Building Trust with AI Agents in Site Reliability Engineering

Engineering teams are increasingly deploying AI agents to manage production operations. These agents can build a deep understanding of your systems, investigate complex issues, and drive incidents to resolution. This article explores how AI agents work, their current capabilities and limitations, and how building trust with these systems will fundamentally change how Site Reliability Engineering (SRE) teams operate.

The Breaking Point for Modern Operations

Every engineering team with production services faces the same challenge: a relentless stream of operational work. Each service added to a system multiplies the potential failure modes, creating a constant flow of alerts and incidents. Engineers often spend their days switching context between building software and firefighting, delaying critical development work.

Many teams have accepted reactive triage as the norm. Low-priority alerts are shelved while critical issues get immediate attention. Over time, engineers learn to ignore the background noise of alerts until they boil over. Minor problems accumulate until they trigger a major, cascading incident, forcing an on-call engineer to resolve a customer-facing outage at 3 AM. This reactive approach works, until it doesn't.

Traditional solutions haven't kept pace. Adding more engineers introduces communication overhead that grows exponentially, quickly negating any linear gains in capacity. Adding more site reliability engineering tools creates sprawl and complexity. Custom scripts break when systems change, and automation requires constant maintenance. The result is a cycle of burnout, where engineers spend hours investigating preventable issues while product development stalls.

AI Agents: A New Paradigm for SRE

Large Language Models (LLMs), when deployed as autonomous agents, enable a new approach to operations. Unlike copilots that require constant human guidance, these AI agents can make independent decisions about which tools to use and when—querying Datadog metrics, checking alerts, or running kubectl commands. They can process thousands of signals at once, maintain context across interactions, and learn from every incident.

An AI agent connects to your production environment through existing APIs and permissions, building system understanding from documentation, metrics, logs, and alerts. Platforms like Rootly are at the forefront of this shift, offering an AI-native incident response platform designed to help teams resolve incidents faster and improve system resilience. Unlike static runbooks, these agents can reason through novel situations they haven’t encountered before.

These agents excel at investigation and diagnosis. They analyze system metrics, logs, and traces, presenting not just their findings but also the evidence chain that led to their conclusions. While an agent might misinterpret a correlation or lack complete system context, it consistently narrows the search space by surfacing relevant patterns and potential causes, all while engineers retain full control over its actions.

An AI Agent at Work: When Minor Alerts Matter

Often, a low-severity alert contains the early warning signs of an impending incident. An AI agent can process these subtle signals to prevent cascading failures. Here’s a common example.

At 3 AM, a Redis latency alert triggers in a recommendation service. The average command latency is 2.3ms, up from a baseline of 0.8ms, and the memory fragmentation ratio is 1.89. The alert is marked P3—low priority. The on-call engineer, busy with a deployment issue, queues it for morning review. After all, the metrics are well below crisis thresholds, and similar alerts have resolved on their own in the past.

The AI agent sees something different. It knows from an architecture review document that the recommendation service also handles session management. It also knows that morning traffic will bring a 30x increase in requests. The current memory pressure signals a potential cascade failure during peak hours.

The agent rapidly expands its investigation. It finds subtle memory pressure in related services: the cart service queue depth is growing 15% hour-over-hour, and the session service error rate is creeping up by 0.01% every 30 minutes. A backup process, scheduled during "low traffic" hours, is competing for resources.

Individually, each metric seems minor. Together, they reveal an imminent system-wide failure:

• Growing memory pressure will trigger Redis evictions.
• Session data loss will force users to re-authenticate.
• Shopping cart operations will begin to time out.
• Product recommendations will degrade.
• Customer experience will suffer, and recovery will require multiple service restarts.

Without intervention, the engineering team would arrive to multiple active incidents, customer complaints, and immense pressure to fix everything at once. Instead, with high confidence in its analysis, the AI agent escalates the issue with a detailed summary, providing clear evidence and a low-risk remediation path.

The on-call engineer, presented with a clear diagnosis, merges the fix. By morning:

• Memory usage has stabilized.
• Queue depths are normal.
• Error rates are back to baseline.
• There is no customer impact.
• The failure pattern is captured and shared.

This case demonstrates the value of automated prevention. The immediate win was avoiding an outage, but the lasting benefit is capturing this failure pattern to prevent future incidents, freeing up engineering time for development.

Core Capabilities of a Trusted AI Agent

The ability to prevent incidents like the one above relies on four core capabilities: building operational knowledge, awareness, investigation, and resolution.

