The Problem: When Observability Data Exceeds Human Capacity
It’s your first week on-call and you get paged at 3am. You’re scrambling through runbooks, searching error messages, trying to understand dependencies in a web of microservices. After talking to a few teammates and gaining context on the system, you resolve it, but not before billing services went down for 15 minutes. Now management wants an RCA.
This scenario is familiar to anyone working in SRE, but the core problem isn’t just the incident itself. It’s that you had to manually hunt through logs, metrics, and traces across dozens of services to understand what happened. Modern observability generates data at a scale that makes manual analysis impractical. A single incident might involve correlating thousands of log lines, hundreds of metrics, and traces spanning 20+ services.
The traditional approach of grep, dashboards, and manual correlation breaks down at scale. You can’t realistically query every relevant log stream, check every metric, and trace every request path in real-time. The signal is there, but it’s buried in petabytes of noise.
Context Engineering: Extracting Signal from Noise
This is fundamentally a context engineering problem: how do we automatically extract relevant signals from massive telemetry datasets, understand relationships between events and services, and build actionable incident context?
Context engineering isn’t just about storing or querying observability data. It’s about understanding what data is relevant for a specific situation, how that data relates across system boundaries, and what it means in the context of the incident at hand. For the billing outage example, context engineering would identify:
Which services are upstream and downstream of billing
What error patterns appeared before the outage
How those errors correlate with metrics like latency or resource usage
What similar incidents looked like in the past
This requires more than static service maps or predefined dashboards. It requires systems that can dynamically reason about observability data in the context of what’s actually happening.
Agentic AI and Observability
Agentic AI systems are particularly well-suited for this problem. Unlike traditional monitoring tools that require predefined queries and alerts, agentic systems can navigate telemetry data autonomously. They can follow traces across service boundaries, correlate log patterns with metric anomalies, and reason about causal relationships in distributed systems.
At NeuBird, we’ve built upon these principles. The system uses context engineering to automatically:
Map service topologies and dependencies in real-time
Correlate events across logs, metrics, and traces
Identify root cause patterns across distributed traces
Generate incident context that’s actually actionable for SREs
Instead of dumping raw telemetry data or firing dozens of alerts, the system provides targeted information: “Service X is failing because the database connection pool is exhausted, which started when deployment Y rolled out 10 minutes ago.”
Implementation Considerations
Building effective context engineering requires solving several technical challenges:
Data Volume and Velocity: You need to process telemetry streams in real-time while maintaining enough historical context to identify patterns. This isn’t just a storage problem—it’s an indexing and correlation problem at scale.
Service Topology: Understanding relationships between services is critical. Static configuration often drifts from reality, so you need automated topology discovery that reflects actual communication patterns.
Semantic Understanding: Logs and metrics are only meaningful if you understand what they represent. Error messages like “connection refused” mean different things depending on where they appear and what else is happening in the system.
Causality: Correlation isn’t causation, but in distributed systems, identifying causal relationships is essential for root cause analysis. This requires reasoning about temporal ordering, dependencies, and failure modes.
Where This Is Heading
Context engineering for observability is still early, but the direction is clear. As systems continue to scale and become more complex, manual analysis becomes impossible. We need systems that can autonomously navigate telemetry data, understand system behavior, and provide SREs with actionable context rather than raw data dumps.
This doesn’t replace SREs – it enables them. The goal is to handle the data processing and correlation work that exceeds human capacity, allowing engineers to focus on decision-making and remediation.
If you’re interested in this space, the Agentic AI and observability communities are actively working on these problems. The principles of context engineering apply beyond just incident response – they’re relevant for capacity planning, deployment validation, and understanding system behavior at scale.
I realized today that I am now too lazy to $cat a README.md file.
I enjoy certain tactile and manual experiences, like taking portraits or wildlife photos (pictured above). But, when it comes to the jungle that is my code repository, I would rather let Claude Code sort it out. Even if it’s just extracting information from a 50-line readme file. This got me thinking… Why even bother writing things for human consumption anymore, when so much of what we produce is fed directly into LLMs?
Claude Code is also guilty of writing ‘useful tips’ for humans. It assumes that I want to know how to solve an obscure bug by auto-documenting it in a readme file. The thing is, I am offloading 99% of programming and implementation logic to Claude. The last thing I want to do is open a document to read how to address an obscure bug that I faced a few context sessions ago. Next time I open this project will be with a coding agent. Not some old IDE, Vim, or Notepad++.
What I really want is for Claude to remember project nuances every time, even if I close all the terminals and browser windows and come back 3 months later with a mean attitude. And since I can’t be bothered to use vim anymore, I want Claude to just be smart enough to remember how to solve problems, without charging me for an enormous context window. Doesn’t it just feel jarring when Claude forgets what AWS profile to use, after spending 10 minutes on a compaction process? How could it throw away something I’ve been asking it to run over and over again?
That brings me to CLAUDE.md. Why Claude doesn’t automatically maintain (r+w) this file with its superb reasoning capabilities is beyond me. In theory, it should do a perfect job in maintaining important footnotes about my project. Sure, it maintains a MEMORY.md, but that’s user-specific with potentially sensitive information that doesn’t get committed to repositories. Anecdotally, I’ve also heard that the extent to which Claude Code consults CLAUDE.md or MEMORY.md can be inconsistent, often ignoring explicit instructions.
The real problem isn’t that Claude Code has a bad memory; it’s that every coding agent today is designed around a human-in-the-loop. It writes READMEs so you can read them. It adds inline comments so you can understand the logic. It documents bugs so you can look them up later. Every artifact it produces assumes a human is on the receiving end.
But that assumption is already feeling outdated. My next interaction with a project isn’t going to be me skimming a README to better understand the architecture. It’s going to be me opening a new Claude Code session and saying “pick up where we left off.” The agent is my primary producer, consumer, and executor. I’m just a person with lots of wonky ideas and irrational expectations.
Until agents are authoring context for other agents rather than human readability, the workarounds are going to feel like duct tape. You can manually write your CLAUDE.md, front-load context at the start of every session, and guide the model to refer to specific documentation. You can add SKILL.md to define when and how to run specific tasks. These methods incrementally improve the experience.
But the shift that actually matters is agents treating persistent, machine-readable context as a first-class output, not a side effect of being helpful to humans. We’re not there yet. But the fact that I’m too lazy to $cat a README file is, I’d argue, a small indicator of where this is all going.
My takeaway is that we all use coding agents in our own unique ways. We are all learning its quirks together. And we all have our fascinations and frustrations with it.
But the trajectory is clear: our expectations for what these agents can do are growing fast. A year ago I couldn’t have imagined the workflows I’m running today. A year from now, CLAUDE.md will probably read me instead.
We hear similar stories from the SREs and DevOps engineers we work with. They are pushing coding agents in so many creative ways. They are breaking existing workflows and testing the boundaries of what is possible with the current state of AI. It has inspired us to innovate on what the next generation of AI SRE tooling looks like.
Stay tuned for some exciting news in the coming weeks.
