Evaluation Hooks

Automated quality assessment, continuous validation, and feedback loops for improving agent performance.

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The Evaluation Challenge

How do you know if your multi‑agent system is working correctly? Agent outputs might look plausible but be factually wrong, incomplete, or inconsistent.

Unlike traditional software where correctness is binary, AI systems operate in probabilistic space. Evaluation must be continuous, automated, and tightly integrated with orchestration.

Agiorcx provides evaluation hooks at key points in workflow execution, enabling automatic quality checks and continuous validation.

Evaluation Hook Types

Pre‑Execution Hooks

Validate inputs before agents execute. Check that context is complete, payloads match schemas, and preconditions are satisfied.

Post‑Execution Hooks

Evaluate agent outputs immediately after execution. Check format compliance, confidence thresholds, and business rule violations.

Mid‑Workflow Checkpoints

Insert evaluation points between workflow stages. Validate accumulated state before proceeding to expensive or irreversible operations.

End‑to‑End Validation

After workflow completion, assess overall quality. Did the system achieve its goal? Are results usable by downstream systems?

Human Feedback Loops

Capture human corrections and ratings. Feed them back into evaluation logic to improve future agent behavior.

Comparative Evaluation

Run multiple agent variants on the same inputs. Compare outputs to identify which configurations perform best.

Evaluation Strategies

Rule‑Based Validation

Explicit business rules and constraints. "Output must contain valid date in ISO 8601 format." "Total amount must match sum of line items."

Fast, deterministic, and easy to debug. Best for well-defined correctness criteria.

Model‑Graded Evaluation

Use another LLM as a judge. "Does this summary capture key points from the source document?" "Is this response professional and helpful?"

Flexible and scalable but requires careful prompt engineering and confidence calibration.

Reference‑Based Validation

Compare agent outputs to known-good references. Use similarity metrics (embeddings, BLEU scores) to assess quality.

Effective when you have ground truth examples or golden datasets.

Tool‑Assisted Verification

Invoke external tools to verify claims. "Does this company exist?" (check database). "Is this calculation correct?" (recompute independently).

High confidence validation for factual correctness.

Ensemble Voting

Run multiple agents or models, compare outputs. If consensus exists, confidence is high. If outputs diverge, trigger human review.

Trade compute cost for reliability.

Evaluation Metrics

Agiorcx tracks evaluation results over time, exposing metrics that help teams understand agent quality.

Validation Pass Rate

Percentage of agent outputs that pass automated evaluation checks on first attempt.

Average Confidence Score

Self-reported or model-graded confidence across agent invocations. Trends indicate drift or degradation.

Human Override Rate

How often humans correct or reject agent outputs. High rates indicate quality issues.

Evaluation Latency

Time spent on evaluation checks. Helps optimize the cost/quality tradeoff.

False Positive / False Negative Rates

Track how often evaluators incorrectly flag good outputs or miss bad ones.

Continuous Improvement Loop

Evaluation isn't just about catching failures. It's about systematic improvement.

  1. 1.Capture evaluation results for every workflow run
  2. 2.Aggregate metrics to identify patterns: which workflows fail most often?
  3. 3.Build evaluation datasets from production runs (successes and failures)
  4. 4.Fine-tune agents or adjust prompts based on failure modes
  5. 5.A/B test improvements using comparative evaluation hooks
  6. 6.Deploy winning variants and repeat the cycle

By making evaluation a first-class orchestration primitive, Agiorcx enables data-driven iteration on agent behavior.