Building Operational Knowledge

Operational knowledge allows an AI agent to quickly diagnose issues by understanding how systems interact. The agent builds a knowledge graph from multiple sources: system queries, infrastructure-as-code files, monitoring data, documentation, and team communications in platforms like Slack. This graph captures service relationships and operational state, while an LLM infers additional connections from unstructured data.

For example, the agent might scan a Kubernetes cluster and identify a deployment manifest:

apiVersion: apps/v1
kind: Deployment
metadata:
 name: recommendation-service
spec:
 replicas: 3
 template:
   spec:
     containers:
     - name: server
       image: my-repo/recommendation-service:v1.2.3
       ports:
       - containerPort: 8080
       env:
       - name: REDIS_HOST
         value: "redis-cache.internal"
       - name: ML_MODEL_PATH
         value: "/models/reco-v4.2"
       resources:
         limits:
           memory: "4Gi"
           cpu: "1000m"



From this file alone, the agent learns the service's Redis dependency, its machine learning model requirements, and its resource constraints. It then enriches this knowledge with data from API calls on usage patterns and performance characteristics. This knowledge building is crucial because modern systems are deeply interconnected, and the graph provides the structure needed to reason about system behavior and guide investigations effectively.

Awareness and Automated Triage

Engineers are flooded with operational noise—alerts, tickets, deployment notifications, and support questions. An AI agent integrates with the entire operational stack to filter this stream, detecting which signals require attention. The goal is simple: take action when needed and stay quiet when not.

The agent processes signals across the environment, combining them to understand their potential impact. A developer's question about Redis in Slack might add context to a minor latency spike. A support ticket could connect seemingly unrelated errors. Over time, the agent learns which combinations of signals warrant attention.

This autonomous triage allows engineers to step back from the constant noise. Instead of reviewing every alert, they can focus on building more resilient systems and addressing structural problems, confident that the AI agent will surface the issues that truly matter.

Automated Investigation

An AI agent investigates like an engineer but operates concurrently across many paths. When an issue arises, the agent draws on its awareness of the environment—recent deployments, team discussions, and past incidents. Using its knowledge graph, it identifies which systems and dependencies could be involved.

The agent generates multiple hypotheses about potential root causes and tests them in parallel using the same site reliability engineering tools engineers use, like querying metrics or checking logs. The Rootly AI SRE, for example, analyzes code, telemetry, and past incidents to quickly identify probable root causes, presenting its findings with transparent reasoning.

Each finding builds confidence in a particular path. The agent documents every step: commands run, data collected, and paths explored. Even if the agent doesn't find the exact cause, its iterative process significantly reduces the search space for engineers, turning hours of investigation into minutes.

Guided Resolution

Resolution turns investigation findings into concrete changes, such as updating a Kubernetes configuration or a service parameter. The goal is to close the loop from detection to fix with minimal human intervention, but this requires earning trust through consistently successful suggestions.

Agents operate under environment-specific rules that determine their level of autonomy:

• Development: Can auto-implement previously approved changes.
• Staging: Can adjust resource limits with team lead approval.
• Production: All changes require engineering review and explicit approval.

Platforms like Rootly facilitate this by integrating directly into an engineering team's workflow, automating the creation of Slack channels or Jira tickets for approved actions to ensure communication and tracking are seamless. Successfully merged changes become part of the agent's knowledge base, not as rigid scripts but as proven approaches to consider for future issues. Teams typically start by giving an agent read-only access in production, gradually expanding its capabilities as it demonstrates a track record of useful suggestions.

The Path to Autonomous Operations

Production systems have grown beyond human scale. The complexity and speed of modern infrastructure mean engineers can't keep up with operational demands using traditional methods.

AI agents work alongside engineers to investigate and solve problems, reducing the time spent on operational tasks from hours to minutes. They build a deep understanding of your systems with each interaction, accumulating knowledge that is typically scattered across wikis, tickets, and chat logs.

The path forward is through progressive trust-building. Teams start with specific subsystems, expand the agent's scope as it proves reliable, and gradually increase its autonomy. The future of these site reliability engineering tools lies in seamless integration. Rootly has designed its platform with an API-first, AI-agent-first approach, allowing AI agents to interact directly with its systems to perform complex, autonomous incident management tasks.

While fully self-healing systems remain a distant goal, the path is clear. Teams will progressively delegate more operational responsibility to AI agents, allowing engineers to focus on what they do best: building better products.

Ready to reduce operational overhead and accelerate incident resolution? Book a demo to see how Rootly's AI-native incident management platform can empower your team.

Meta Description

Discover how AI agents in Site Reliability Engineering build trust, automate incident resolution, and prevent outages, transforming operations for efficient SRE teams.

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