And thanks for taking the time out of your day to README.md
How to upgrade from observability to actionability
If you could only pick one tool for software development, Claude or Stack Overflow, which would you choose?
That wasn’t a hard question, was it? Stack Overflow traffic collapsed by 50% following ChatGPT’s launch in late 2022 (VentureBeat). AI didn’t just disrupt Stack Overflow. It made us rethink our choice of tools in many different areas.
Now ask yourself the same question about your IT Incident Response tooling.
If you could only pick one approach to understanding and fixing production issues, which would you choose? An AI agent that can reason across your entire telemetry stack, or your Datadog dashboards?
The Revolution That Already Happened
Let’s acknowledge the past. Datadog changed the observability game. Before Datadog, engineering teams were drowning in tool sprawl: one tool for metrics, another for logs, another for traces, yet another for alerting. Datadog centralized all of that into a single platform under a unified data model with over 1,000 integrations (Datadog). That was genuinely revolutionary and the market rewarded it with $2.68 billion in revenue in fiscal year 2024.
But that revolution was mostly about observability: centralizing data and making it visible. The core innovation was “all your telemetry in one place, beautifully rendered.” The consumption model was still a human being opening a browser, staring at panels, and reasoning through what the pretty graphs meant.
That was the right answer in 2016. It is the wrong approach in 2026.
The game has changed. The question is no longer “can I see what’s happening in my systems?” The question is “can I act on what’s happening, fast enough, at a scale and complexity that exceeds human cognitive capacity?”
Dashboards only provide observability. Tools built on top of dashboards summarize observed data. Only the next generation of AI-native tools will provide true actionability.
Observability vs. Actionability
Observability is the ability to understand the internal state of a system by examining its outputs: metrics, logs, and traces. It answers the question: what is happening? Dashboards are the native interface for observability. They visualize data. They render time-series. They let humans scan, filter, and explore. The value proposition is visibility.
Actionability is the ability to automatically detect, diagnose, and respond to system behavior in real time, across every relevant data source, without depending on a human to be the reasoning engine. It answers a fundamentally different question: what should we do about it, and can we do it now?
Observability tells you the kitchen is on fire. Actionability puts out the fire while simultaneously telling you which burner caused it, why the smoke detector didn’t trigger sooner, and that the same burner had a gas leak flagged in a maintenance ticket three weeks ago.
The problem with the observability-era is that it assumed human cognition was the bridge between data and action. Dashboards collect and display information. Then, humans observe and formulate a course of action. This worked when systems were simpler, data volumes were manageable, and the blast radius of an incident was limited. And most importantly, the pace of software development scaled linearly with the number of developers.
None of those conditions hold anymore.
Modern production environments generate telemetry at a volume and velocity that overwhelms human processing. A single Kubernetes cluster can produce thousands of metrics per second. Microservice architectures create dependency graphs with hundreds of nodes. Multi-cloud deployments scatter signals across regions and providers. And AI brings exponentially faster development and deployment cycles.
Products built around dashboards have a specific architectural DNA. They are designed to ingest, store, and index telemetry to render it for human consumption. Every design decision, from data retention policies to query languages to UI patterns, is optimized for a human being sitting in front of a screen, asking questions, and interpreting visual answers. It simply hoards data because it is not designed to optimize for what’s valuable and what’s not. 90% or more of the data is unactionable. Yet every year, companies simply pile on more and more data and pay for every kilobyte of it.
AI-native agent platforms have completely different architectural DNA. They are designed to interpret precise data from any source, reason across it programmatically, and produce accurate remediation steps. No curated panels and no assumptions that someone needs to “see” the data before something can be done about it.
This difference isn’t cosmetic. It creates at least three fundamental limitations that dashboard-native products cannot overcome by simply bolting AI features onto their existing platform.
Limitation 1: The Single-Pane-of-Glass Ceiling
Dashboard platforms pride themselves on being a “single pane of glass.” But that pane only shows you what the platform ingests. And the more you ingest, the more you pay.
Real incidents don’t respect vendor boundaries. The root cause of your latency spike might involve the config change documented in a Jira ticket, the deployment that went out through your CI/CD pipeline, the Slack conversation where an engineer mentioned something weird about the staging environment, the runbook your team wrote in Confluence six months ago, and the fact that a similar pattern appeared in an incident postmortem from last quarter.
A dashboard shows you a slice. An AI agent reasons across the whole picture.
AI-native platforms can integrate context from infrastructure telemetry, change management systems, team communication, documentation, incident history, and code repositories simultaneously. They don’t need a panel for each data source. They don’t need a human to visually correlate across 12 browser tabs. And most importantly, they don’t need to copy and store data again in a different format just to correlate information.
Limitation 2: The Reactive Trap
Dashboards are, by their nature, reactive interfaces. You open a dashboard in response to something: an alert, a customer complaint, a hunch. Even proactive monitoring features like anomaly detection still ultimately surface their findings on a dashboard that a human has to look at, interpret, and act on.
True AI-native agents flip this model entirely. They don’t wait for you to open a browser. They don’t surface anomalies on a panel for you to notice between meetings. They detect, investigate, and begin resolution autonomously. By the time you see the Slack notification, the agent has already correlated the symptom with the cause, checked whether the pattern matches historical incidents, and drafted a remediation plan.
Limitation 3: The Cost-Complexity Spiral
Here’s where the financial argument gets brutal.
Dashboard-centric platforms charge you to ingest, index, and store data for visual consumption. The more complex your infrastructure, the more data you generate, and the higher your bill. Mid-sized companies routinely spend $50,000 to $150,000 per year on Datadog, with enterprise deployments easily exceeding $1 million annually once APM, logs, and RUM are included (Middleware, 2025). In extreme cases, a single customer has generated a $65 million annual bill, as Coinbase did in 2021 before restructuring their contract (The Pragmatic Engineer, 2023).
And what does that spending buy? Fundamentally, it buys the right for a human to look at the data. All that ingestion, indexing, and retention is in service of rendering panels that someone has to create and visually interpret. The more complex your systems get, the more data you need to ingest, the more you pay, and the harder it becomes for a human to reason across all of it. Cost scales up while human effectiveness scales down.
AI-native agents break this spiral because they don’t need to render and store every metric for human visual consumption. They reason over signals. They can be selective, contextual, and efficient about which data matters for a given investigation.
AI-native agents don’t require petabyte-levels of data ingestion, nor do they need a costly indexing of data which are only sampled and mostly irrelevant. They read exactly what they need, when they need it, across whatever sources are relevant for the investigation.
The result is that AI-native platforms can deliver significantly better outcomes, faster resolution, and more proactive detection while consuming much fewer resources. You’re not paying to display data. You’re paying for precise diagnosis and automated remediation, just when you need it most.
Verbally Describing a Painting
And this brings us to the most revealing tell of the observability industry.
Dashboard vendors know the model is breaking. They can read the same tea leaves everyone else can. So what are they doing about it? They’re bolting AI onto their dashboards that summarizes the data in natural language.
