moving toward collaborative agent ecosystems. Hawkeye serves as the intelligent coordinator, rapidly identifying and triaging incidents, then collaborating with specialized agents that assist each domain team with their specific job functions. The DBA team gets an AI assistant with deep database knowledge and access to specialized database tools. The security team gets an agent trained on security protocols with access to threat intelligence platforms. Each domain agent becomes an expert teammate for its respective human specialists, while Hawkeye orchestrates the overall incident response and ensures nothing falls through the cracks.

The magic happens when these specialized agents can communicate and collaborate under Hawkeye’s coordination, creating a comprehensive operational response that combines rapid triage with deep domain expertise.

The Customization Imperative: MCP-Powered Enterprise Agent Specialization

Every enterprise has unique operational patterns, specialized tools, and domain-specific requirements. A generic AI SRE agent might work for simple environments, but enterprise-grade operations demand customization.

This is where Model Context Protocol (MCP) integration becomes transformative. Through MCP, enterprises can extend their AI SRE capabilities with:

Specialized CLI Tools: Direct integration with kubectl for Kubernetes operations, AWS CLI for cloud management, Azure CLI for hybrid deployments, Confluent CLI for streaming platforms, and any other command-line tools their teams use daily.

Domain-Specific Agents: Database administrators can deploy specialized DBA agents with privileged access to database internals, query performance analytics, and schema optimization tools. Security teams can integrate security agents with access to threat intelligence platforms, vulnerability scanners, and compliance monitoring systems.

Custom Workflow Integration: Through MCP, Hawkeye can integrate with proprietary internal tools, legacy systems, and specialized monitoring platforms that are unique to each enterprise.

But here’s where it gets really powerful: these aren’t isolated customizations. They’re building blocks for collaborative agent ecosystems.

Agent Collaboration in Action: The A2A Protocol Revolution

The next frontier in enterprise AI isn’t just about customizing individual agents—it’s about enabling them to work together intelligently. Google’s recently announced Agent2Agent (A2A) protocol represents a breakthrough in agent interoperability, and Neubird is at the forefront of implementing this collaborative approach.

The A2A protocol addresses a fundamental challenge: how do you enable AI agents from different systems, built by different vendors, to communicate and coordinate effectively? The answer lies in standardized agent-to-agent communication that allows specialized agents to:

• Discover each other’s capabilities dynamically
• Securely exchange information across organizational boundaries
• Coordinate actions without human intervention
• Delegate tasks to the most appropriate specialist agent

Consider a real-world scenario where this collaboration transforms incident response:

Incident Detection: Hawkeye detects a performance anomaly that suggests database involvement but requires deeper investigation.

Agent Coordination: Using A2A protocol, Hawkeye communicates with a specialized DBA agent, requesting detailed database performance analysis and query optimization recommendations.

Cross-Domain Analysis: The DBA agent identifies slow queries and connection pool issues, then coordinates with a security agent to verify that recent database access pattern changes aren’t security-related.

Integrated Response: All three agents—Hawkeye for overall incident coordination, the DBA agent for database-specific remediation, and the security agent for compliance verification—work together to provide a comprehensive resolution plan.

Outcome: What would have required multiple human specialists working across different systems is now handled by specialized AI agents working in coordination, reducing resolution time from hours to minutes.

Specialized Data Access: The Power of Agent Diversity

One of the most compelling aspects of collaborative agent ecosystems is how different agents can have access to different data sources and capabilities, creating a collective intelligence that exceeds what any single agent could achieve.

Source Code Access Agents: Some agents in the ecosystem have access to application source code repositories, enabling them to understand code-level issues and suggest specific fixes or optimizations. When Hawkeye identifies performance bottlenecks or error patterns that suggest application-level causes, it can collaborate with these agents to analyze the relevant code paths, identify problematic commits, and recommend specific code changes or rollback strategies.

Automation and Remediation Agents: These specialized agents can execute the remediation recommendations that Hawkeye provides, following enterprise change management workflows and approval processes. They have access to infrastructure automation tools, deployment pipelines, and configuration management systems, enabling them to implement fixes—from scaling resources and updating configurations to deploying patches—while maintaining compliance with organizational standards and safety protocols.

Business Context Agents: Specialized agents can access business intelligence platforms, understanding traffic patterns, user behavior, and business impact metrics. When Hawkeye detects anomalies, these agents help determine whether unusual patterns represent actual problems or expected business events (like marketing campaigns or seasonal traffic), enabling more accurate prioritization and impact assessment for incident response decisions.

Compliance and Security Agents: These agents have privileged access to security tools, audit logs, and compliance monitoring systems. When Hawkeye identifies potential security-related incidents or compliance violations, it collaborates with these agents to assess threat levels, validate security policies, and ensure that any remediation actions maintain regulatory compliance while addressing the operational issue.

When these diverse agents collaborate through protocols like A2A, they create a comprehensive operational intelligence that no single agent could match. Hawkeye coordinates the overall incident response and identifies the root cause, the source code agent analyzes the specific application changes needed, the automation agent determines the safest deployment approach while following change management protocols, and the business context agent evaluates the user impact and optimal timing for implementation—all working together under Hawkeye’s orchestration to provide holistic recommendations that consider technical feasibility, operational safety, and business requirements.

Knowledge-Powered Agent Collaboration: Making Institutional Wisdom Accessible

The power of collaborative agent ecosystems extends beyond just technical capabilities—it’s about making organizational knowledge accessible and actionable across the entire operational response. While individual agents bring specialized technical expertise, the ecosystem approach enables sophisticated knowledge integration that transforms how enterprises capture and leverage their operational wisdom.

Contextual Knowledge Coaching Across Agents: When SREs provide contextual guidance during investigations—explaining application behavior patterns, identifying service dependencies, or clarifying business impact priorities—this knowledge becomes available to the entire agent ecosystem. A coaching interaction with Hawkeye about database performance patterns during peak hours becomes available to the specialized DBA agent for future database-related incidents. Knowledge provided to a security agent about acceptable access patterns becomes context for future security investigations across the ecosystem.

Cross-Domain Learning From Incident History: Past incidents become more valuable in collaborative ecosystems because different agents can learn from incidents outside their primary domain. A database performance issue that was ultimately caused by application-level connection pool management becomes learning data for both the DBA agent and the application performance agent. This cross-pollination of incident knowledge creates collective intelligence that grows more sophisticated with each resolved incident.

Enterprise Knowledge Mining and Application: Collaborative agent ecosystems can leverage enterprise knowledge more effectively because different agents can specialize in different types of organizational knowledge. Source code agents can mine application repositories for deployment patterns and dependency information. Business context agents can analyze past incident reports to understand impact patterns and escalation procedures. Security agents can process compliance documentation to understand policy requirements. When these agents collaborate, they create a comprehensive understanding of enterprise context that informs every aspect of incident response.

The result is an agent ecosystem that doesn’t just respond to technical problems—it responds with full awareness of organizational priorities, historical patterns, and business context that makes the difference between generic troubleshooting and enterprise-aware operational excellence.

Real Enterprise Implementation: Beyond Theory

This isn’t theoretical future technology. We’re seeing early implementations of collaborative agent ecosystems in enterprise environments, with compelling results.

One of our enterprise customers—a leading AI insights company—has implemented a collaborative approach where Hawkeye coordinates with specialized agents for different aspects of their operations. When they experienced a complex issue spanning their entire AWS stack, instead of having human engineers manually coordinate between different specialist teams, their agent ecosystem handled the collaboration:

• Hawkeye identified the incident pattern and coordinated the investigation
• A specialized database agent analyzed RDS performance metrics and connection patterns
• An application performance agent examined ECS configurations and container metrics
• A cost optimization agent assessed the resource utilization implications

The result was a 90% reduction in mean time to resolution, with full root cause analysis delivered in under 5 minutes. But the real transformation was in knowledge retention—insights that previously lived only in senior engineers’ heads became institutional knowledge that any team member could access during future incidents. But more importantly, they achieved something that single-agent approaches struggle with: comprehensive analysis that considered infrastructure, application, database, and business perspectives simultaneously.

