Categories: Thought Leadership

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.

After three full days at SREcon25, my mind is buzzing and my feet are tired—but I couldn’t be more energized! I had the privilege of speaking with dozens of SREs and IT leaders about their daily challenges, triumphs, and aspirations for what’s next in site reliability engineering.

What quickly became clear is that while tooling has evolved tremendously, mainly around observability and capturing data, the human element of ITOps—specifically the burden placed on SREs for incident response—remains a critical challenge for organizations of all sizes.

5 Themes That Dominated Our Booth Conversations

1. “We have so much knowledge locked in our SREs’ heads—we need a way to leverage it efficiently.”

This sentiment was echoed consistently across conversations. Companies recognize the immense value their SREs bring, yet watch them spend hours troubleshooting complex issues, pulling them away from strategic initiatives. One IT director from a financial services company put it bluntly: “Our SREs are our most valuable asset, but they’re drowning in alerts and diagnostics instead of designing more resilient systems.”

2. “The constant escalations are affecting our work-life balance and team morale.”

A senior SRE from a major e-commerce platform shared how being perpetually on-call has affected her work-life balance. “My team is amazing, but we’re constantly fielding escalations that, frankly, could be solved automatically if we had the right tools analyzing our telemetry data.” This reality has led to burnout and turnover, creating a vicious cycle where institutional knowledge walks out the door.

3. Documentation and RCA reports consume hours that could be spent on more valuable work.

Documentation emerged as a surprising pain point. After spending hours solving an issue, the last thing teams want to do is document every detail for the post-mortem. But without that documentation, organizations miss opportunities to learn and improve. This tedious but crucial step often gets shortchanged in the rush to move on to the next fire.

4. We’re spending more time correlating data across multiple dashboards than solving problems.

The dashboard fatigue was real. Many teams have monitoring for their monitoring systems. But connecting the dots across all these systems still requires a human to manually correlate data from multiple sources. What these teams want isn’t more dashboards—it’s intelligent analysis that delivers answers, not just more data points.

5. “We need round-the-clock expertise, but hiring and retaining specialists is increasingly difficult.”

The talent shortage was a recurring theme. “Finding and retaining SREs with deep expertise across our entire stack is nearly impossible,” said a VP of IT Operations. “We need that expertise available round-the-clock, but scaling our team isn’t financially feasible.”

The AI SRE Teammate: A Paradigm Shift

What made our conversations at SREcon particularly exciting was hearing reactions to Hawkeye—our AI SRE sidekick. When we demonstrated how Hawkeye works alongside SRE teams to diagnose complex issues in minutes, the responses were illuminating:

“That’s so cool!” said a senior SRE after watching Hawkeye analyze a complex diagnostic package in real-time, pinpointing a memory issue in their billing service that was affecting order processing.

“I love those hats! But I love what’s under them even more,” quipped an IT director, referring to both our booth swag and Hawkeye’s capabilities. “The ability to have an AI teammate that can scale our team’s expertise without adding headcount? That’s exactly what we need.”

What Really Matters to Today’s SREs

IT Operations is the New Battleground for Digital Success

As organizations continue their cloud journey, the complexity of managing distributed systems has positioned IT operations as a critical competitive differentiator. Companies that can maintain reliability while innovating rapidly have a clear advantage—but this balancing act is increasingly difficult with traditional approaches.

SREs Deserve Better Tools

The engineers we spoke with weren’t looking to be replaced—they were looking to be empowered. They want tools that understand context, learn from past incidents, and deliver clear, actionable insights rather than just more alerts. As one SRE put it: “I want to spend my expertise on designing resilient systems, not parsing through logs for hours.”

AI is Ready for Mission-Critical Operations

What struck me most was the shift in perception around AI in ITOps. The skepticism of previous years has given way to genuine excitement about the possibilities of GenAI working alongside human experts. When attendees saw how Hawkeye by NeuBird could diagnose issues across multiple tools and platforms in minutes rather than hours, the light bulbs went on.

Time to Resolution is the Metric that Matters

While organizations track numerous SLAs and metrics, the one that resonated most was Mean Time to Resolution (MTTR). “Every minute of downtime costs us thousands in revenue and erodes customer trust,” explained a DeVops team lead. “If we could reduce our MTTR by even 50%, the impact would be tremendous.”

Read more: We are building the soul of your ITOps team

Reimagining What’s Possible

As we packed up our booth yesterday, I couldn’t help but reflect on the significance of these conversations. The challenges facing SRE and IT operations teams are substantial, but so is the opportunity to transform how these teams work through intelligent automation and AI collaboration.

The future of ITOps isn’t about replacing human expertise—it’s about amplifying it. It’s about creating an environment where SREs can leverage their deep institutional knowledge to build more resilient systems while having an AI sidekick handle the routine investigation and analysis that consumes so much of their time today.

If you’re facing similar challenges in your organization, we’d love to continue the conversation. Let’s explore how Hawkeye can help your team reduce MTTR, scale your operations, and transform your approach to incident management.

