Documentation Index
Fetch the complete documentation index at: https://docs.codewolf.ai/llms.txt
Use this file to discover all available pages before exploring further.
Key features
Investigate
Fix
Prevent
Optimize
Understand
Tag @Codewolf in Slack when issues strike. AI agents analyze deployments, correlate logs across services, trace requests, and identify root causes. From alert to diagnosis in minutes.Slack mentions:
- @Codewolf payment API is throwing 500 errors, customers can’t checkout
- @Codewolf investigate this PagerDuty alert - database latency spiking
- @Codewolf trace this request ID through our services
Triggers:
- Alert: High error rate detected in checkout service
- Webhook: Customer support ticket for checkout failures
- Incident channel created: Site down - payment service unreachable
AI agents don’t just find problems, they fix them. With full production context and live telemetry, agents generate robust PRs with code patches and configuration changes that resolve issues fast and correctly.Slack mentions:
- @Codewolf create a PR to fix the connection pool exhaustion
- @Codewolf fix the memory leak in auth service
Triggers:
- Post-investigation: Automatically generate PR with fix
- Alert resolution: Apply configuration change to resolve issue
Catch issues before they reach production. AI agents review PRs for risks, run post-deployment checks, detect anomalies in metrics, and flag suspicious patterns. Stop problems before they become incidents.Slack mentions:
- @Codewolf review this PR for production risks
Triggers:
- GitHub PR opened: Warning - PR #412 introduces potential memory leak
- GitHub deployment: Analyze errors after deploy #1234
- Post-deployment: Detected N+1 query issue in staging
Schedules:
- Daily 9am report: 3 services missing error handlers, 2 deprecated APIs in use
Continuously improve your infrastructure. AI agents analyze costs, identify resource waste, recommend performance improvements, and track efficiency trends. Get daily insights without manual analysis.Slack mentions:
- @Codewolf analyze our infrastructure costs and find savings
Triggers:
- Cost spike detected: Investigate unusual AWS spending increase
Schedules:
- Weekly Monday report: Reduce AWS costs by 23% by rightsizing 12 EC2 instances
- Daily 9am report: Query optimization opportunities in payment service
Codewolf AI agents understand your entire system. Not just your codebase, but production behavior, infrastructure, service dependencies, deployment patterns, and system relationships. This deep context enables smarter investigations, better fixes, and proactive prevention.Slack mentions:
- @Codewolf explain how the payment service connects to Stripe
- @Codewolf what services depend on the auth API
- @Codewolf show me similar incidents we’ve had with database connections
Codebase & Architecture:
- Code structure, patterns, and architectural decisions
- Monorepo organization and cross-service dependencies
- Shared libraries and common utilities
Production & Runtime:
- Production behavior and runtime characteristics
- Performance patterns and resource usage
- Error rates and failure modes
Infrastructure & Topology:
- Service dependencies and data flow relationships
- Deployment patterns and infrastructure topology
- Network topology and API contracts
Knowledge & History:
- Historical incidents and resolution strategies
- Team knowledge from Slack conversations
- Documentation and runbooks
How Codewolf agents work
Codewolf connects to your systems, continuously builds operational context, and deploys specialized AI agents to handle engineering tasks autonomously.
Context generation
Agents continuously ingest and update context from your integrations and historical events. Context evolves as your systems change, ensuring decisions are always based on current state.
Triggers and events
Codewolf processes events from multiple sources including Slack mentions, alerts, cron jobs, GitHub events, and webhooks. Triggers are normalized, prioritized, and routed appropriately.
Agent orchestration
A central coordinator dynamically spins up worker agents based on task type and complexity. Specialized agents run in parallel to investigate issues, generate fixes, or perform analysis.
Skill-aware integrations
Each integration is configured with specific agent capabilities. Built-in rate limiting, retries, and backoff ensure reliable operation across all connected systems.
Architecture
Security
Every customer gets their own isolated Private Agent Sandbox with:
Isolated infrastructure:
- Dedicated agent compute resources per customer
- Isolated agent file storage - your data never touches other customers
- Isolated agent database - complete data separation
Enterprise security:
- AES-256 encryption in transit and at rest
- SOC 2 Type II certified
- SSO & SAML support
- Role-based access control
Your data is never shared with other customers or used for model training.
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