These companies spent billions of dollars building sophisticated visualization platforms to display data. Beautiful charts. Real-time streaming graphs. Custom widgets. And now their big AI innovation is to convert all of that visual data back into plain English sentences.
It’s like creating an elaborate art piece and then hiring someone to stand next to it and describe the intricacies. The painting was supposed to be the communication medium. If you need a translator for your translator, the original medium has failed.
But the irony goes deeper. When a dashboard vendor adds natural language querying, voice interfaces, or autonomous investigation agents, they are not enhancing the dashboard experience. They are building escape hatches from it. Every conversational AI feature is a way to not look at dashboards. And the investigation agents simply state the obvious that you can already see: latency graph goes up, user experience goes down.
The truly forward-thinking approach isn’t to sprinkle AI on top of dashboards. It’s to start with AI-native reasoning that can precisely and efficiently gather only the relevant information from your infrastructure. You can’t provide that kind of a revolutionary product when your core business model is customers indexing billions of ‘what if’ data points in your vendor locked platform.
What Comes Next
The post-dashboard operating model looks like this:
An AI agent that has secure access to your metrics, logs, traces, topology, change events, runbooks, incident history, team communication, and code repositories. When something goes wrong, the agent detects and reacts to it autonomously. It correlates the symptom with potential causes across every data source. It checks whether the pattern matches historical incidents. It identifies the most likely root cause, assesses blast radius, and recommends or initiates remediation. It shows the evidence trail so engineers can verify its reasoning.
If your incident response strategy still fundamentally depends on a human opening a browser, navigating to a dashboard, visually scanning panels, and manually reasoning through root cause, you’re using a 2016 reactionary model for a 2026 problem. Your systems are complex, your data is voluminous, and your engineers’ time is too valuable to spend as a reasoning machine between data and decisions.
Stack Overflow didn’t die because it was bad. It died because something categorically better arrived and made the old way feel absurd in retrospect.
Your dashboards are next.
Start your actionability journey here with our free trial of the next generation AI-native SRE Agent.
If you’re a software engineer, Cursor needs no introduction. It is reshaping how we write, test, debug, and ship code.
Cursor was built for developers from the ground up. It indexes entire codebases, generates functions, refactors modules, and writes tests with minimal hand-holding. Its deep context-awareness is what makes it feel less like a tool and more like a teammate.
But what about the “other side” of the engineering org? The Site Reliability Engineers, DevOps Engineers, Platform Engineers, and Engineers who are on-call to keep production code running while developers constantly innovate and ship new features?
That’s where NeuBird comes in. We provide the AI-native SRE solution, bringing a Cursor-like agentic workflow to operations teams. Here is how we do it:
Telemetry Indexing
Similar to how Cursor indexes your entire codebase, NeuBird indexes your observability data. It connects to monitoring tools, incident management platforms, communication tools, and cloud providers to build an understanding of your infrastructure topology, service dependencies, tribal knowledge, and historical incident patterns.
Agentic Incident Response
NeuBird’s agentic workflow follows a strategic process:
Dynamic Investigation Planning: Formulates an investigation plan, adapting based on retrieved historical context and the specifics of the current incident.
Secure Telemetry Query Generation: Rather than sending raw telemetry to the LLM, NeuBird generates structured telemetry queries in a common syntax, ensuring reliable data access regardless of the underlying data source.
Secure Data Processing: Queries execute in a secure, ephemeral runtime environment. Only abstracted metadata is shared with the LLM for reasoning.
Iterative Refinement: Refines its analysis based on each round of data retrieval, adapting to new information.
Root Cause Analysis and Remediation: The final output includes correlated signals, identified root cause, and actionable remediation steps.
The Autonomy Slider
Like Cursor’s autonomy slider, NeuBird offers varying degrees of automation. At its most basic, it functions as an investigative assistant that provides pre-analyzed context to on-call engineers. At its most autonomous, it can leverage coding agents to automate remediation steps, with human-in-the-loop controls to verify production changes. NeuBird covers all areas of incident response, from alert triage to resolution.
The Parallels
1. Deep Contextual Understanding
Cursor
NeuBird
What it indexes
Your entire codebase: files, AST, dependencies, git history, PR summaries
Your entire observability ecosystem: metrics, logs, traces, topology, incident history
What it understands
Code structure, dependencies, patterns, architecture, team conventions
Infrastructure topology, service dependencies, historical incident patterns, change events
Context retrieval
Natural language queries matched to relevant code chunks
Incident signals matched to relevant historical investigations and runbooks
Cursor doesn’t just see files in a directory. It understands that a change to your authentication module affects your API gateway, your middleware, and your test suite. NeuBird doesn’t just see CloudWatch alarms. It understands that a CPU spike on your EKS cluster correlates with a deployment event from 45 minutes ago that changed a database connection pool setting affecting downstream services.
2. Agentic Workflows
Cursor
NeuBird
Trigger
Developer prompt or task description
Alert from PagerDuty, Datadog, CloudWatch, or manual investigation
Planning
Multi-step plan with file paths, code references, and a to-do list
Dynamic investigation plan informed by historical patterns and current incident signals
Generates telemetry queries, correlates signals across platforms, identifies root cause, and presents executable remediation steps
Iteration
Re-runs tests, fixes errors, refines until code works
Refines analysis based on each round of data retrieval, adapts to new findings
Cursor’s Agent mode plans multi-step changes, executes them across files, runs tests, and iterates until the code works. NeuBird’s agentic workflows do the same for incidents. When an alert fires, NeuBird automatically queries relevant data sources, correlates signals across platforms, identifies root cause, and delivers actionable remediation steps.
The key similarity is the closed-loop nature of both systems. Neither tool generates a response and walks away. They execute, observe the results, and adjust their approach iteratively until the objective is met.
3. Works With Your Existing Tools
Cursor
NeuBird
Foundation
Fork of VS Code: familiar UI, extensions, keybindings carry over
Integrates with tools already in your stack, no rip-and-replace
Ecosystem
VS Code extensions, Git, GitHub, terminal, MCP servers
Datadog, Splunk, Prometheus, CloudWatch, PagerDuty, ServiceNow, Slack, Jira, Grafana, MCP servers, and more
Marketplace
Downloadable on major OS
Available on AWS and Azure Marketplace
Setup
Install and open a directory as a project
Connect via API keys, setup in under 10 minutes
Cursor is based on VS Code because that’s where developers already work. They keep their extensions, keybindings, and muscle memory. NeuBird integrates with the tools you already run: Datadog, PagerDuty, Slack, ServiceNow, because rip-and-replace doesn’t work for production infrastructure. Both tools meet you where you are.
