PRACTICAL EVALUATION FOR MULTI-AGENT LLM SYSTEMS: DATASETS + TRAJECTORY CHECKS
A practitioner shares a concrete evaluation framework for agentic systems: start with curated task datasets and ground-truth scoring to run hyperparameter/model...
A practitioner shares a concrete evaluation framework for agentic systems: start with curated task datasets and ground-truth scoring to run hyperparameter/model/agent-config sweeps and ablations, then add trajectory-level metrics to assess the agent’s decision process. Trajectory checks include delegation quality (orchestrator vs subagents), data flow fidelity (entities preserved across steps), and resilience (strategy changes after tool failures). This surfaced hidden issues like URL loss and false success reports, enabling safer refactors of the orchestration layer.
Gives a repeatable way to quantify prompt/model/orchestration changes and prevent silent regressions.
Exposes failure modes that final-answer accuracy alone misses, improving reliability of agents in production.
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Build a labeled scenario suite and run automated sweeps across models, temperatures, and agent configs to compare final accuracy.
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Add trace-level assertions for delegation boundaries, entity preservation between steps, and error propagation on tool failures.
Legacy codebase integration strategies...
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Instrument current agents to emit structured step traces (inputs, outputs, tool calls, errors) and evaluate offline before refactors.
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Wrap the existing orchestrator with an eval harness and gate changes in CI using the curated dataset and canary runs.
Fresh architecture paradigms...
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Define strict schemas for messages, entities, and tool results to make trajectory evaluation deterministic from day one.
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Stand up the eval dataset and CI checks first, then scale to more agents, tools, and prompts.