Agent in a Box

Autonomous On-Call Incident Remediation Agent

engineering

Autonomous On-Call Incident Remediation Agent: Reducing MTTR with AI

Problem Statement

Modern engineering teams at startups face "alert fatigue," where the signal-to-noise ratio in monitoring tools like Datadog or Sentry is dangerously low. When a critical production incident occurs at 3:00 AM, the primary responder (often a high-cost Senior Engineer) spends the first 30-45 minutes performing repetitive "triage hygiene": fetching logs, checking recent deployment diffs, querying database health, and searching Slack for similar past incidents. This manual discovery phase significantly inflates Mean Time to Resolution (MTTR) and causes rapid burnout among on-call engineers.

The problem is exacerbated by the "context gap." Documentation in Notion or Wikis is often outdated, and the knowledge of how to fix a specific microservice failure exists only in the head of the person who wrote the code. For a startup, a 60-minute outage can result in thousands of dollars in lost revenue and permanent damage to brand trust. Existing "auto-remediation" tools are often too rigid, relying on hard-coded scripts that break as the infrastructure evolves. There is a desperate need for an AI agent that can reason through an incident in real-time, gather the necessary context from disparate tools, and present a verified remediation plan—or execute safe, predefined recovery actions—before the human engineer even opens their laptop. This solution complements other efficiency tools like an Automated API Documentation & SDK Generator Agent by ensuring the infrastructure remains as reliable as the code.

What the Agent Does/Doesn't Do

  • Does: Monitors alert channels (Slack/PagerDuty), queries observability platforms (logs/metrics), analyzes recent GitHub commits, cross-references internal documentation, and generates a "Situation Room" briefing with a root cause hypothesis and suggested fix.
  • Does: Executes "Safe-Path" remediations (e.g., restarting a non-critical service pod, clearing a Redis cache, or rolling back a specific deployment) if authorized.
  • Doesn't: Make architectural changes, delete production databases, or modify IAM permissions.
  • Doesn't: Replace the human engineer; it acts as a "Force Multiplier" to handle the first 30 minutes of investigation, much like how an Autonomous Cloud FinOps Agent manages infrastructure costs.

Workflow

  1. Alert Ingestion: Agent triggers on a PagerDuty webhook or High-Severity Slack alert.
    • Input: JSON Alert Payload (Service ID, Error Message, Timestamp).
    • Output: Initialized Incident Workspace.
  2. Context Harvesting: Agent queries Datadog/New Relic for related metrics and GitHub for deployments within the last 30 minutes.
    • Input: Service Name.
    • Output: Log snippets and Diff links.
  3. Knowledge Base Lookup: Agent searches Pinecone/Vector DB for similar past incidents and internal Runbooks.
    • Input: Error Trace.
    • Output: Top 3 related historical resolutions.
  4. Reasoning & Hypothesis: Agent uses an LLM to correlate logs, diffs, and docs to identify the "likely culprit."
    • Input: Harvested Context.
    • Output: Root Cause Analysis (RCA) draft.
  5. Remediation Proposal: Agent generates a CLI command or Script to fix the issue.
    • Input: RCA Draft.
    • Output: Executable "Plan of Action" sent to Slack.
  6. Human-in-the-loop Execution: If the engineer clicks "Approve," the agent executes the command via PagerDuty Runbook Automation.
    • Input: Approval Signal.
    • Output: Success/Failure confirmation.

Tool Stack

  • Claude 3.5 Sonnet - Primary reasoning engine for log analysis and RCA.
    • Pricing: $3.00/1M input tokens | $15.00/1M output tokens (Pricing) ✓ Verified 2025-01-09
    • Documentation
  • LangGraph - Orchestration framework for stateful, multi-step investigation loops.
    • Pricing: Free (Open Source) or LangSmith integration tiers (Pricing) ✓ Verified 2025-01-09
    • Documentation
  • Sentry - Error tracking and performance monitoring.
  • Slack - Incident communication and approval interface.
  • PagerDuty Runbook Automation - Secure execution of remediation scripts.
    • Pricing: Contact Sales for Enterprise; Automation Actions often bundled (Pricing) ✓ Verified 2025-01-09
    • Documentation
  • Pinecone - Vector database for storing and retrieving past incident retrospectives.
    • Pricing: Standard: $0.01/hr per pod | Serverless: Usage-based (Pricing) ✓ Verified 2025-01-09
    • Documentation
  • Datadog [Unverified] - Observability and metrics.
  • GitHub [Unverified] - Version control and deployment tracking.

Quick Integration

Sentry: Fetching Incident Context

import requests

# Configuration
SENTRY_API_TOKEN = 'YOUR_SENTRY_AUTH_TOKEN'
ORGANIZATION_SLUG = 'your-org-slug'
PROJECT_SLUG = 'your-project-slug'

url = f"https://sentry.io/api/0/projects/{ORGANIZATION_SLUG}/{PROJECT_SLUG}/issues/"
headers = {"Authorization": f"Bearer {SENTRY_API_TOKEN}"}
params = {"query": "is:unresolved", "limit": 5}

response = requests.get(url, headers=headers, params=params)
issues = response.json()
# Extract issue titles and permalinks for LLM context

Source: Sentry API Docs

Slack: Sending Remediation Approval

from slack_sdk import WebClient

client = WebClient(token="xoxb-your-bot-token")
response = client.chat_postMessage(
    channel="#ops-incidents",
    text="🚨 *Incident Detected*",
    blocks=[
        {"type": "section", "text": {"type": "mrkdwn", "text": "*Proposed Fix:* Restart Redis Pod"}},
        {"type": "actions", "elements": [
            {"type": "button", "text": {"type": "plain_text", "text": "Approve"}, "style": "primary", "value": "approve_fix"},
            {"type": "button", "text": {"type": "plain_text", "text": "Deny"}, "style": "danger", "value": "deny_fix"}
        ]}
    ]
)

Source: Slack API Methods

Prompt Skeletons

### System: Incident Commander Prompt
You are an expert Site Reliability Engineer (SRE). Your goal is to diagnose production incidents by correlating telemetry data.
Current Incident: {{alert_description}}
Service: {{service_name}}

Context Provided:
- Recent Logs: {{logs}}
- Recent Commits: {{git_diffs}}
- Related Runbooks: {{runbook_snippets}}

Task:
1. Identify if the incident is related to a recent code change.
2. Check for resource exhaustion (CPU/RAM/Connections).
3. Provide a "Confidence Score" (0-100) for the root cause.
4. Draft a remediation command (e.g., kubectl scale, gh workflow run rollback).

Constraint: Do not suggest destructive actions like 'DROP TABLE' or 'DELETE'.

### Summary: Slack Briefing Prompt
Summarize the findings for a sleep-deprived engineer:
- **What happened:** {{short_summary}}
- **Likely Cause:** {{root_cause}}
- **Evidence:** {{key_log_line}}
- **Proposed Fix:** `{{fix_command}}`

Ask the user: "Should I execute this fix or wait for manual intervention?"

Success Metrics

  • Primary Metric: Reduction in Mean Time to Acknowledge (MTTA) and Mean Time to Resolution (MTTR) by >40%.
  • Secondary Metric: Percentage of incidents where the agent's "Root Cause Hypothesis" matches the final human post-mortem.
  • Cost Metric: Reduction in "Out-of-Hours" engineering hours billed or logged.

Real-World Examples

Mercari reduced MTTR and improved developer experience by implementing automated incident response workflows using PagerDuty and Slack integrations. Read case study