As their CEO noted: “By the time our team is paged, the root cause is already clear. We’ve reclaimed engineering time, cut down off-hours firefighting, and accelerated resolution by 10x. It’s a true force multiplier for how we operate and deliver.”

Hybrid Environment Success: Another enterprise customer—a large financial services organization—needed AI SRE capabilities across their hybrid infrastructure spanning on-premises data centers, private cloud, and AWS. Traditional cloud-only AI solutions couldn’t access their on-premises telemetry or operate within their strict data governance requirements.

Their Hawkeye deployment operates entirely within their controlled environment, coordinating with specialized agents across their heterogeneous infrastructure. The system correlates data from on-premises Splunk deployments, private cloud Kubernetes clusters, and AWS services—all while maintaining compliance with financial services regulations. The result: comprehensive operational intelligence across their entire hybrid environment without compromising security or governance requirements.

As their Infrastructure Director noted: “Most AI solutions assume you’ve moved everything to the cloud. Hawkeye works where our infrastructure actually is—and that’s made all the difference in our ability to leverage AI for operations.”

API-First Enterprise Integration: The System Integrator Revolution

While most AI SRE vendors focus on direct enterprise sales, we’re seeing a more sophisticated trend emerge: system integrators building comprehensive operational solutions that embed multiple specialized agents into unified service offerings.

Major system integrators are leveraging our API-first architecture to build managed services that combine:

• Hawkeye for incident response and root cause analysis
• Specialized agents for domain-specific operations
• Custom agents for client-specific requirements
• Integration with existing ITSM and operational workflows

This approach transforms how enterprises think about operational capabilities. Instead of building internal specialist teams or hoping their existing staff can keep up with increasing complexity, they get access to AI-powered expertise that’s embedded in their existing operational processes and enhanced through agent collaboration.

The system integrator model also accelerates the adoption of agent ecosystems because it removes the complexity of implementing and managing multiple specialized agents. Enterprises get the benefit of collaborative AI without the overhead of building and maintaining the ecosystem themselves.

The Feedback-Driven Roadmap: Building for Real Collaboration Needs

Our approach to agent ecosystem development is driven by real customer deployments, not theoretical possibilities. This production-driven evolution has led to sophisticated capabilities that you won’t find in single-agent solutions:

Advanced Incident Management Workflows: Alert filtering, deduplication, and incident-centric user experiences that coordinate between multiple agents while presenting unified interfaces to human operators.

Sophisticated Instruction Capabilities: The ability to fine-tune not just individual agent behavior, but agent collaboration patterns based on specific types of problems and organizational preferences.

Enhanced Remediation Coordination: Recommendations that consider inputs from multiple specialized agents, ensuring that solutions are comprehensive and don’t create new problems in other domains.

Enhanced RAG-Based Knowledge Integration: Expanding beyond basic knowledge base integration to sophisticated RAG solutions specifically designed for SRE teams. This includes automatic mining of enterprise documentation, integration with specialized SRE knowledge platforms, and advanced semantic search across incident history, runbooks, and organizational knowledge to surface relevant context automatically during investigations.

MCP Server Ecosystem Expansion: Growing support for specialized tools and platforms through MCP integration, enabling enterprises to extend their agent ecosystems with any operational tool they need.

The Collaborative Future: Why Agent Diversity Beats Agent Monopoly

As enterprise environments continue to grow in complexity, the single-agent approach becomes increasingly unsustainable. No matter how sophisticated an individual AI agent becomes, it can’t match the collective intelligence of specialized agents working in coordination.

The enterprises that succeed in the age of AI operations will be those that embrace agent diversity and collaboration. They’ll build ecosystems where:

• Hawkeye serves as the coordination hub for incident response and root cause analysis
• Specialized agents provide deep domain expertise in databases, security, networking, and other critical areas
• Custom agents handle organization-specific requirements through MCP integration
• Agent-to-agent communication enables sophisticated collaboration through protocols like A2A
• Human operators interact with a unified interface that abstracts the complexity of the underlying agent ecosystem

Implementation Strategy: Building Your Agent Ecosystem

For enterprises considering this collaborative approach, the path forward involves several key steps:

Assess Deployment Requirements Early: Evaluate your infrastructure distribution, governance requirements, and data residency constraints to determine the optimal deployment model. Plan for agent ecosystem deployment that accommodates your actual infrastructure reality rather than forcing infrastructure changes to accommodate AI limitations.

Start with Core Coordination: Begin with Hawkeye as your incident response and RCA coordination agent. This provides immediate value while establishing the foundation for agent collaboration.

Identify Specialization Opportunities: Assess your operational domains to identify areas where specialized agents could provide significant value—typically databases, security, cost optimization, and application performance.

Leverage MCP for Custom Integration: Use MCP integration to connect with your existing tools and build custom agent capabilities for organization-specific requirements.

Plan for A2A Integration: Prepare for agent-to-agent collaboration by designing your operational workflows with inter-agent communication in mind.

Measure Collaborative Impact: Track not just individual agent performance, but the effectiveness of agent collaboration in reducing overall incident resolution time and improving operational outcomes.

Establish Knowledge Capture Workflows: Design processes for SREs to provide contextual coaching during investigations and ensure that insights from resolved incidents are captured and made available for future similar scenarios. Plan for integration with existing documentation systems and runbooks.

Agentic Workflows for Today’s Reality

The excitement around AI agents and collaborative ecosystems is justified, but success requires building for where enterprises actually are, not just where they’re heading. Many organizations are still managing mission-critical workloads in hybrid environments with complex governance requirements. Agent ecosystems that only work in idealized cloud-native environments may be impressive in demonstrations, but they fail to address real-world operational challenges.

At Neubird, we’ve learned that the future of agentic workflows starts with solving today’s problems in today’s environments. That means building agents that can operate across on-premises data centers, private clouds, and multiple public cloud providers while respecting enterprise governance, security, and compliance requirements. It means creating collaborative agent ecosystems that work with existing tool chains and infrastructure rather than requiring wholesale technology stack changes.

The Ecosystem Advantage

The future of enterprise AI SRE isn’t about finding the perfect single agent—it’s about building the perfect agent ecosystem for your specific operational needs. While competitors are still trying to build one agent that does everything adequately, leading enterprises are building multiple agents that collaborate to do everything excellently.

At Neubird, we’re not just building Hawkeye as an AI SRE agent. We’re building the foundation for enterprise agent ecosystems that can adapt, specialize, and collaborate to meet the complex operational challenges of modern enterprise environments.

The question isn’t whether agent collaboration will become the standard for enterprise operations—it’s whether your organization will be ready to take advantage of this collaborative future. The enterprises that start building their agent ecosystems now will have a significant operational advantage over those that wait for single-agent solutions to somehow solve all their problems.

Because in the end, the most sophisticated agent ecosystem will always outperform the most sophisticated individual agent. And that’s not just a technical reality—it’s the future of enterprise operations.

This concludes our three-part series on the AI SRE reality check. From production-proven solutions to superior data integration to collaborative agent ecosystems, the enterprise landscape is rapidly evolving beyond the limitations of single-agent approaches.

Ready to build your collaborative agent ecosystem? Contact us to learn how Hawkeye can serve as the coordination hub for your enterprise agent ecosystem, integrating with specialized agents and custom tools through MCP and A2A protocols.

