Dag Quality

DAG Quality

Validates outputs, scores confidence, detects hallucinations, monitors convergence, decides on iteration, and synthesizes feedback. The quality gate between DAG nodes. Consolidates dag-output-validator, dag-confidence-scorer, dag-hallucination-detector, dag-convergence-monitor, dag-iteration-detector, and dag-feedback-synthesizer.

When to Use

✅ Use for:

• Validating node output against declared schema
• Scoring confidence on agent outputs (0-1)
• Detecting fabricated content, false citations, unverifiable claims
• Deciding whether to iterate (re-execute a node or loop)
• Generating structured improvement feedback for re-execution
• Monitoring quality trends across iterations

❌ NOT for:

• Executing nodes (use dag-runtime)
• Planning DAG structure (use dag-planner)
• Grading skills themselves (use skill-grader)

Quality Pipeline

flowchart TD
  O[Node output] --> SV[Schema validation]
  SV -->|Invalid| REJ[Reject + specific errors]
  SV -->|Valid| CV[Content validation]
  CV --> CS[Confidence scoring]
  CS --> HD[Hallucination detection]
  HD --> D{Quality above threshold?}
  D -->|Yes| ACC[Accept → pass to downstream]
  D -->|Below threshold, iteration < max| FB[Generate feedback]
  FB --> RE[Re-execute with feedback]
  D -->|Below threshold, iteration = max| ESC[Escalate to human]

Schema Validation

Structural check: does the output match the node's declared output contract?

• Required fields present
• Types correct (string, number, array, object)
• Constraints met (min/max length, ranges, enums)
• Nested structures valid

Content Validation

Semantic check: is the content reasonable?

• Non-empty meaningful content (not just filler)
• Length within expected range for the task
• Internal consistency (no contradictions)
• References exist (cited files, URLs, identifiers)

Confidence Scoring

Aggregate four evaluator signals (see skill-lifecycle.md for full architecture):

Final score = weighted average of available signals (normalize weights to sum to 1.0).

Hallucination Detection

Specific checks for fabricated content:

• Citation verification: Do cited sources exist? Do they say what's claimed?
• Internal consistency: Does the output contradict itself or the input?
• Confidence calibration: High-confidence claims on topics where uncertainty is expected
• Entity verification: Do named entities (people, tools, APIs) actually exist?

Iteration Decision

If quality_score >= 0.8: ACCEPT
If quality_score < 0.8 AND iterations < max_iterations: ITERATE with feedback
If quality_score < 0.5 AND iterations >= max_iterations: ESCALATE to human
If quality_score < 0.3 on first attempt: ESCALATE immediately (fundamentally wrong)

Feedback Synthesis

When iterating, produce structured improvement guidance:

{
  "overall_score": 0.65,
  "specific_issues": [
    {"field": "recommendations", "issue": "Only 2 of 5 required recommendations provided", "fix": "Add 3 more recommendations addressing scalability, testing, and deployment"},
    {"field": "citations", "issue": "Source [3] returns 404", "fix": "Replace with a working source or remove the claim"}
  ],
  "strengths_to_preserve": ["Clear structure", "Good code examples"],
  "iteration_guidance": "Focus on completeness (missing recommendations) and citation accuracy. Do not rewrite the well-structured sections."
}

Convergence Monitoring

Track quality scores across iterations to detect:

• Improving: Score trending up → continue iterating
• Plateauing: Score stable for 2+ iterations → stop, accept current best
• Degrading: Score declining → stop, revert to best previous iteration
• Oscillating: Score alternating up/down → stop, the feedback is contradictory

Replaces

Consolidates: dag-output-validator, dag-confidence-scorer, dag-hallucination-detector, dag-convergence-monitor, dag-iteration-detector, dag-feedback-synthesizer