Lessons from the Grind: An SRE’s Journey to Reinvent SRE Ops with AI

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.