Part 2 of 3: The AI SRE Reality Check

Here’s an uncomfortable truth about the AI SRE market: everyone has access to essentially the same foundational models. Whether you’re using GPT-4, Claude, or Llama, the raw reasoning capabilities are becoming commoditized. The vendors pitching you their “revolutionary AI SRE solution” are often using the same LLMs you could access directly.

So if the models are commoditized, what creates real differentiation? The answer lies not in the intelligence of the AI, but in the intelligence of how you feed it information. The winners in AI SRE won’t be determined by who has the best model—they’ll be determined by who can provide the best context.

The Model Myth: Why LLM Access Doesn’t Equal SRE Success

Walk into any AI SRE demo and you’ll hear impressive claims about model capabilities. “Our AI can analyze logs at superhuman speed!” “We use the latest GPT model for unprecedented accuracy!” “Our reasoning engine processes thousands of metrics simultaneously!”

All of this might be true, but it misses the fundamental challenge of enterprise SRE: the problem isn’t processing individual data points—it’s understanding the relationships between them across disparate systems, time windows, and data formats.

Consider a typical incident in a modern enterprise environment:

• A Kubernetes pod starts crashlooping
• API response times spike in your application monitoring
• Database connection pools show increased latency in your observability platform
• CloudWatch metrics indicate resource constraints
• Your log aggregation system captures error messages across multiple services

An AI that can analyze any one of these signals brilliantly is still useless if it can’t correlate them into a coherent narrative. And here’s where most AI SRE solutions fail: they treat each data source as an isolated island rather than part of an interconnected ecosystem.

The Context Problem: Why Most AI SRE Tools Fail at Complex Investigations

The dirty secret of many AI SRE solutions is that they’re essentially sophisticated log analyzers with chatbot interfaces. They can parse individual data streams effectively, but they struggle with the kind of cross-system correlation that defines real-world incident response.

Let’s look at what happened with one of our customers—a leading AI insights company that was experiencing mysterious performance degradation. Their previous monitoring approach involved engineers manually jumping between:

• CloudWatch for AWS infrastructure metrics
• ECS configurations for container-level insights
• Application logs scattered across multiple services
• Performance monitoring dashboards showing symptoms but not causes

The engineering team was spending hours correlating these disparate data sources, trying to build a coherent picture of what was happening. By the time they identified root causes, incidents had often escalated beyond their initial scope.

When they deployed Hawkeye, the transformation was immediate. Instead of treating each telemetry source as a separate problem, Hawkeye established a unified view across their entire AWS environment. The result? A 90% reduction in mean time to resolution, with full root cause analysis delivered in under 5 minutes.

As their CEO explained: “By the time our team is paged, the root cause is already clear. We’ve reclaimed engineering time, cut down off-hours firefighting, and accelerated resolution by 10x.”

The Hybrid Advantage: Data Virtualization + MCP Integration

Most AI SRE vendors are taking a monolithic approach to data integration—trying to solve every integration challenge with a single method. At Neubird, we recognized early that different types of data and interactions require different approaches. That’s why we built a hybrid architecture that combines the best of both worlds.

Data Virtualization for Correlation Power: For time-series data, traces, configurations, and logs—the foundational telemetry that requires correlation across systems—we use sophisticated data virtualization. This creates a unified schema across all your observability tools, enabling Hawkeye to perform cross-system joins and correlations that would be impossible with traditional point-to-point integrations.

Think of it this way: instead of having Hawkeye query your Prometheus instance, then your Splunk index, and then your CloudWatch metrics as separate operations, our data virtualization layer presents all of this information as a unified, queryable dataset. This enables complex correlations like: “Show me all instances where Kubernetes resource constraints occurred within 5 minutes of database connection pool exhaustion, correlated with API gateway error rates exceeding baseline.”

MCP Integration for Real-Time Tool Access: For real-time command-line operations and specialized sub-systems, we embrace Model Context Protocol (MCP) integration. This gives Hawkeye direct access to tools like kubectl, AWS CLI, Azure CLI, Confluent CLI, and other operational interfaces that SRE teams use daily.

But we don’t stop at individual tool access. We’re pioneering the next frontier of enterprise AI operations: collaborative agent ecosystems. Through emerging protocols like Google’s Agent2Agent (A2A), we’re enabling Hawkeye to coordinate with specialized agents—DBA agents with deep database access, security agents with threat intelligence platforms, cost optimization agents with business context. This collaborative approach creates collective intelligence that no single agent could achieve alone, transforming enterprise operations from isolated AI assistance to orchestrated agent ecosystems working in concert. Make sure to read Part 3 of this series for more details on our plans for multi-agent workflows.

Why This Hybrid Approach Wins: The combination creates capabilities that neither approach could achieve alone. Data virtualization enables the complex correlations that identify problems, while MCP integration enables the real-time investigation and validation that confirms root causes.

A custom technology solutions company experienced this power firsthand when they faced a complex issue spanning their entire AWS stack—RDS, SQS, ElastiCache, and Lambda services. Instead of their engineers spending days jumping between different monitoring interfaces and CLI tools, Hawkeye correlated the telemetry data to identify the root cause, then verified it through direct system queries via MCP interfaces. Total resolution time: minutes instead of days.

The Knowledge Integration Layer: Turning SRE Expertise Into AI Context

While data virtualization and MCP integration solve the technical data access challenge, enterprise AI SRE success requires solving an equally important problem: knowledge capture and application. The most valuable insights for incident resolution often exist in the minds of experienced SREs—understanding application behavior patterns, knowing which metrics matter most for specific services, recognizing seasonal traffic variations, and remembering lessons learned from past similar incidents.

Traditional monitoring solutions treat this knowledge as external to the system. SREs have to remember these insights and manually apply them during each investigation. Hawkeye takes a different approach: it treats SRE knowledge as a first-class data source that can be captured, refined, and applied automatically.

Real-Time Knowledge Coaching: During investigations, SREs can provide contextual guidance directly within Hawkeye’s interface. Comments like “this service typically shows high CPU during batch processing hours” or “ignore Redis connection spikes during deployment windows” become part of Hawkeye’s understanding for future incidents involving these systems. This contextual coaching happens naturally as part of the investigation workflow, without requiring separate documentation processes.

Institutional Memory From Past Incidents: Every resolved incident becomes training data for future investigations. Hawkeye automatically identifies patterns between current symptoms and historical resolutions, surfacing relevant past incidents and their solutions. But more importantly, it learns to recognize when current incidents share characteristics with past ones, enabling faster root cause identification based on organizational experience rather than just generic algorithmic analysis.

Application Context Integration: Most enterprises have rich contextual knowledge about their applications—deployment patterns, dependency relationships, business impact hierarchies, and operational characteristics—but this information is scattered across wikis, runbooks, and SRE team knowledge. Hawkeye provides structured ways to capture this context and automatically applies it during investigations, ensuring that business-critical services get appropriate prioritization and that investigation approaches match application-specific characteristics.

Beyond Point Solutions: The System Integrator Revolution

While most AI SRE vendors are focused on selling directly to end customers, we’re seeing a more sophisticated trend emerge: system integrators embedding AI SRE capabilities directly into their digital service desk offerings.

This isn’t just about API access—though our API-first architecture makes these integrations seamless. It’s about recognizing that enterprises don’t want to replace their entire operational ecosystem. They want to enhance it.

Major system integrators are building Hawkeye into their managed services offerings because it allows them to deliver expert-level SRE capabilities without the overhead of hiring and training specialized talent. For their enterprise clients, this means getting AI-powered incident response that’s integrated into their existing workflows, ticketing systems, and escalation procedures.

