OpenSRE Deep Dive: Build and Deploy Production-Grade AI SRE Agents from Scratch

A hands-on guide to designing, training, and deploying autonomous AI SRE agents

Table of Contents

• What We're Building: Autonomous AI SRE Agents in the Physical AI Stack™
• Prerequisites: Toolchain, Infrastructure, and Compliance Checklist
• Step 1: Bootstrapping Your OpenSRE Environment
• Step 2: Integrating Observability Tools (SENSE Layer)
• Step 3: Building the Reasoning Engine (REASON Layer)
• Step 4: Implementing Automated Remediation (ACT Layer)
• Advanced Configuration: Custom Agents and Knowledge Bases
• Testing & Validation: From Unit Tests to Chaos Engineering
• Error Handling & Debugging: Production War Stories
• Production Hardening: Security, Scaling, and Compliance
• Monitoring & Observability: Metrics, Logs, and Alerts for OpenSRE Agents
• Cost & Performance Optimization: Cloud vs. On-Premises Trade-offs

What We're Building: Autonomous AI SRE Agents in the Physical AI Stack™

The Autonomous SRE Imperative

In 2026, the average enterprise cloud environment generates 1.2 million observability events per hour—a 300% increase from 2022 Best Open Source AI SRE Tools in 2026 - IncidentFox. Human SRE teams, even at scale, can only investigate 0.1% of these events before alert fatigue sets in, leading to missed incidents and prolonged outages. The economic impact is staggering: $5,600 per minute for critical application downtime in financial services, with MTTR (Mean Time to Resolution) averaging 4.2 hours for complex distributed systems.

OpenSRE emerges as the first open-source, self-hosted framework designed to close this gap by deploying autonomous AI SRE agents that operate within the Physical AI Stack™. These agents don’t just alert—they investigate, diagnose, and remediate production incidents in real-time GitHub - Tracer-Cloud/opensre.

Architecture: OpenSRE in the Physical AI Stack™

OpenSRE is not a standalone tool—it’s a full-stack integration framework that maps directly to the Physical AI Stack™, enabling end-to-end autonomy from sensor to actuation. Below is the reference architecture, with each layer annotated to its Physical AI Stack™ counterpart:

Key Integration Points by Layer:

End-to-End Incident Response: AWS RDS Failover Walkthrough

Let’s examine a production-grade incident resolved by an OpenSRE agent: an AWS RDS primary instance failure in a multi-AZ deployment. This scenario is derived from real-world outages at scale.

Step 1: SENSE – Alert Ingestion

• Trigger: Grafana alert fires on aws_rds_cpu_utilization > 95% for 10 minutes.
• OpenSRE Action: Agent receives alert via webhook and creates an Incident Record in PostgreSQL with:

  {
    "incident_id": "inc-20260515-1432-aws-rds",
    "status": "investigating",
    "severity": "critical",
    "context": {
      "alert_source": "grafana",
      "metric": "aws_rds_cpu_utilization",
      "threshold": 95,
      "instance_id": "db-prod-primary-1a"
    }
  }
  

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• Failure Mode: If the alert lacks instance_id, the agent falls back to querying CloudWatch for all RDS instances in the region, adding 3–8 seconds of latency.

Step 2: CONNECT – Cross-Service Investigation

• Agent Query: Uses AWS CLI to fetch:

  aws rds describe-db-instances --db-instance-identifier db-prod-primary-1a
  

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  Output:

  {
    "DBInstances": [{
      "DBInstanceStatus": "failed",
      "MultiAZ": true,
      "ReadReplicaDBInstanceIdentifiers": ["db-prod-replica-1b"]
    }]
  }
  

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• Tool Integration: Agent also queries Datadog for correlated metrics (e.g., disk_queue_depth, replica_lag) via REST API.
• Failure Mode: If the AWS CLI call fails (e.g., due to IAM permissions), the agent retries with exponential backoff (max 3 attempts) before escalating to a human.

Step 3: COMPUTE – Local LLM Inference

• Prompt: The agent constructs a ReAct-style prompt for the local LLM (e.g., Llama3-8B):

  You are an AI SRE agent investigating an AWS RDS failure.
  Observations:
  1. Primary instance db-prod-primary-1a is in "failed" state.
  2. Multi-AZ deployment with replica db-prod-replica-1b.
  3. CPU utilization spiked to 98% before failure.
  4. Replica lag is 0ms (healthy).
  
  Tools available:
  - aws_rds_promote_replica: Promotes a read replica to primary.
  - slack_notify: Sends message to #prod-alerts.
  - github_create_pr: Opens a PR to roll back the last deployment.
  
  Question: What is the root cause, and what remediation steps should be taken?
  

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• LLM Output: The model generates a thought-action-observation loop:

  Thought: The primary instance is failed, but the replica is healthy. This suggests a hardware or AZ failure. The CPU spike may be a symptom, not the root cause.
  Action: aws_rds_promote_replica db-prod-replica-1b
  Observation: Replica promotion initiated. New primary: db-prod-replica-1b.
  Thought: Promotion successful. Next, I should notify the team and check if the last deployment triggered the failure.
  Action: github_get_last_deployment
  Observation: Last deployment was 12 minutes before failure (commit: abc123).
  Action: slack_notify "Promoted db-prod-replica-1b to primary. Investigating commit abc123 as potential root cause."
  

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• Failure Mode: If the LLM hallucinates a non-existent tool (e.g., aws_rds_reboot), the agent logs the error and falls back to a predefined runbook for RDS failures.

Step 4: REASON – Root Cause Analysis

• Agent Hypothesis: The CPU spike was caused by a recent GitHub deployment (commit abc123).
• Verification: Agent queries GitHub API for the deployment’s SQL changes:

  gh api repos/{owner}/{repo}/commits/abc123 --jq '.files[].filename'
  

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  Output:

  migrations/20260515_add_index.sql
  

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• Analysis: The migration added a full-table scan index on a 500GB table, causing the CPU spike.

Step 5: ACT – Automated Remediation

• Action 1: Promote the replica (already done in Step 3).
• Action 2: Open a GitHub PR to revert the migration:

  gh pr create --title "Revert: Add index on users.email" --body "This index caused CPU spike on db-prod-primary-1a. See incident inc-20260515-1432." --base main --head revert-abc123
  

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• Action 3: Update the incident status in PostgreSQL:

  UPDATE incidents SET status = 'resolved', resolution = 'Promoted replica and reverted commit abc123' WHERE incident_id = 'inc-20260515-1432';
  

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Step 6: ORCHESTRATE – Incident Lifecycle Management

• State Machine: The agent transitions the incident through states:
  Loading diagram...

stateDiagram-v2 [] --> investigating investigating --> remediating: Root cause identified remediating --> verifying: Actions executed verifying --> resolved: Validation passed verifying --> failed: Remediation error failed --> investigating: Retry or escalate resolved --> []

- **Audit Trail**: Every action is logged to PostgreSQL with:
  - Timestamp
  - Tool