4. Enterprise-Grade Security
Cursor
NeuBird
Certification
SOC 2 Type II
SOC 2 Type II
Dataretention
Privacy Mode with zero-retention agreements with all LLM providers
Zero persistent data storage; telemetry processed in real-time in-memory and discarded after use
Code/data exposure
Raw source code never stored on servers; only obfuscated metadata persisted (In Privacy Mode)
Only abstracted metadata shared with LLM; raw telemetry never leaves your environment
Read-only access to telemetry, RBAC, SSO/SAML, audit trails
Encryption
AES-256 at rest, TLS 1.2+ in transit
Enterprise-grade encryption in transit and at rest
Training policy
Your code is never used to train models
Your data is never used to train models
Cursor offers SOC 2 Type II compliance, Privacy Mode with zero-retention, and sandboxed execution environments. NeuBird is SOC 2 Type II certified, offers VPC deployment, uses read-only access to your telemetry, and never stores your data persistently.
For operations teams, the security story is arguably even more critical. Telemetry data can contain customer PII, financial transactions, authentication tokens, and other sensitive information. NeuBird’s read-only architecture keeps all raw data within your existing infrastructure. To precisely retrieve relevant information, NeuBird generates structured queries that the secure data access layer translates into platform-specific API calls. Data flows back through the secure processing layer, where analysis happens without exposing raw telemetry to the language model.
The Operations Tooling Revolution
Just as Cursor is specialized to handle complex codebases and enable agentic workflows for programming, NeuBird specializes in handling complex issues in production-grade environments through agentic workflows for incident response.
The developer tooling revolution is well underway. The operations tooling revolution is just getting started.
If your SRE team is spending more time investigating incidents than building for reliability,try Hawkeye free for 14 days and see how AI-native operations tooling changes the way you work.
Why some AI SREs impress in demos but disappoint in production, and what it takes to build an agent that actually works.
You can build a “Demo AI SRE” in weeks: an LLM connected to a few integrations with a polished UI. It will explain logs you paste in, summarize dashboards, and suggest plausible causes. But when your on-call engineer uses it against a production incident with inconsistent schemas from dozens of tools, missing traces, and more than 1TB of logs from hundreds of services, it will struggle to find the actual root cause. According to MIT Sloan research, only 5% of custom generative AI pilots succeed. The rest rely on generic tools that are “slick enough for demos, but brittle in workflows.”The jump between “impressive demo” and “trusted production tool” is where most AI SRE initiatives fail.
This guide provides a technical framework for evaluating AI SRE tools based on these engineering challenges that separate production-ready solutions from demo-ware.
If you’re running a POC, or comparing vendors, use these criteria to cut through the noise.
The Illusion of Competency
Modern LLMs are remarkably capable at high-level incident resolution tasks, given the right set of inputs and guidance. With minimal engineering effort, you can build systems that perform impressively in controlled settings. Here are some examples and some questions you should ask yourself afterwards.
Log / Metric Explanation: Paste a stack trace or metric anomaly, and ChatGPT will provide a coherent interpretation. This works because the context is pre-selected and the LLM’s pattern-matching excels at explaining structured data it can see in full. Ask yourself: who retrieved the logs / metrics and what system is needed to automate precise retrieval.
RAG-Style Dashboard Q&A: “Ask your dashboard” experiences retrieve relevant panels and let the LLM summarize them. Effective for exploration, but fundamentally limited to data points that are already visualized. Ask yourself: is the output helping me get to the bottom of the issue at hand, or is it just summarizing two different graphs I saw on the dashboard.
Single-Source Agents: An agent that queries only Datadog logs, or CloudWatch, or Splunk can be built in days. The schema is known, the API is documented, and the failure modes are constrained. Ask yourself: how to securely connect, retrieve, and process sensitive production data across multiple environments.
Guided Playbooks: “Collect X, summarize Y, suggest Z” workflows feel intelligent but are essentially deterministic scripts with LLM-generated response layered on top. Ask yourself: will this process still work if I replaced the entire demo scenario with a production outage?
These capabilities create compelling demos. The AI explains your logs clearly, suggests plausible root causes, and responds in seconds. Then you run it against a real incident with dozens of data sources, partial traces, and services outputting thousands of log lines per minute, and it all falls apart.
Why Production-Grade AI SRE Agent Is a Multi-Discipline Engineering Challenge
Demos hide complexity by design. Production exposes it. Here are the six engineering challenges that separate tools SREs actually trust from tools that get abandoned after the first real incident:
1. Data Acquisition and Normalization
Logs, metrics, traces, and events may have different schemas, cardinality traps, sampling behaviors, missing fields, inconsistent timestamps, and tooling quirks. Prometheus metrics use different label conventions than Datadog. CloudWatch log groups have different retention and query semantics than Splunk indexes. OpenTelemetry traces may be sampled at 1% in production. An LLM without proper guidance will correlate noise, not signal.
2. Context Selection
This is where most LLM-based approaches fundamentally break. A production Kubernetes cluster can generate millions of log lines per hour. One incident might span 15 services. The LLM’s context window, even at 128K tokens, cannot hold it all. Even if it could, simply passing in more context will not result in a more accurate response. The hard problem is picking the right slices of telemetry: tight time windows, precise list of resources involved in the incident, and correlating any external tools like GitHub that pushed a change to the relevant deployments.
3. Actionability and Safety
Generating remediation steps that are safe, scoped, and correct for a specific environment requires deep integration with change management, RBAC, and blast radius assessment. An AI that simply suggests generic actions like “restart the pod” or only summarizes a list of services in the deployment is worse than having no AI at all.
4. Measurable Evaluation
“It seems helpful” is not a metric. Production AI SRE must be evaluated on: precision of root cause analysis, number of avoided escalations, engineering time saved, and mean-time-to-resolve (MTTR) incidents. Avoid evaluating outputs that sound good on the surface like “there was a transient network issue”, but provide zero actionable value.
5. Enterprise Security, Governance, and Compliance Requirements
Enterprise deployments require: on-prem/VPC deployment options, secrets management, RBAC enforcement, audit trails, PII redaction, and guarantees that customer data never leaks into prompts. At scale, you need rate limiting, backpressure handling, intelligent caching, multi-tenancy isolation, and predictable cost models.
Key Metrics for Evaluating AI SRE Performance
“It seems helpful” is not a metric. Before committing to any AI SRE solution, establish baseline measurements and track these specific outcomes:
Accuracy Metrics
Metric
What to Measure
Industry Benchmark
Root Cause Precision
% of incidents where AI correctly identifies the actual root cause
>80% on historical incidents
False Positive Rate
% of AI suggestions that are incorrect or irrelevant
<15%
Citation Accuracy
% of AI claims that link to verifiable log lines, metrics, or traces
>95%
Efficiency Metrics
Metric
What to Measure
Industry Benchmark
MTTR Reduction
Time to resolution before vs. after AI SRE
40-70% reduction typical
Escalation Avoidance
% of incidents resolved without human escalation
30-50% for mature deployments
Time to First Insight
How quickly does AI surface actionable information?
<15 minutes from alert
Engineering Hours Saved
Hours reclaimed per on-call rotation
10-20 hours/week typical
Trust and Adoption Metrics
Metric
What to Measure
Why It Matters
Verification Rate
Can engineers independently verify every AI claim?