This approach is transforming how enterprises think about operational capabilities. Instead of building internal SRE teams or hoping their existing staff can keep up with increasing complexity, they’re getting access to AI-powered expertise that’s embedded in their existing operational processes.

Universal Compatibility: Meeting Customers Where They Are

Here’s something you won’t hear from most AI SRE vendors: they’ll only tell you about the observability tools they integrate with easily. At Neubird, we’ve taken a different approach—we support more observability solutions than any other AI SRE platform because we’ve learned that enterprises use whatever combination of tools serves their needs best.

Our customers don’t live in single-vendor worlds. They use:

• Splunk for log analysis alongside Prometheus for metrics
• Grafana for visualization while DataDog handles APM
• CloudWatch for AWS services mixed with Azure Monitor for hybrid deployments
• New Relic for applications integrated with Elastic for search and analytics

Traditional integration approaches break down in these mixed environments. Each tool requires custom connectors, different authentication methods, varied data formats, and unique query languages. The result is usually a fragmented view where AI can analyze individual tools effectively but struggles to correlate insights across the entire stack.

Our data virtualization approach solves this by creating a unified interface across all these tools. From Hawkeye’s perspective, it doesn’t matter whether data is coming from Splunk or Elastic, Prometheus or DataDog—it’s all part of a coherent, queryable dataset that enables comprehensive analysis.

Hybrid and Multi-Cloud Reality: Agents That Work Where Problems Actually Live

While the industry talks about the future of cloud-native operations, enterprises are dealing with the reality of hybrid environments today. Many organizations are managing on-premises data centers with strict governance requirements, disaggregated Kubernetes clusters running across multiple environments, data silos that can’t easily be exposed to external services, and security policies that prevent broad telemetry sharing with third-party platforms.

This is where Neubird’s approach to agentic workflows differentiates from cloud-only solutions. Hawkeye isn’t confined to a single cloud provider or tied to a narrow slice of infrastructure. It’s designed to operate across the complex, heterogeneous environments where real-world problems actually live.

Deployment Where You Need It: Our customers have deployed Hawkeye inside private data centers, within highly restricted VPCs, and across multiple cloud providers simultaneously. This deployment flexibility enables Hawkeye to access telemetry and perform investigations regardless of where your infrastructure lives, without requiring you to expose sensitive data to external platforms or standardize on a single cloud provider’s ecosystem.

Governance-Aware Operations: Real enterprise environments have compliance requirements, data residency constraints, and security policies that affect how AI agents can operate. Hawkeye’s architecture accommodates these requirements by design, enabling intelligent operations while respecting organizational boundaries and governance frameworks.

This real-world deployment experience has taught us that the future of agentic workflows isn’t just about building smarter agents—it’s about building agents that can actually be deployed and trusted in the complex environments where enterprises operate today.

Real Impact: When Superior Integration Meets Real Problems

The proof of superior integration architecture isn’t in the technical specifications—it’s in the results customers achieve when facing their most complex operational challenges.

Consider what happened with a large infrastructure and software provider using Splunk on AWS. Their engineers were spending hours manually analyzing logs and incident data, leading to prolonged troubleshooting cycles. The complexity of their IT environment required specialized knowledge across multiple domains, making root cause analysis increasingly difficult.

After implementing Hawkeye, issues that previously required hours of log analysis were diagnosed and resolved in minutes. But the key wasn’t just faster log analysis—it was Hawkeye’s ability to automatically correlate data across their entire Splunk deployment, identifying patterns and relationships that would have taken human analysts much longer to discover.

As they noted in their lessons learned: “Cross-system correlation accelerates diagnosis. Our complex environment revealed that the most challenging incidents often involve interactions between multiple systems. Hawkeye’s ability to correlate data across our entire infrastructure was crucial in reducing time to root cause analysis.”

The Correlation Capability Gap

This brings us to the fundamental difference between AI SRE solutions that work in demos and those that excel in production: correlation capability. Most solutions can tell you what’s happening in individual systems. Few can tell you why it’s happening across multiple systems.

Real incidents rarely respect the boundaries of monitoring tools. A performance problem might start with a configuration change captured in your ITSM system, manifest as resource constraints in your infrastructure monitoring, appear as error rates in your APM solution, and impact customer experience metrics in your business intelligence platform.

Solutions built on traditional integration approaches—whether that’s individual APIs, webhook notifications, or even lists of MCP servers—struggle with this correlation challenge. They can access each system individually, but they can’t easily perform the complex joins and temporal correlations that turn individual signals into actionable insights.

Our data virtualization approach treats all telemetry sources as part of a unified dataset, making these correlations not just possible, but natural. Instead of asking Hawkeye to query five different systems and manually correlate the results, we can ask it to find patterns across all systems simultaneously.

Why This Matters for Your Enterprise

If you’re evaluating AI SRE solutions, don’t be distracted by model capabilities that everyone has access to. Focus on integration architecture that determines what context the AI actually receives.

Ask potential vendors:

• How do you handle correlation across different observability tools?
• Can you perform temporal joins across disparate data sources?
• How do you manage authentication and authorization across multiple systems?
• What happens when we need to correlate real-time CLI output with historical telemetry data?
• How do you handle schema drift and API changes across our tool ecosystem?

The vendors that give you detailed, architecture-focused answers are the ones building for enterprise reality. The ones that pivot back to model capabilities are the ones that haven’t solved the hard problems yet.

The Future of AI SRE Integration

As we look ahead, the integration challenge is only going to become more complex. Enterprises are adopting more observability tools, not fewer. Multi-cloud deployments are becoming standard. The number of specialized monitoring solutions continues to grow.

The AI SRE solutions that succeed will be those that can adapt to this increasing complexity without requiring enterprises to standardize their entire observability stack around a single vendor’s ecosystem.

At Neubird, we’re not just building for today’s integration challenges—we’re building for tomorrow’s collaborative agent ecosystems. Our roadmap includes expanded MCP server support for emerging operational tools, enhanced data virtualization capabilities for next-generation observability platforms, and pioneering implementation of Google’s Agent2Agent (A2A) protocol for seamless inter-agent communication. We’re enabling Hawkeye to serve as the coordination hub for enterprise agent ecosystems, where specialized agents collaborate on complex operational challenges—each bringing their own expertise and data access to create collective intelligence that transforms how enterprises handle incident response, root cause analysis, and operational optimization.

Because at the end of the day, the best AI SRE solution isn’t the one with the smartest model—it’s the one that can make the smartest use of all the data and tools you’re already invested in.

In Part 3 of this series, we’ll explore the next frontier of AI SRE: moving beyond single-agent solutions to collaborative agent ecosystems. We’ll examine how enterprises are leveraging Google’s new Agent2Agent (A2A) protocol to enable specialized agents to communicate and coordinate, creating collective intelligence where DBA agents, security agents, and operational agents work together with Hawkeye as the coordination hub for comprehensive operational coverage that no single agent could achieve alone.

Ready to experience the power of superior data integration? Contact us to see how Hawkeye’s hybrid architecture can unlock insights hidden in your existing observability stack.

 

When Broadcom acquired VMware, it created more than industry headlines—it created an inflection point. For decades, VMware was the operational bedrock of enterprise IT. It wasn’t just about virtualization; it was the control plane for managing compute, bolstered by a rich ecosystem of observability, diagnostics, and IT automation tools.

Today, that control plane is shifting. Enterprises seeking a more cloud-native approach are rapidly exploring KubeVirt—an open-source extension of Kubernetes that enables VMs to run side-by-side with containers under a unified control plane. It’s elegant in theory, powerful in practice, but incomplete in one critical dimension: operability.

