BasicAgent
AI Monitoring for Autonomous Agent Operations
AI monitoring for production LLM and agent systems: runtime metrics, governance evidence, failure detection, and recovery controls.
AI monitoring keeps AI systems stable after launch. It connects runtime metrics to quality, cost, and governance evidence.
Featured: governance + monitoring runbook
See how policy gates, bgmon lifecycle controls, and completion audits create monitorable, enforceable agent behavior.
policy gate
autoheal
evidence trail
Monitor these signals
- latency, timeouts, and retries
- cost and token usage drift
- tool call failures and fallbacks
- evaluation gate pass rates
Related pages
- AI agent monitoring:
/ai-agent-monitoring/ - AI observability:
/ai-observability/ - LLM audit trail:
/llm-audit-trail-agent-pipelines/
Create account
Build narrative
Follow a coherent path from thesis to lab notes to proof-of-work instead of isolated pages.
Step 1
Intelligence systems office
The strategic map for what is being built and why.
Step 2
Lab notes
Build footprints and progression logs as proof-of-work.
Step 3
Control surface
Governance and monitoring architecture for operational reliability.
Step 4
Private alignment
Convert insight into execution with scoped collaboration.