Trust requires transparency
Adoption Rate
% of on-call engineers actively using the tool
Low adoption = low value
Uncertainty Communication
Does the system admit when data is insufficient?
Prevents over-reliance
How to test during POC: Run the AI SRE against 10-20 historical incidents where you already know the root cause. Measure precision before trusting it with live incidents.
What a Production-Grade AI SRE Agent Actually Looks Like
Call us biased, but here is what a real production-grade AI SRE Agent actually looks like, after our team experienced the gap between demos and production firsthand. We focused on the hard engineering problems, telemetry normalization, context selection, and enterprise deployment, because we knew that’s where most AI SRE tools fail.
NeuBird’s solution is an integrated system with purpose-built components.
Component
Why It Matters
Telemetry Connectors
Native integration support for Datadog, Splunk, Prometheus, Grafana, CloudWatch, Azure Monitor, PagerDuty, ServiceNow, and more. Schema differences, pagination, rate limits, and sampling are handled automatically.
Entity Graph / Topology
Services, pods, nodes, deployments, owners and dependencies are mapped and maintained in a private database. This allows the agent to scope blast radius or trace causality across service boundaries.
Investigation Planner
Decides what to query next, how wide or narrow to search, when to drill down, and when to stop. This is where context engineering and iterative self-reasoning happens.
Query Compiler and Guardrails
Translates investigation plans into platform-specific queries, handles pagination, timeouts, and sampling. Uses structured data for deterministic queries and validates LLM-assisted outputs.
Signal Processing
Establishes baselines, detects anomalies, detects changes, and deduplicates signals.
Remediation Steps
Steps are grounded with citations and links. Every root cause analysis points to specific log lines, metrics, and traces that support it.
Feedback Loop
Continuous improvement based on environment specific failure modes and captures patterns over time without storing any sensitive data.
Quick Litmus Test: Can Your AI SRE Do This?
Before trusting any AI SRE solution in production, verify it can reliably perform these tasks:
From an alert, automatically identify the right service, right time window, right resources, and surface the top variance from baseline without manual context-setting.
Provide reproducible queries and direct links that an on-call engineer can click to verify every claim independently.
Avoid confident conclusions when data is missing, ambiguous, or insufficient. Clearly communicate uncertainty rather than hallucinating plausible-sounding explanations.
If the system can’t do these reliably, it will not solve production issues and your engineers will be more burdened by the tool than helped.
AI SRE Evaluation Checklist
Use this checklist when running a POC or comparing vendors.
Data & Integration
Supports your full observability stack
Handles schema normalization across different platforms
Works with sampled traces
Supports your cloud providers and ITSM tools
Context & Reasoning
Automatically identifies relevant services to investigate from the alert
Selects appropriate time windows without manual input
Provides reproducible queries you can run independently
Links conclusions to specific logs, metrics, traces, and events sources
Clearly communicates uncertainty when data is insufficient
Actionability & Safety
Remediation suggestions are scoped and specific
Blast radius assessment provided
Configurable trust boundaries (read-only → full automation)
Human approval workflow for high-risk actions
Enterprise Readiness
SOC 2 Type II certified
Deployment options match your requirements (SaaS/VPC/on-prem)
RBAC with granular permissions
Full audit trail for compliance
Data residency options if required
Vendor Validation
Customer references in similar industry/scale
Proven MTTR reduction metrics
Clear pricing model
Responsive support during POC
Roadmap alignment with your needs
The Bottom Line
Building “something like an AI SRE” is easy to prototype, but it will not survive the true test of production incidents. It will not be trusted by the engineers who have to rely on it to accelerate their mean time to resolve incidents at 3 in the morning. The NeuBird AI SRE Agent was built by our team of engineers who have lived through the gap between impressive demos and production reality. We skipped the shortcuts because we’ve seen where they lead.
Ready to see the difference? Contact us to request a technical deep-dive or just start your free trial.
Yesterday’s operational models are breaking under the weight of today’s business demands. Organizations are running more and more workloads across increasingly complex IT environments (with multiple telemetry sources across on-prem and cloud) with teams that haven’t grown proportionally to match this increased complexity. As expectations for always-on applications keep rising, teams simply don’t have the capacity to keep up.
Despite more tools, detecting and resolving issues still takes significant time as teams navigate fragmented signals from disparate logs, metrics, traces and change data. I’ve spent decades building and scaling infrastructure platforms and seen this pattern repeated in one enterprise environment after another.
The Pressure on Tech Leaders Is Building and Traditional SRE Workflows Can’t Keep Up
For business leaders tasked with protecting the top line, controlling costs to protect the bottom line and delivering great customer experience, reliability is no longer just an operations metric that lives on a spreadsheet. It now has an outsized impact on line-of-business outcomes and customer experience, 24/7/365. With downtime costing Global 2000 companies $400 billion a year¹, any tolerance for extended investigation and recovery times has effectively disappeared.
The heads of platform, engineering and infrastructure teams are expected to innovate faster and support critical app modernization and AI initiatives, while reducing firefighting time and maintaining reliability across increasingly complex IT stacks. Building in the right agentic workflows is key to accelerating time-to-market and staying competitive in the AI era.
The problem with modern SREOps and incident management is not missing data but the time required to correlate signals and reason over distributed systems under production pressure.
What SRE teams need is leverage, not more alerts. There is a definite need to cut toil, supercharge productivity, give engineers time back for innovation and meaningfully reduce the constant on-call fatigue. Autonomous incident resolution from NeuBird AI makes it possible for engineering, SRE and platform teams to shift their focus from reactive troubleshooting to proactive innovation.
Why Modern Infrastructure Requires a New Approach to SRE and Incident Management
I’ve personally experienced how digital transformation, cloud-native architectures and AI initiatives add layers of complexity that traditional SREOps and incident management workflows weren’t designed to handle.
Organizations routinely spend hundreds to thousands of hours diagnosing issues, assembling war rooms and manually correlating information that should be available at the start of every investigation. This is not sustainable and it’s not an effective use of highly skilled engineering talent.
As AI has been applied to SRE and incident management, solutions have largely converged around three approaches: automated alert triage and noise reduction, automated RCA, or and incident workflow automation. Each improves a specific part of the incident lifecycle, but none, on its own, fundamentally changes the core work of incident response, which remains largely manual. Humans still do the most tedious work: investigating, reasoning and connecting the dots under pressure.
Teams need a solution built in the AI era for the AI era.
In my prior role at Pure Storage, I saw the power of NeuBird AI in action and immediately had an epiphany. It felt like a “ChatGPT moment” for infrastructure and reliability engineering. NeuBird doesn’t just tackle one part of the incident management lifecycle, it holistically addresses the entire problem: reducing devops and SRE toil, boosting developer productivity and protecting the business from outage induced downtimes.
After having run large scale production infrastructure and working with customers who run global infrastructure at scale, I firmly believe that world class incident management and reliability engineering needs the trifecta of core capabilities as listed below.