The Hidden Ingredient Behind VMware’s Success? Observability

VMware’s dominance was never just about hypervisors. Its real moat was its supporting ecosystem:

• Telemetry tools that gave IT teams insight into what was happening

• Remediation workflows that turned signals into actions

• Compliance and diagnostics built into the fabric of VM management

That ecosystem meant enterprises could operate at scale and sleep at night.

But with KubeVirt, many of these layers are missing or fragmented. The Kubernetes-native world is rich with telemetry—from Prometheus to Datadog, OpenTelemetry, Splunk, New Relic, and more—but there’s no single operational glue that brings it together for virtual machine diagnostics, especially when VMs behave like legacy workloads in a modern cloud-native world.

The Problem Isn’t the Data—It’s the Noise

Modern telemetry is abundant, but context windows for reasoning (especially for GenAI agents) are narrow. Dumping metrics, logs, and traces into a dashboard or even a model doesn’t help if the signal-to-noise ratio is poor.

To make KubeVirt viable for real enterprise operations, we need systems that don’t just collect data—we need systems that can think. Systems that can surgically extract the right data across time, space, and observability surface to understand and resolve real incidents.

Enter Hawkeye: Agentic SRE for the KubeVirt Era

At Neubird, we’ve built Hawkeye—a production-grade, GenAI-powered agentic SRE system designed for Kubernetes, OpenShift, and yes, KubeVirt. Hawkeye is not just an observability overlay; it’s a reasoning engine that actively investigates and resolves incidents through a chain of thought.

Here’s how it works:

✅ Use Case 1: VM Crash or Freeze

• Hawkeye receives an alert from Prometheus that a KubeVirt-managed VM is unresponsive.

• It begins an iterative investigation, checking resource pressure via kubectl top node, then digs into host-level metrics (e.g., CPU throttling, memory swap) via Datadog or OpenTelemetry.

• It queries logs in Splunk for correlated error events and examines Kubernetes events for pod eviction or node taints.

• The agent surfaces root cause—the VM is scheduled on a node under memory pressure due to a runaway container.

• It recommends (and can optionally trigger) a live migration of the VM to a healthier node using virtctl.

✅ Use Case 2: Network Connectivity Failure

• A service running inside a VM suddenly becomes unreachable.

• Hawkeye traces the service path—from KubeVirt network bridge to CNI plugin logs—and cross-checks against recent configuration changes using AWS Config or GitOps history.

• It detects a misconfigured network policy applied via a recent Helm deployment and flags the exact commit.

✅ Use Case 3: High Disk I/O Latency

• Alert from Datadog or Prometheus shows elevated I/O latency on a VM.

• Hawkeye pulls PVC metrics and compares read/write patterns over the past 2 hours.

• It inspects the host disk layer for other competing workloads and maps it back to node-specific diagnostics.

• Through iterative narrowing, it identifies noisy neighbors causing contention—and suggests node affinity rules or PVC migration.

How Hawkeye Makes It Possible

Hawkeye integrates deep telemetry access and agentic reasoning with the following pillars:

• 🔍 Surgical Data Extraction: Filters telemetry to retrieve only the relevant data across time and context, minimizing model overload.

• 🔁 Iterative Chain-of-Thought: Models reason step by step, refining hypotheses like an SRE would in a war room.

• 📡 Multi-Source Observability: Hooks into Prometheus, Splunk, Datadog, AWS CloudWatch, OpenTelemetry, and direct kubectl/virtctl access to unify structured and unstructured signals.

• 🛠️ Agentic Actions: Not just detection—Hawkeye suggests or performs remediation actions (restart, migrate, patch, etc.) with audit tracking.

A New Era of Compute Needs a New Kind of SRE

If VMware was the old guard of virtualization—with an ecosystem built for the 2000s—KubeVirt represents the next generation: cloud-native, open, and extensible. But to make it viable in production, we need a modern operational brain to sit on top of the stack.

With Hawkeye, we’re making KubeVirt not just possible, but operable—by turning GenAI and telemetry into a surgical, intelligent, and agentic SRE that enterprises can trust.

Because deploying GenAI in infrastructure isn’t about who can do it first—it’s about who can do it responsibly, safely, and scalably.

Ready to see Hawkeye in action?
Drop us a note at neubird.ai and let’s talk agentic SRE for your KubeVirt stack.

Part 1 of 3: The AI SRE Reality Check

AI SRE started as a bold idea—now it’s becoming a category. Neubird is proud of pioneering this shift, and today, more teams are adopting the term and the transformation it represents.

The influx of new announcements from vendors big and small shows the need is real: operations teams are under pressure, and the old playbook isn’t cutting it. We’re glad to see others validating what we’ve believed from the start—that AI agents have the potential to reshape incident management as the tech stack becomes more and more complex.

But here’s what these announcements don’t tell you: most of these solutions are still in beta or preview, untested in the complex reality of enterprise production environments. And when the rubber meets the road, that distinction makes all the difference.

The Beta Bubble: When Demos Meet Reality

There’s a massive gap between a controlled demo environment and a production enterprise infrastructure. In demos, you see clean data flows, predictable failure patterns, and scenarios designed to showcase the AI’s capabilities. In production, you encounter the chaos of real systems: conflicting data sources, legacy integrations, security constraints, compliance requirements, and the kind of complex, cascading failures that don’t fit neatly into training datasets.

This is why so many SRE Agent pilots that look promising in evaluation phases struggle when deployed at scale. The controlled conditions that made the demo shine simply don’t exist in the real world.

Consider what happens when an AI SRE solution encounters:

• Hybrid and Multi-cloud environments with inconsistent telemetry formats across AWS, Azure, and GCP
• Legacy systems that don’t follow modern observability patterns
• Security policies that restrict data access and require read-only permissions with precise scoping
• Compliance requirements that demand audit trails and data residency controls
• Integration complexity across dozens of monitoring tools, each with their own APIs and data models

Beta solutions, by definition, haven’t faced these challenges at scale. They’re still figuring out the basics while enterprise teams need solutions that work on day one.

Enterprise Reality Check: Why Production Demands Proven Solutions

When Neubird’s customers deploy Hawkeye, they’re not running pilot projects—they’re solving critical business problems with real consequences. A large infrastructure and software provider needed to slash their root cause analysis time without compromising security. A custom technology solutions company required 24/7 expert-level monitoring to maintain their SLAs while scaling their customer base. An AI insights company needed to eliminate alert fatigue and stop waking engineers for repetitive issues.

These weren’t evaluation scenarios—they were production deployments with immediate expectations for results.

The infrastructure provider saw immediate impact: issues that previously required hours of log analysis in Splunk were diagnosed and resolved in minutes. Hawkeye automatically correlated data across their entire AWS infrastructure, providing 24/7 expert-level analysis that enabled rapid response regardless of time of day.

The technology solutions company achieved a 92% reduction in Mean Time to Resolution (MTTR). Critical issues that once took days to resolve were now resolved in minutes, with Hawkeye automatically correlating data across their entire AWS stack—spanning Amazon RDS, SQS, ElastiCache, Lambda, and beyond. As their CTO noted: “The complexity of modern cloud-native environments demands a new approach to IT operations, and Hawkeye delivers exactly that. Having an AI SRE working alongside our team 24/7 has transformed how we operate.”

The AI insights company experienced a 90% faster incident resolution rate, with full root cause analysis delivered in under 5 minutes. More importantly, their engineers reclaimed their nights and weekends, as the CEO explained: “NeuBird’s Hawkeye flips the script on incident response. By the time our team is paged, the root cause is already clear—and it gets smarter with every incident. Our SREs can coach Hawkeye in real-time during investigations, and that tribal knowledge becomes institutional knowledge that helps with future incidents. We’ve reclaimed engineering time, cut down off-hours firefighting, and accelerated resolution by 10x.”

