Workflow Evaluator
A quality checkpoint that scores automation or AI outputs against schemas, policies, or heuristics before finalizing.
A workflow evaluator scores or validates outputs before they proceed. It catches bad data, policy violations, or model errors early.
Used in AI pipelines, data syncs, and content generation, it checks structure, safety, and business rules. Fails trigger retries, edits, or review.
Evaluators sit between processing and action. They improve trust in automation by enforcing standards before committing changes.
Frequently Asked Questions
What checks should an evaluator include?
Schema validation, required fields, policy filters (PII, profanity), and business rule checks. Add confidence thresholds for model outputs.
How do I respond to evaluator failures?
Retry with adjustments, escalate to human review, or route to DLQ. Log reasons and metrics to improve upstream steps.
Can evaluators be model-based?
Yes—use classifiers or judge models for quality or safety. Pair with deterministic checks for critical rules.
Does evaluation add latency?
Some. Optimize by ordering cheap checks first, caching, and setting time budgets. Use async paths when possible.
How do I measure evaluator effectiveness?
Track rejection rate, false positives/negatives, downstream errors prevented, and retry success. Calibrate over time.
Should evaluators differ by workflow?
Yes—tailor checks to risk and domain. Customer-facing steps need stricter rules than internal drafts.
How do I keep evaluators up to date?
Version rules/models, review violation logs, and adjust thresholds. Retire stale checks that no longer add value.
What logs are needed?
Inputs, outputs, pass/fail, reasons, and correlation IDs. Keep enough to audit decisions and improve upstream quality.
Can evaluators trigger auto-fixes?
Yes—auto-correct minor issues (formatting, missing defaults) before final submission, but log changes for traceability.
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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