Automated Alert Triage and Noise Reduction
Automated Root Cause Analysis
Incident Workflow Automation
Most existing solutions out there, unfortunately, only address one of the above and even that only partially. Let’s unpack this below.
Automated Alert Triage and Noise Reduction – Reducing alert fatigue is a necessary first step. Filtering non-actionable noise and clustering related alerts helps teams focus, but true incident resolution requires more than suppression. What’s needed is intelligent triage grounded in root cause understanding.
NeuBird delivers this comprehensively. It doesn’t simply surface information or silence alerts. It goes further by understanding the underlying root cause of failure. Alerts are intelligently grouped based on correlated signals and context across stacks, tools and clouds, helping teams resolve incidents quickly and eliminate repeat failures across on-prem and multi-cloud environments.
Automated Root Cause Analysis: Effective root cause analysis requires more than summarizing monitoring data or generating an initial hypothesis. Most existing solutions stop there. Comprehensive, actionable RCA demands cross-system reasoning and evidence-backed remediation steps.
In hybrid and multi-cloud environments, incidents rarely reside within a single tool or stack. Yet many approaches remain confined to a single cloud or a single monitoring ecosystem.
Here is where NeuBird starts to shine. Beyond automated alert triage delivering dramatic reduction in alert fatigue, NeuBird AI builds a working memory and context of the infrastructure, much like an expert SRE embedded in your SRE and Devops teams. It autonomously delivers evidence-backed RCA and remediation steps in real time by analyzing telemetry across disparate systems and vendors.
More importantly, it continuously learns: each incident strengthens NeuBird AI’s understanding of the environment, reducing toil and preventing repetitive work.
Incident Workflow Automation: Most existing solutions mostly attempt to automate the operational work of incidents, including ticket creation and status updates. However, they lack deep integrations with monitoring tools and telemetry sources across distributed IT stacks on-premises and across multi-cloud. Additionally, prior incident learnings are left buried in reports instead of contributing to institutional knowledge, leaving the underlying system unchanged and vulnerable to repeat failures..
NeuBird AI takes an end-to-end approach across incident workflows, closing the loop from investigation through resolution and post-incident learning.. It embeds directly into existing operations workflows to understand what happened and why. It then automatically generates context rich diagnoses, ticket updates and post-mortems based on real diagnostic context via integrations with other DevOps and observability tools like Datadog, PagerDuty, ServiceNow, Slack and GitHub and through agent-to-agent collaboration with Azure SRE Agent and Claude Code. Through this approach, NeuBird delivers workflow automation that is cross-system, context-aware, and production-ready.
The Bet I’m Making on NeuBird’s Agentic AI for SRE
As we can see, the NeuBird AI team has built something truly unique: an AI SRE agent built from scratch for modern infrastructure. As a customer, I experienced firsthand how NeuBird AI reduces toil, gives meaningful time back to engineering teams and delivers measurable, tangible ROI to the line of businesses.
Instead of starting from zero after every alert, engineers get early, evidence-backed understanding. Incidents that once required prolonged investigation and cross-functional war rooms are resolved faster and often autonomously with zero human intervention, averting outages and downtime. Seeing NeuBird AI’s impact as a customer made it clear this wasn’t just an incremental improvement, it is a fundamentally better approach to building, managing and running highly available enterprise infrastructure that is highly available with zero downtime
Having worked with Gou and Vinod in our prior company Portworx, and knowing their passion for making customer lives easy, I couldn’t resist the NeuBird opportunity. NeuBird solves multiple major pain points for enterprise infrastructure teams, serves a very large underserved market that is ripe for disruption, has built a mind blowing product that looks straight out of science fiction and has one of, if not, the best engineering team on the planet in this space.
I cannot wait to get this in the hands of our customers and partners!
The job of an AI SRE doesn’t end when the incident is mitigated, the alert quiets down, or the postmortem is published. That’s only the midpoint. The work isn’t complete until the system itself has learned from the failure and become structurally more resilient. This is the critical half of AI SRE: turning incidents into institutional learning.
Patterns uncovered during RCAs should inform future design reviews, infrastructure fixes should be encoded in shared Terraform modules so they propagate org-wide, and improvements made for one service should automatically benefit others. When reliability learnings are pushed into the platform, SRE moves from firefighting to a true force multiplier.
Just as important, organizations must be able to document and understand why a decision was made. Without that visibility, accuracy cannot be validated, accountability cannot be enforced, and autonomous behavior cannot safely scale.
The term context graphs has increasingly surfaced in industry discussions following an insightful Foundation Capital article describing them as a “living record of decision traces stitched across entities and time so precedent becomes searchable.” At NeuBird, we approach this from an SRE perspective and introduce a reasoning graph: an inspectable record of how evidence was evaluated, dependencies weighed, alternatives considered, and actions selected.
This makes the why behind every agent decision observable, so accuracy can be assessed, behavior refined, and autonomy operated with confidence.
The Reasoning Graph: Making “Why” Observable
NeuBird AI’s reasoning graph turns agent behavior from opaque execution into something teams can review, audit, and improve.
Let’s consider the following example of this in action. NeuBird’s AI SRE agent detected a CrashLoopBackOff alert on a production deployment. Within minutes, it analyzed evidence from kubectl, CloudWatch, and Prometheus, identified the root cause (OOMKilled due to insufficient memory), and recommended scaling from t3.medium to t3.large instances. The SRE team reviewed and approved the change. The agent executed via CI/CD. The alert is cleared. Incident resolved.
Two weeks later, the finance team notices a $600 spike in the AWS bill. This is where the reasoning graph transforms from an audit trail into institutional learning.
Act I: The Original Incident Analysis
The agent’s reasoning was methodical: memory usage consistently at 1.8GB against a 2GB limit, no gradual increase over 7 days.
Root Cause Analysis: Normal utilization with no headroom.
Implementation: The AI SRE agent files GitHub issue with the right context which includes the problem, RCA and recommended remediation. GitHub Copilot picks it up and submits a PR. A Human engineer approves the PR. The deployment is successful and alert cleared. NeuBird SRE AI confirms resolution
The complete reasoning graph is stored
Act II: The Discovery
Two weeks later, the team notices a $600 increase in their monthly AWS bill. Without a reasoning graph, this becomes detective work that would require searching through Slack threads, reviewing CloudWatch changes, checking PRs across multiple repos, interviewing engineers.
With a reasoning graph, a simple natural language query: “Show infrastructure changes in the last 2 weeks with cost impact.”, delivers the following analysis:
20 incidents found. Same pattern: OOM. Same RCA: Upgrade instance size. Total cost: $30 × 20 = $600/month.
The decision chain is immediately retrievable. Each technically sound, SRE approved, but no budget review required. The “why” is instantly observable..
The key insight: Each individual decision was correct from an SRE perspective. But accumulated without cost oversight, they created an unplanned budget impact.
Gap identified: Infrastructure changes made during the incident response process bypassed budget approval workflows.
Act III: Institutional Learning
The SRE agent’s work is truly complete only when this knowledge gets encoded allowing it to evolve.