The Production Difference: What Enterprise-Grade Actually Means

While competitors are still working through beta feedback, Neubird has been refining Hawkeye based on actual enterprise production deployments. This isn’t theoretical improvement—it’s evolution driven by real customer needs in real environments.

Security and Compliance Foundation: Neubird recently achieved SOC2 Type II certification, demonstrating our commitment to the security and compliance standards that enterprises require. This isn’t just a checkbox—it reflects the mature processes and controls that enterprise customers need to trust an AI system with access to their critical infrastructure data.

Deployment Flexibility for Enterprise Reality: Different enterprises have different security postures, infrastructure constraints, and operational requirements. While many AI SRE solutions assume fully cloud-native environments, enterprise reality is far more complex. Organizations are running mission-critical workloads across hybrid environments—spanning on-premises data centers, private clouds, and multiple public cloud providers, often with strict governance requirements and data sovereignty concerns.

That’s why we offer three distinct deployment models:

• Standard SaaS Model: The fastest path to value, with dedicated logical resources and enterprise-grade security
• Bring Your Own LLM and Storage: For organizations that need their data processing to never leave their control
• Private Account Deployment: Maximum customer control with deployment in your own AWS account, Azure Subscription, or even on-premises infrastructure within private data centers and restricted VPCs

This deployment flexibility isn’t theoretical—it’s based on real enterprise deployments where we’ve learned that ease of deployment matters just as much as intelligence, privacy and control are non-negotiable, and agents must adapt to heterogeneous technology stacks rather than requiring infrastructure standardization.

Battle-Tested Integration: Our customers don’t have the luxury of greenfield environments. They need solutions that work with their existing observability stacks—whether that’s Splunk, Grafana, Prometheus, Elastic, Dynatrace, CloudWatch, or any combination thereof, deployed across cloud and on-premise environments.. Hawkeye integrates with more observability tools than any other AI SRE solution because we’ve had to solve real integration challenges, not just demonstrate capability in controlled environments.

The Feedback-Driven Evolution Advantage

Here’s what many don’t realize about the AI SRE space: the technology is evolving rapidly, but only solutions with real customer feedback can evolve in the right direction. Beta solutions are making educated guesses about what enterprises need. Production solutions are responding to what enterprises actually use.

This customer-driven development has led to sophisticated capabilities that you won’t find in beta solutions:

Universal Telemetry Integration: Hawkeye supports more observability sources than any other AI SRE platform, seamlessly connecting to tools across all major cloud providers (AWS, Azure, GCP) and on-premise environments. Whether your telemetry lives in Splunk, Grafana, Prometheus, Elastic, Dynatrace, CloudWatch, or dozens of other platforms, Hawkeye provides unified access without requiring you to standardize on a single vendor’s ecosystem. (Read Part 2 for more on our approach to connecting LLMs to the right context)

Comprehensive Context Access: Real incident resolution requires more than just log analysis. Hawkeye provides integrated access to configuration data, logs, metrics, traces, alerts, and interactive command-line tools—creating a complete operational picture that enables true root cause analysis. This multi-dimensional context is what separates effective AI SRE from sophisticated log parsers.

Production-Ready Operational Features: Advanced incident management workflows with alert filtering, deduplication, and incident-centric user experiences address the alert fatigue that real customers face, not clean demo scenarios. Sophisticated instruction capabilities allow users to fine-tune investigations based on problem types and organizational patterns, while customizable remediation recommendations provide actions that enterprises can actually implement in their specific environments.

Knowledge-Driven Investigation Enhancement: Unlike solutions that treat AI as a black box, Hawkeye learns from SRE expertise in real-time. SRE teams can coach Hawkeye during investigations, providing context about application behavior, known failure patterns, and organizational priorities that aren’t documented anywhere. This contextual coaching becomes part of Hawkeye’s understanding for future similar incidents. Additionally, Hawkeye automatically learns from past incident patterns, building institutional knowledge that persists even when team members change roles or leave the organization.

Enterprise Integration and Collaboration: API-first architecture enables deep embedding into existing workflows and ITSM platforms, while support for Model Context Protocol (MCP) allows custom tool integration and specialized agent development. Looking ahead, our implementation of Google’s Agent2Agent (A2A) protocol will enable collaborative agent ecosystems where specialized agents work together under Hawkeye’s coordination. (Read Part 3 for more on our collaborative agent approach)

These aren’t features you build in a lab. They’re capabilities you develop by solving real problems for real customers.

The Stakes Are Too High for Beta Solutions

In the world of enterprise IT operations, downtime isn’t just inconvenient—it’s expensive. Every minute of service disruption can cost thousands of dollars in lost revenue, not to mention the impact on customer trust and SLA compliance. When the stakes are this high, enterprises can’t afford to be beta testers.

They need solutions that work immediately, integrate seamlessly, and evolve based on real-world feedback. They need the confidence that comes from working with a vendor who has already solved the problems they’re facing, not one that’s still figuring out the basics.

Why Enterprise Teams Choose Proven Over Promising

The choice facing enterprise teams isn’t just between different AI models or feature sets—it’s between solutions that have been proven in production and those that are still proving themselves. While competitors are launching beta programs and gathering initial feedback, Neubird customers are already seeing transformative results.

A recent industry survey found that 81% of board directors consider business disruptions due to skills and talent shortages a top priority. The same survey revealed that 47% see the need to move to a blended human-machine workforce model as critical. This isn’t a future trend—it’s a present reality that requires solutions available today, not promises of what might be available tomorrow.

When you’re choosing an AI SRE solution, ask yourself: Do you want to be part of someone else’s learning process, or do you want to benefit from lessons already learned? Do you need a solution that might work in your environment, or one that’s already proven it can?

The difference between beta and production-ready isn’t just about maturity—it’s about whether you’re buying a promise or purchasing proven results.

In Part 2 of this series, we’ll explore why the real differentiation in AI SRE isn’t about having better models—it’s about having better data integration and orchestration capabilities. We’ll dive into why Neubird’s hybrid approach of data virtualization plus MCP integration creates correlation capabilities that single-approach solutions simply can’t match.

Ready to see the difference a production-proven AI SRE solution can make? Schedule a demo to learn how Hawkeye can transform your incident response—without the risks of being an early adopter.

 

Why NeuBird  is Leading the Way in Hybrid and Multi-Cloud Enterprise Agents

The excitement around AI agents is real—and deserved. But as the enterprise world races to adopt agentic workflows, it’s worth pausing to ask: Are we building for tomorrow’s ideal or today’s reality?

At NeuBird, we believe the future of agentic workflows starts with solving the challenges enterprises face right now. And the reality is: not every organization is fully cloud-native. Many are running mission-critical workloads in hybrid environments—spanning on-prem data centers, private clouds, and multiple public cloud providers.

This is where NeuBird is leading the way. While many are just starting to explore the potential of AI agents, we’ve already deployed them in complex enterprise environments. Our SRE agent, Hawkeye, isn’t confined to a single cloud or tied to a narrow slice of infrastructure. It’s designed to operate across hybrid environments—because that’s where real-world problems still live.

Cloud-Only Agents Aren’t Enough

It’s tempting to assume that everything is moving to the cloud—and to build agents that only work in that context. But for most enterprises, that’s not yet the case. Many organizations are still managing:

• On-prem data centers with strict governance and compliance requirements 
• Disaggregated Kubernetes clusters running across environments 
• Data silos that can’t be easily exposed to external services 
• Security policies that prevent broad telemetry sharing with third-party platforms 

Agents that only function in cloud-native ecosystems may work in theory—but NeuBird builds for where enterprises actually are.