Book a demo today and discover what’s possible when AI and human expertise work side by side.

Every few months, I get asked the same question—sometimes by investors, sometimes by prospects, and sometimes by deeply curious engineers:

“Why do we need an agent? Can’t I just ask Claude or GPT to look at my logs or dashboards and tell me what’s wrong?”

On the surface, it’s a fair question. Ask these models almost anything and they’ll generate a convincing – sometimes even correct – answer. It’s tempting to imagine that with the right prompt, you can have an AI Sherlock Holmes on your team—ready to sift through logs, spot anomalies, and pinpoint root causes on demand.But this perspective on LLMs, while understandable, oversimplifies what’s needed in enterprise environments.

After years building and scaling enterprise infrastructure, I’ve learned that this fantasy crumbles under the weight of reality. Let me explain why.

The Enterprise Reality Check

1. You Need the Right Data—Not All the Data

A typical enterprise generates terabytes of telemetry daily—metrics, logs, traces, events, configs, and tickets across hundreds of services and teams. No LLM, not even GPT-4 Turbo with its expanded context window, can reason over this firehose in one go.

When an SRE asks a question like “Why did latency spike in the checkout service at 2 AM?”, the answer doesn’t lie in all your logs. It lies in a few very specific pieces of information, scattered across multiple tools and sources.

An agent needs to know what data to fetch, where to fetch it from, and how to filter it down to what matters—before any reasoning even begins. That’s not just an LLM prompt; it’s an orchestration problem.

2. Governance and Access Control Are Non-Negotiable

In the enterprise, data access is gated. For good reason. Teams have different levels of access. Some logs may contain sensitive credentials, PII, or customer data. You can’t just scoop all that into a cloud LLM, nor should you.

Any AI system operating in this environment must respect governance boundaries. That means role-based access, audit trails, and data isolation—all things you won’t get by copy-pasting into an off-the-shelf LLM.

3. Effective Troubleshooting Requires Iterative Reasoning

Even when you have the right data, real troubleshooting is not a one-shot process. It’s a loop.

• Ask a question
• Sample some data
• Reason about what you saw
• Realize you need more context
• Go fetch more
• Repeat until clarity emerges

This is where a well-designed AI agent shines. It wraps the LLM in an iterative loop—a structured reasoning engine that can interact with systems, query APIs, follow hunches, and converge on an answer. At NeuBird, we call this the thinking loop—and it’s the foundation of how our AI SRE agent works.

Image. “The Thinking Loop”- Iterative Reasoning Process.

4. Domain-Specific Reasoning – the Missing Ingredient

Our agent, Hawkeye, isn’t just smart—it’s trained to think like an SRE. It follows runbooks, just like humans do. These aren’t static scripts. They’re dynamic workflows built from the collective expertise of professionals who’ve managed infrastructure for years.

At NeuBird, we’ve distilled decades of domain knowledge into these runbooks. They guide the agent’s decision-making: when to look at pod-level logs, when to check autoscaler behavior, when to cross-check a spike against a recent Terraform apply.

What makes these runbooks special is how they function as reasoning guides for LLMs. Rather than drowning LLMs in raw data, we deploy these reasoning engines surgically, with runbooks providing the essential context. This targeted approach helps extract the best analytical power from LLMs while adding the domain-specific knowledge they inherently lack.

LLMs can’t learn that from first principles. They need scaffolding. They need rules. They need the wisdom of people who’ve done this work for a living. That’s what we build into the agent.

5. Connections Across Multiple Enterprise Data Sources

Most IT teams operate with a complex ecosystem of observability tools—each pulling from diverse data sources. The answer to critical infrastructure questions doesn’t reside in a single log file. It’s scattered across multiple tools and sources.

An effective agent needs to know what data to fetch, where to find it, and how to filter it down to what matters—before reasoning can even begin. This isn’t just a prompt engineering challenge; it’s an orchestration problem requiring reliable, enterprise-sanctioned connections to your data infrastructure.

6. DIY Projects Are Fun—Until They’re Not

Yes, you can prototype a toy version of this. You can wire up ChatGPT to your Datadog logs or pipe some traces into Claude and get a flashy demo. I’ve seen it. I’ve done it.

But that’s not a production-grade system. Real-world systems have:

• 20+ sources of telemetry
• Multi-team permissions
• On-call workflows
• Compliance constraints
• SLAs tied to minutes

Building an agent that works across all of that? That’s not a weekend project. That’s a multi-year, cross-functional engineering challenge—and one that NeuBird has chosen to take on so our customers don’t have to.

Read more: Our from the trenches insights on ITOps from SREcon 2025

The Bottom Line

LLMs are tools. Powerful ones. But they are not solutions by themselves. In the world of IT and SRE, where context is everything and stakes are high, you need more than just generative language—you need generative operations.

That’s what we’re building at NeuBird.

An agent that doesn’t just answer questions—but solves problems.