The team creates a new rule: Infrastructure changes over $500/month require budget team approval.
But here’s what makes this different from a policy document gathering dust: The rule is encoded directly into the AI agent’s decision tree. Not as a suggestion. As a mandatory gate in the automated reasoning flow.
Act IV: The System Evolved
A month later, another service hits the same CrashLoopBackOff pattern. This time the agent is empowered with the decision tree to repeat the learnings.
The system adapted. The organization’s reliability posture improved and stayed within budget guardrails. The learning from one incident was encoded as operational knowledge for all future incidents.
Explainability as a Prerequisite for Accountable Autonomy
With reasoning graphs in place, incident response no longer ends at resolution. Each event feeds directly into organizational learning: incidents inform policy, recurring patterns become searchable precedent, and insights compound over time.
Explainability comes from explicit decision traces that make agent behavior transparent and inspectable. Accountability follows by enabling decisions to be reviewed, audited, and reused, allowing autonomy to scale. Together, these capabilities turn autonomous actions from isolated responses into durable system behavior while continuously improving reliability through institutionalized learning.
Over the past year, agentic AI has moved from skepticism and cautious experimentation to trusted use in production systems.
For SRE and DevOps teams, the question is no longer whether agents can help, but how they have evolved into a trusted partner. In 2025, we saw early but unmistakable signals of what was coming: scoped autonomy, early agent-to-agent collaboration, and AI-assisted triage crossing the boundary from demos into real operational workflows.
In 2026, the most consequential shift will not come from larger models or cleverer prompts. It will come from how agentic systems are structured and operated under real production pressure, especially in environments where correctness, latency, and trust outweigh creativity.
The following predictions outline the key shifts that will define how modern IT operates in 2026 and beyond.
1. Agent pipelines become a first-class DevOps pattern
In 2026, developers and SREs will routinely build agent pipelines: composable sequences of AI agents, each responsible for a specialized operational role. These pipelines will be described using declarative DSLs (domain-specific languages) – think Terraform – and compiled by an agent pipeline engine into optimized execution graphs.
The engine will resolve dependencies, enable parallel execution, cache intermediate results, and estimate cost and latency before execution. Engineers will version-control pipelines, share reusable modules across teams, and visualize exactly which models are invoked with what context at each stage.
For SRE teams, this redefines incident response as a first-class software system, not an operational afterthought.
2. Context Engine as a Service emerges as a core architectural layer
Context engineering has already emerged as the dividing line between DIY agents that only demo well and agents that survive in production. In 2026, Context Engine as a Service (CEaaS) will emerge as a core architectural layer across domains, underpinning enterprise agentic systems. It will sit above existing structured and unstructured data sources, deriving task-specific context from the underlying data.
The “context engine” for LLMs mirrors how databases evolved into standardized data layers for applications. Just as databases abstracted storage and exposed query interfaces (SQL, NoSQL APIs), CEaaS abstracts context management and exposes retrieval interfaces optimized for LLM input. As agentic systems grow in scope and complexity, centralizing context construction becomes increasingly important for consistency, reuse, and operational control.
By centralizing context construction, enterprises will make agent behavior more predictable, composable, tunable, and reusable across agents, workflows, and teams. By the end of 2026, CEaaS will become foundational to enterprise-grade agentic platforms, separating them from experimental systems.
3. Industry benchmarks for agentic AI will be built around real operational tradeoffs
Broader adoption of agentic AI will hinge on benchmarks that reflect real operational tradeoffs rather than isolated model performance.
These benchmarks will evaluate agentic systems against the three-axis problem of speed, quality, and cost—the core constraint of production agentic workflows. Improving diagnostic depth increases latency and cost, while optimizing for speed increases the risk of missed signals.
Evaluation will shift from model-centric scores to system-level outcomes grounded in production use cases. For IT and SRE operations this would include time to insight and mitigation, diagnostic correctness, false positives, and cost per investigation. Trust will be earned through consistent, verifiable system behavior under production constraints.
Looking ahead
2026 will be the year agentic AI systems earn operational trust and take responsibility for executing production workflows end to end.
This trust will be earned through reliable, context-aware execution that explicitly balances speed, diagnostic quality, and operational cost under real production constraints. As agents increasingly act as first responders and assume end-to-end workflows, this shift must be paired with appropriate guardrails, governance, and evaluation to ensure reliable behavior in production.
The shift is already underway. This year is about making this execution model the standard for reliable operational systems.
The SRE (Site Reliability Engineering) role is designed around one core objective: engineering reliable and scalable systems. In practice, many SRE teams spend a large percentage of their time doing something else entirely— responding to incidents.
Incident alerts pull engineers into extended triage loops: paging into Slack or PagerDuty, pivoting between dashboards, analyzing metrics, querying logs, scanning traces, and finding root cause of the issue. . Alerts lack context and the work is rarely linear. Multiple signals and symptoms compete for attention and incidents cascade obscuring observability. Reliability engineering, the proactive work that actually reduces future incidents, gets deprioritized in favor of keeping production running.
As an SRE, Antoni saw this pattern repeat consistently. Pages arrived late at night, often triggered by noisy thresholds rather than true user impact. An alert in one system would cascade into activity across several others. Engineers responded to pages and incidents, spending hours in investigation—manually stitching together metrics, logs, and traces across siloed tools to infer the root cause.
Hours later, the immediate symptoms would be mitigated. Traffic stabilized. Latency dropped. Alerts cleared. Often, the underlying cause was often only partially understood. By morning, the incident was marked “resolved,” even though the system behavior that caused it remained. The next incident rarely looked identical, but it rhymed closely enough to feel familiar.
That wasn’t an edge case. It was the operating model.
How Incident Response Became a Grind
These patterns repeat across SRE teams operating modern production systems.
Firefighting as Toil
The Google SRE Book gives precise language to this problem through its definition of toil. In SRE, the goal is to maximize time spent on long-term engineering work that improves reliability over time. To avoid ambiguity, the book defines toil as work that is:
Manual and repetitive
Interrupt-driven and reactive
Required to keep the system running
Scales with system growth but does not produce lasting reliability gains
Crucially, the SRE Book identifies interrupts as the largest source of toil. These include non-urgent alerts, messages, and service notifications that fragment attention and break sustained engineering focus. The next major contributor is on-call (urgent) response, where frequent paging and incomplete context force engineers into reactive troubleshooting, even for transient or low-severity issues.
This framing maps closely to how modern SRE teams experience incident response. Alert noise and fragmented investigation workflows consume cognitive bandwidth, leaving little room for preventative work. The result is a reinforcing loop: more incidents drive more interrupts, which reduces the time available to fix systemic issues, which in turn leads to more incidents.
Reducing interrupt-driven investigation and on-call load, the primary sources of toil identified by the Google SRE Book, is exactly the problem Hawkeye is designed to address.