Real Deployment Experience, Real Enterprise Concerns

We’ve worked closely with large enterprises to deploy Hawkeye inside their environments—sometimes inside private data centers, other times in highly restricted VPCs, and often across multiple cloud providers. Along the way, we’ve learned:

• Ease of deployment matters just as much as intelligence. An agent that’s hard to stand up or manage simply won’t get adopted. 
• Privacy and control are non-negotiable. Enterprises need to trust that their telemetry and reasoning workflows remain within their boundary of control. 
• Agents must adapt to heterogeneous stacks. It’s not enough to reason over metrics from a single cloud service. Hawkeye works across Datadog, OpenSearch, Splunk, Azure, AWS, and more—because that’s what hybrid looks like. 

Building for the Now—With an Eye on What’s Next

We’re strong believers in the future of the open agentic web. Agents will soon collaborate, cross-check each other’s reasoning, and combine strengths. But none of that matters if you can’t deploy an agent in your environment today—and trust that it will work where your problems actually live.

That’s why NeuBird is focused on agentic workflows that work in the real world—hybrid, multi-cloud, and governed. We’re proud to have been early, and we’re even more excited to keep leading the way as more enterprises join this journey.

The future of agents is coming—but at NeuBird, we’re already solving for it today.

Why Enterprises Must Embrace Agentic Workflows—and Diversity of Thought in AI Agents

Enterprises are entering a new era—one not defined by dashboards and scripts, but by agentic workflows. In this world, AI agents don’t just generate responses—they act, decide, and reason. And the best ones do it with surgical precision.

At NeuBird, we’ve spent a few years building such an agent. Hawkeye, our AI SRE, is designed to operate in the chaos of IT telemetry, where more data is not just noise—it’s a liability. Hawkeye, available through the Azure Marketplace, was built from the ground up by SREs for SREs, with three core principles that define what an enterprise-grade agent must be:

1. Surgical Data Selection: Enterprises don’t lack data—they drown in it. But LLMs have a memory (context window) limit. The real challenge is not generating an answer, but finding the right context to reason with. Hawkeye’s core IP is in isolating the most relevant telemetry, fast—whether it’s logs, alerts, metrics, or traces—and ignoring the rest.
2. LLM-Powered Reasoning: Once the data is selected, Hawkeye reasons through it step-by-step, probing for causality, correlation, and historical patterns. This isn’t simple summarization—it’s the diagnostic loop an SRE would follow, encoded in AI.
3. Runbooks from Real Experts: Agents need guidance. Ours is driven by runbooks created by seasoned IT operators. These aren’t generic rules—they’re distilled expertise, tuned to enterprise reality.

This week Microsoft announced a wave of AI agents designed to solve real business problems—spanning IT operations, security, sales, and more.   Among them was the Azure SRE Agent—a natural addition to Microsoft’s growing agent ecosystem. The bigger story isn’t just one agent. It’s Microsoft’s broader commitment to the age of AI agents—and to building the open agentic web. Their support for Agent-to-Agent (A2A) protocols is especially noteworthy. With A2A, agents can securely exchange context, combine reasoning, and validate each other’s decisions—bringing more reliable outcomes and greater confidence in automated workflows.

It’s a vision we at NeuBird share. We’ve long believed that enterprises won’t rely on a single AI agent. Instead, they’ll assemble a team of agents trained in different “schools of thought” much like they do with human experts. It’s this diversity that drives better outcomes and more resilient systems.

Agents like Azure SRE and Hawkeye bring different strengths to incident response. Azure SRE offers deep, native insight into Azure environments, tuned for operational precision within that stack. Hawkeye adds cloud-agnostic reasoning and dynamic chain-of-thought workflows—correlating signals across observability, cloud, and incident management systems to surface root causes and drive real-time remediation. When these agents work together, they cross-check each other’s reasoning, fill in the blind spots and create a stronger, more resilient foundation for incident response. 

Diversity isn’t just valuable—it’s essential.

Because in the world of AI agents, the real risk isn’t bad data or model failure—it’s echo chambers.

The future belongs to enterprises that embrace agentic workflows and embrace AI diversity.  Stay tuned for more information on how these agents collaborate with each other!

In the world of Site Reliability Engineering (SRE) and IT operations, problems rarely come with clean, structured answers. Engineers are often tasked with sifting through vast piles of telemetry data, connecting dots across logs, metrics, traces, and alerts to pinpoint what went wrong and why. So, when people ask us, “What exactly makes your product an AI agent?”, we like to start with a simple idea:

An AI agent doesn’t just answer questions. It acts, taking a task to completion autonomously. 

In the world of IT operations this requires thinking like an SRE.Here’s how:

1. Surgical Data Selection

Access to relevant data is the foundation of effective troubleshooting. Protocols like MCP (Model Context Protocol) are crucial, helping our agents connect with external applications and tap into tribal knowledge across your organization. But in IT operations, more data isn’t always better. In fact, dumping entire logs or telemetry streams into a large language model (LLM) leads to confusion and hallucination.Precision is key. Just like a human SRE crafts the right grep or query, our AI agent first identifies and extracts only the most relevant slice of data before reasoning. For IT telemetry—metrics, alerts, logs, traces—this requires more surgical and mathematically precise query methods for selection and extraction. In short: no noise, just signal.

2. Iterative, Self-Reflective Reasoning

Identifying relevant data is just the beginning. Our AI agent then reads that data and starts reasoning with it—asking itself questions, forming hypotheses, and making follow-up queries. It explores other sources of telemetry, looking for correlation, causality, or missing context. This mirrors how human engineers debug: read logs, generate hunches, chase leads, and test theories.

This is where the agent becomes more than a query engine. It becomes a thinking system, capable of following a chain of thought.

3. Multi-LLM Validation and Argumentation

One of the core challenges of using generative AI in production systems is that results aren’t always mathematically or programmatically verifiable. To address this, our agent uses multiple LLMs to argue with and validate each other’s answers. Think of it like automated peer review.

If one model draws a conclusion, another is prompted to critique or double-check the reasoning. This helps weed out weak logic and reduce hallucinations, creating a more reliable AI partner for critical infrastructure work.

4. Incorporating Human and Unstructured Knowledge

Sometimes, structured telemetry isn’t enough. Our AI agent can bring in knowledge from less structured sources—like internal wikis, product documentation, past trouble tickets, or even direct human input. If the agent gets stuck, it knows how to ask the user for clarification or for additional context, just like a good junior engineer would.

It doesn’t pretend to know everything. It knows how to learn.

5. Expert-Guided Thought Chains via Runbooks

Finally, all this reasoning is guided by runbooks and heuristics created by veteran SREs and IT operators. These aren’t just scripts to follow blindly—they’re cognitive blueprints that tell the agent how to think in certain scenarios. Whether it’s a failed deployment, a CPU spike, or a flapping Kubernetes pod, our agent has a built-in mental model of how seasoned engineers would approach the issue.

This is what makes it an agent.

Not a chatbot. Not a dashboard. But a reasoning system that mimics how real-world engineers approach ambiguity, complexity, and problem-solving.

In the world of modern IT operations, this isn’t just a nice-to-have. It’s a necessity.

And we’re building it.

 

In my previous post, I explored why enterprises need AI agents—not just LLMs —to solve SRE and IT problems. While many IT leaders recognize the limitations of raw LLMs when confronting the complex realities of enterprise environments, there’s still a question that comes up again and again: what does it take to build an agent that enterprise teams can actually trust with their mission-critical operations?

The answer goes far beyond having access to powerful large language models (LLMs). Here are the essential elements that any purpose-built enterprise AI agent must address to be truly effective in production environments.

