Observability for agents
Why standard APM doesn’t cut it. The four pillars of agent observability: traces, evaluations, drift, and content policy.
Whitepaper · AI Ops
AI agents at scale — observability and guardrails
Multi-agent systems in production: traces, evals, drift, kill-switches.
28
Pages
13 min
Read time
AI Ops
Topic
PDF
Format
Key takeaways
- 01 A four-pillar observability model for multi-agent systems.
- 02 Evaluation harnesses that survive prompt drift.
- 03 Kill-switch architectures that have prevented two production incidents.
Evaluations that survive
How to build evaluation harnesses that detect regression in production rather than just in dev. Includes a sample harness with synthetic and ground-truth datasets.
Drift detection
Detecting prompt drift, model drift, and content policy drift. Three early-warning signals that have caught production incidents.
Kill-switches that work
A kill-switch architecture that lets you contain a misbehaving agent without taking the platform offline. Live in production with two named clients.
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