Tool Sprawl and the Hidden RCA Tax
Operational signals are spread across logs, metrics, traces, events, and change data, often in separate systems. Incident state lives in tickets, while decisions unfold in chat. Context is fragmented across tools.
As a result, root cause analysis becomes a manual correlation exercise. Engineers align timestamps, reconcile conflicting signals, and infer causality under pressure. Each incident requires context to be reassembled before meaningful investigation can begin.
War Rooms Reflect Distributed Context
During an incident, multiple domain experts are often brought in to support investigation.
Operational context is distributed across runbooks, knowledge bases, prior incidents, dashboards, and individual experience. Bringing people together becomes a way to assemble that context in real time. Investigation is slowed not by lack of data, but by the effort required to align and reason over information that lives in different places.
What if that context could be captured once and made available at the start of every investigation, rather than reassembled under pressure each time?
Reporting and Postmortems Consume Disproportionate Time
Incident resolution rarely ends when alerts clear. Antoni recalls incidents that took about 30 minutes to mitigate, followed by hours spent writing the postmortem. The failure mode was understood early, but producing an accurate report required revisiting metrics and logs to extract evidence, reconstruct timelines, and document triggering and contributing events.
In practice, the time required often outweighs the engineering value it produces. Reporting becomes a manual reconstruction of pre-mitigation investigation steps and system behavior.
Turning Lessons Into Action
After years of seeing these patterns repeat, one conclusion became clear: adding more dashboards would not fix incident response.
The issue was not data availability, but how context was assembled, how investigation unfolded under production pressure, and how outcomes were captured afterward. These steps were fragmented, manual, and repeated across incidents.
Addressing that required rethinking the incident response process itself. That realization directly shaped how Hawkeye was built.
Building Hawkeye: Reducing Toil at the Source
Hawkeye is designed to eliminate the most repetitive and interrupt-driven parts of incident response, especially the manual effort of assembling context, coordinating expertise, and reconstructing investigations after the fact.
Technically, Hawkeye operates as an AI SRE that autonomously picks up alerts and reasons over telemetry in real time. It ingests signals across logs, metrics, traces, events, and change data; correlates them across services and dependencies; and maintains an evolving model of what is most likely happening and why.
Instead of engineers starting investigations from zero after a page fires, Hawkeye provides:
Automated investigation triggered as soon as an incident is detected
Early surfacing of likely root causes and remediation steps, backed by correlated evidence
Cross-domain reasoning across on-prem, hybrid, and multi-cloud environments
Native integrations with tools teams already use; like Datadog, PagerDuty, Splunk, ServiceNow, and CloudWatch
Flexible, secure deployment, running in-VPC or as SaaS
The impact is not just faster mean time to resolution (MTTR), an important indicator of incident response effectiveness. Fewer interrupts reach humans. Engineers spend less time stitching context together. Investigation becomes structured rather than exploratory under pressure.
From Firefighting Back to Engineering Reliability
What becomes noticeable when the grind eases is how teams behave differently. With repetitive investigation and triage handled automatically, engineers start spending more time on long term reliability and design projects . On-call becomes quieter and more predictable. Post-incident reviews result in concrete fixes instead of rushed summaries.
This is toil reduction in practice. Not removing humans from operations, but removing the work that prevents them from doing what SREs were meant to do in the first place: engineer systems that fail less often and recover quickly.
Hawkeye was built by SREs who spent years on call, dealing with alert noise, fragmented context, and investigations that restarted from zero. The goal is simple: help SREs spend less time firefighting and more time engineering reliability.
For years, enterprise IT teams have lived with an uncomfortable truth: incident resolution still takes too long, and root-cause analysis remains too manual.
Even with strong observability, detailed logs, and mature cloud monitoring, the most time-consuming part of operations hasn’t changed — diagnosing issues and uncovering the true root cause. Engineers lose hours correlating signals, combing through dashboards and logs, and validating assumptions under pressure. Small delays often cascade into repeat incidents or even outages.
I’m excited to share the self-service free trial of Hawkeye, your 24×7 on-call AI SRE. Teams can now add agentic-AI-powered incident resolution into their existing IT workflows and go from sign-up to their first autonomous investigation in minutes.
This launch marks an important milestone: making autonomous root-cause analysis and resolution accessible to teams without lengthy onboarding, heavy integration cycles, or upfront cost.
Why Self-Service Matters for Modern IT
In modern systems, seconds matter — not just to uptime, but to customer experience, developer velocity, and business continuity.
Yet most incident workflows still depend on human-driven triage:
Dashboards full of signals but few real answers
Separate teams investigating different layers
Manual correlation that drags from minutes into hours
Recurring incidents traced to symptoms rather than root causes
As I’ve seen across global businesses, the issue isn’t a lack of tools or telemetry. It’s the gap between visibility and action.
That’s the gap Hawkeye was built to close.
With this new self-service experience, AWS customers can onboard Hawkeye into their environment in minutes and immediately begin receiving real-time diagnosis, root-cause analysis, and targeted fixes.
Hawkeye becomes your expert SRE teammate: always on, always reasoning, and always working within the security perimeter you control.
Instant Root-Cause Analysis
Connecting fragmented telemetry, surfacing the underlying issue, and reducing MTTR by up to 90%.
Cutting Through the Noise with Context Engineering
Reasoning across logs, metrics, traces, and infrastructure signals to identify the most probable cause.
Actionable, Targeted Remediation
Guiding teams to the exact fix, backed by domain expertise and your operational context.
24×7 Autonomous Investigation
Diagnosing issues at any hour — without waiting for a human to start the investigation.
This isn’t AI summarizing data. It’s an agentic system investigating incidents end to end – helping engineers and SREs get to answers faster and reduce the time spent in troubleshooting.
Why Self-Service is Core to Modern IT
Enterprise AI systems traditionally require heavy configuration, extended integration, or upfront investment.
We wanted to remove all of that friction.
With today’s launch:
Any AWS team can get started in minutes
The free trial runs unlimited for 14 days
There are zero fixed subscription fees afterward
This is how teams should experience agentic AI: immediate impact on Day 1.
DeepHealth is a great example. Their team onboarded rapidly and saw immediate value. As their VP of Cloud, Technology and Product, Madhu Jahagirdar, shared:
“Our team was able to get up and running with Hawkeye by NeuBird rapidly. It’s like having an always-on AI SRE that delivers real-time incident diagnosis and actionable fixes 24×7 — saving our engineers hours of troubleshooting and improving service quality for our customers.”
This pattern is becoming common: faster investigations, fewer escalations, and engineers freed to focus on building instead of firefighting.
The Next Phase of Autonomous IT
The shift is already underway:
From manual triage to autonomous investigation
From alert floods to precise resolution steps
From firefighting to engineering momentum
Hawkeye’s self-service trial accelerates this shift by removing barriers and letting teams see the value firsthand.
Start the Free Trial
Get started with Hawkeye today and go from sign-up to first autonomous investigation in minutes. Deploy it. Trigger an investigation. Watch it work in real time.
The future of incident response is autonomous – and now any AWS team can experience it within minutes.
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