Navigating Enterprise IT Data

Enterprise data is fundamentally different from the expansive datasets that general-purpose AI models train on. The data that matters for resolving critical IT and SRE issues isn’t neatly packaged for consumption. It’s massive and fragmented—scattered across dozens of systems with their own access protocols and data formats. And unlike consumer queries, the stakes in enterprise operations are high.

Modern enterprises generate an overwhelming volume of telemetry—the combined output of monitoring systems, application logs, infrastructure metrics, network traces, and configuration states. The challenge lies in extracting the right data for analysis.

The challenge isn’t having enough data—it’s knowing exactly where to look.

Without a sophisticated approach to data navigation, teams waste precious time combing through irrelevant information while the incident clock ticks. An agent must have the intelligence to target the right data sources, apply appropriate filters, and extract only what’s relevant—all before meaningful analysis can begin.

This requires an understanding of data topography that goes far beyond what can be achieved through simple prompting of a generic language model. What you need is an agent that can navigate your enterprise data landscape with precision.

Four Cornerstones of Enterprise-Ready AI Agents

1. Data Precision: Finding What Matters

When your payment processing service suddenly degrades during peak traffic, finding the root cause isn’t as simple as checking a single dashboard. The answer lies scattered across API logs, cloud metrics, container data, and database performance stats.

An effective agent needs to know what data to fetch, where to find it, and how to filter signal from noise—before reasoning can even begin. This isn’t just a prompt engineering challenge; it’s an orchestration problem requiring intelligent data navigation.

At NeuBird, our agent Hawkeye is designed to extract only the relevant data needed for analysis, rather than attempting to process everything at once. This targeted approach allows for faster, more precise problem-solving while avoiding the context limitations that plague generic LLMs.

 

2. Trust Framework: Enterprise-Grade Connections

Most IT teams operate with a complex ecosystem of observability tools—each pulling from diverse data sources. Any AI system operating in this environment must respect governance boundaries through:

• Role-based access controls: The agent should inherit and respect your existing permissions systems, ensuring that sensitive data remains protected.
• Audit trails: Every data access, analysis step, and recommendation should be logged and traceable.
• Compliance-oriented architecture: Built from the ground up to operate within regulated environments, not as an afterthought.

Rather than bolting connectivity onto an existing LLM, we built Hawkeye around a core of enterprise data connections—designing systems specifically for secure, permissioned access to the full spectrum of IT telemetry.

3. Iterative Intelligence: The Problem-Solving Loop

Effective troubleshooting isn’t a one-shot process—it’s an iterative loop:

Ask a question > Get the right data > Reason about what you saw > Realize you need more context > Go fetch more > Repeat until clarity emerges

This mirrors how your best SREs actually work. Our iterative reasoning framework enables Hawkeye to:

• Form initial hypotheses based on available information
• Identify information gaps and actively seek the missing context
• Refine its understanding as new data becomes available
• Navigate the full reasoning cycle until it converges on solutions, not just observations

All of this at blazing fast speed ⚡ 

4. Expertise Embedded: Domain-Specific Knowledge

General AI models lack the specialized knowledge that experienced SREs develop through years of hands-on work with complex systems.

At NeuBird, we’ve built domain knowledge directly into Hawkeye’s foundation, encoding the expertise of veteran infrastructure engineers. This isn’t just a collection of static rules—it’s a dynamic reasoning framework that guides the agent through the intricate decision paths of IT troubleshooting. NeuBird’s AI SRE isn’t just smart—it’s trained to think like a human engineer. 

As my co-founder, Vinod, described in his article, domain-specific chain-of-thought is the new runbook. They are dynamic, context-aware and act as reasoning guides for LLMs.

AI SRE in Action: Real Business Transformation

When deployed in production environments like Model Rocket’s AWS infrastructure, Hawkeye delivers concrete, measurable results:

• Reduced incident resolution times, up to 92%—turning hours of troubleshooting into minutes
• Blazing fast root cause analysis
• 24/7 expert-level analysis across your IT stack

Hawkeye’s secure multi-source connector architecture brings AI reasoning to where your data lives, while maintaining strict governance requirements. For businesses managing complex cloud environments, this enables instant access to AI-driven analysis without compromising security or compliance.

The Path Forward

The future of enterprise AI depends not on smarter models alone, but on agents that truly understand the enterprise context, connect reliably to existing data ecosystems, and deliver trusted outcomes.

As AI reshapes how we manage complex systems, the organizations that thrive will be those that embrace purpose-built agents that enhance their operational capabilities. These agents will transform how teams respond to challenges, allowing them to shift from reactive firefighting to proactive optimization.

At NeuBird, we’re building for this future. Hawkeye isn’t just another AI tool—it’s your AI-powered SRE built for the enterprise. Always reliable, always private, always accurate.

There’s a quiet truth that anyone building serious agentic systems eventually discovers:  this isn’t just about chaining together powerful LLMs.

It’s about making hard choices between competing priorities that most organizations aren’t prepared to navigate.

Let me explain.

The Three-Axis Problem of Agentic Design

When we build AI agents that can reason, iterate, and troubleshoot IT systems, we’re really trying to solve a three-axis optimization puzzle:

Speed: You need answers quickly, especially during incidents when every minute costs money.

Quality: The answer must be accurate and actionable—not just plausible.

Cost: Each LLM call consumes expensive computational resources. It hits a GPU, and GPUs aren’t cheap or infinite.

Here’s where the challenge lies:

• If you increase reasoning depth to improve quality, the agent slows down and burns more compute.
• If you rush the workflow to save time and money, quality suffers.
• If you chase quality at any cost, you blow past SLAs and budget constraints.

This isn’t theoretical—I’ve witnessed this tension play out across every enterprise AI implementation I’ve been involved with, not just for SREs, but across the entire spectrum of enterprise AI.

Domain-Specific Chain-of-Thought is the New Runbook

The best way we’ve found to optimize across these axes is through domain-specific chain-of-thought.

This is dynamic, context-aware reasoning that guides the agent’s search:

• They help the agent decide what questions to ask and what data to examine first
• They eliminate wasteful exploration paths
• They encode years of operational knowledge from human engineers

Domain-specific chain-of-thought makes agentic workflows predictable, efficient, and tunable—three things you won’t get from a raw LLM chain, no matter how sophisticated the model.

The Hidden Cost: Dirty or Redundant Data

Another silent killer in this equation? Enterprise data sprawl.

Most enterprise telemetry is noisy, redundant, or outdated. If an agent doesn’t know how to:

• Filter irrelevant signals
• De-duplicate overlapping metrics
• Access telemetry in a structured, governed  way

…it ends up consuming more GPU cycles, taking longer to reason, and returning less useful answers. I’ve seen organizations waste millions on AI solutions that failed because they couldn’t navigate the messy reality of enterprise data.

At Neubird, we’ve built our AI SRE agent on top of an enterprise-grade telemetry platform. Why? Because you simply cannot  optimize for speed, cost, and quality unless you start with clean, properly scoped  data.

The Future of Enterprise AI is Resource-Aware

LLMs aren’t infinite. Neither is your cloud bill. The next wave of enterprise agents won’t be judged merely on their intelligence, but on how resource-aware they are.

The winners will be agents that can:

• Adapt their reasoning depth based on the situation
• Tune workflows according to  urgency or user role
• Make smart decisions about when to go deep vs. when to act fast
• Ignore what doesn’t matter

This isn’t flashy, but it’s what delivers actual value. I’ve learned that the hardest engineering challenges aren’t about theoretical capabilities—they’re about making the right tradeoffs in complex environments.

That’s where we’re heading. That’s what we’re building at Neubird.