BasicAgent
LLM Evaluation for Agent Systems
LLM Evaluation for Agent Systems — How to evaluate multi-step agent pipelines: golden runs, stage-level checks, diffing, regression gates, and drift monitoring.
Most eval advice is for single prompts. Agents fail at the workflow level:
- stage contracts break (JSON shape, tool arguments)
- retrieval changes silently
- costs spike
- “helpful” model updates change outputs
Evaluation that actually works for agent pipelines
- Golden runs
- stable inputs → expected stage outputs (or invariants)
- Stage-level checks
- schema validation, citations present, numeric consistency, required fields
- Diffing
- compare outputs across model versions / prompt versions / retrieval versions
- Regression gates
- block deploys when gates fail
- Drift monitoring
- alert when distributions shift (length, citations, tool usage, cost)
Download a checklist you can use immediately: /tools/agent-evaluation-checklist/
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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.