EVALUATING AGENTIC SYSTEMS BEYOND FINAL ANSWERS
A practitioner describes an evaluation framework for multi-agent assistants that goes past final-answer accuracy by adding trajectory-level checks. They curate ...
A practitioner describes an evaluation framework for multi-agent assistants that goes past final-answer accuracy by adding trajectory-level checks. They curate use-case datasets to run accuracy, hyperparameter sweeps, and ablations, then layer metrics for delegation quality, data-flow fidelity (entity preservation), and resilience to tool failures. This surfaced real issues like stripped URLs and orchestrators masking subagent errors, enabling safer refactors.
It turns agent development from trial-and-error into measurable, repeatable engineering.
It catches hidden failures (lost entities, masked tool errors) that final-answer checks miss.
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terminal
Build curated datasets per use case and run final-answer accuracy with ablations and hyperparameter sweeps across model, temperature, and agent configuration.
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Add trajectory assertions for delegation quality, entity preservation across steps, and recovery after tool or API errors.
Legacy codebase integration strategies...
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Instrument existing agents to capture full traces and add deterministic assertions without changing business logic.
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Run controlled ablations (e.g., remove vector DB, VLM, or prompt sections) to quantify their impact before and after refactors.
Fresh architecture paradigms...
- 01.
Design an evaluation harness first: ground-truth datasets, trace logging, and idempotent tools with explicit inputs/outputs.
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Decide early on LLM-as-judge vs rule-based checks and codify failure-handling contracts between orchestrator and subagents.