How to build an AI SRE agent that solves production incidents like a team of engineers

Site Reliability Engineers (SREs) are constantly flooded with alerts in large-scale, distributed environments, where every service, API, and infrastructure layer can fail. As outages span multiple services and generate increasingly complex telemetry, pinpointing root causes has become significantly more difficult.

To address this, many enterprises have turned to AI agents to automate incident management, aiming to reduce alert fatigue and shorten mean time to resolution. In practice, however, these systems often struggle to cut through the noise. They tend to summarise alerts rather than properly identifying the root cause, leaving engineers to do the real investigative work.

The next step is to build AI SRE agents that think more like human engineers: capable of forming hypotheses, testing them against telemetry, and following evidence to uncover true root causes. Achieving this requires benchmarking against real incidents, ensuring agents can adapt to the complexity and unpredictability of production environments. Only then can AI move from a simple automation tool to a trusted teammate in incident response.

From summarisation to investigation

Traditional monitoring systems excel at detecting incidents but often fail to provide meaningful insight. When multiple alerts trigger simultaneously, the key question is not just what happened but why? AI SRE agents should replicate the investigative workflow of human engineers: forming hypotheses, testing them, and refining their understanding until the underlying cause is clear.

Modern observability platforms make this possible. Agentic AI can ingest telemetry data across various services, investigate incidents, and recommend or even implement remediation steps. By capturing each stage of reasoning (from task planning and tool invocation to data retrieval and decision-making) organisations can transform AI from a black box into a transparent, accountable participant in incident management.

  Ground agents in real incidents (not synthetic tests)

A common mistake in AI operations tooling is evaluating agents only against simplified or synthetic scenarios. Real-world systems fail unpredictably: degradations may occur gradually, cascading failures can emerge, and noisy metrics often obscure the true root causes.

Benchmarking against actual production incidents is therefore essential. Observing how AI responds to real outages allows teams to refine reasoning paths, reduce false positives, and strengthen

root-cause analysis. This creates a continuous improvement loop, where each investigation improves the agent's ability to tackle future incidents.

Access to large-scale, real-world telemetry provides another advantage. Real-world data from production environments enables AI SRE agents to recognise meaningful patterns, trace causal relationships and filter out irrelevant signals (capabilities that are difficult to develop using hypothetical scenarios alone).

  Following causality, not noise

Distinguishing correlation from occurence is a key skill for SREs, and the same principle applies for AI agents. Alerts and anomalies rarely exist in isolation; they propagate through dependencies across services and infrastructure. Effective AI must trace these signals along their relationships to uncover root causes, rather than treating each alert as a separate issue.

Agents that can correlate telemetry across infrastructure, service chains, and applications gain a holistic view of incidents. This enables them to filter out irrelevant signals, focus on meaningful causal relationships, and carry out more insightful investigations.

Causality-driven reasoning depends on access to high-quality, real-world production telemetry. Observability must extend beyond uptime and latency to include metrics such as model accuracy, data integrity, and operational behaviour. With this broader context, AI can detect complex failure modes (including those introduced by AI-driven workloads) without being misled by superficial signals.

As agents learn from real incidents and adapt to changing conditions, they evolve from reactive responders into proactive problem solvers. The result is a self-reinforcing cycle in which exposure to production data sharpens reasoning, speeds investigations, and improves resilience at scale.

From tool to teammate

The ultimate goal is not to replace engineers but to amplify their expertise. Well-designed agents can streamline investigations, highlight likely root causes, and confidently recommend (or even execute) remediation steps.

When grounded in real telemetry, benchmarked against actual incidents, and designed to prioritise causality over noise, AI becomes more than a monitoring tool. It becomes a trusted teammate that investigates, reasons, and learns alongside the engineers it supports.

In high-scale environments, minutes of diagnostic delay translate directly into revenue loss and customer impact. As digital estates grow, organisations that treat AI as an investigative partner rather than a passive summarisation tool will excel in operational resilience.

By combining telemetry, experimentation, and structured reasoning, these organisations can move from reactive firefighting to proactive incident prevention. The result is a future where outages are minimised, reliability is maximised, and engineers can focus on strategic challenges rather than firefighting alerts.