Evaluation Loop
A feedback step where model outputs are scored against rules or heuristics before the workflow proceeds, common in agentic systems.
An evaluation loop scores model outputs against rules, heuristics, or secondary models before accepting them. It acts as a quality gate to catch errors early.
Used in drafting, summarization, code generation, and routing, it checks outputs for correctness, safety, or policy alignment. Failed evaluations trigger retries, edits, or human review.
In workflows, the loop sits between generation and action. It reduces bad sends, improves reliability, and provides metrics on where models need adjustment.
Frequently Asked Questions
What signals can an evaluation loop use?
Schema validation, regex checks, allow/deny lists, similarity to references, secondary model judges, and business-rule scoring.
When should I trigger a retry?
If failures are due to fixable issues—formatting, missing fields, minor policy misses. Cap retries and change the prompt or constraints between attempts.
What if the evaluation is slow?
Cache reference data, precompute checks, or run lighter heuristics first. Use async processing when possible and set latency budgets.
How do I avoid over-filtering good outputs?
Tune thresholds with real data, review false positives, and include human spot checks. Gradually tighten rules as precision improves.
Can humans be part of the loop?
Yes—route low-confidence cases to reviewers with the model output, scorecard, and suggested fixes to speed decisions.
How do I log evaluation outcomes?
Store scores, pass/fail status, reasons, prompts, and outputs. Use logs to improve prompts, rules, and model selection.
What models work well as judges?
Smaller, cheaper models fine-tuned for classification or policy checks; or dedicated heuristic/rule sets for deterministic criteria.
How do I measure impact?
Track reduction in bad outputs sent downstream, retry success rate, human escalation rate, and latency added by the loop.
Should evaluation criteria differ by use case?
Yes. Safety-heavy tasks need stricter checks; low-risk internal drafts can be looser. Align criteria with risk and audience.
Agentic AI
An AI approach where models autonomously plan next steps, choose tools, and iterate toward an objective within guardrails.
Agentic Workflow
A sequence where an AI agent plans, executes tool calls, evaluates results, and loops until success criteria are met.
Agent Handoff
A pattern where one AI agent passes context and state to another specialized agent to keep multi-step automation modular.
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