multi-agent-validation

name	multi-agent-validation
description	Validate artifacts with multiple independent AI tools — images, code, documents, designs. Use when a single perspective has blind spots, for visual output validation, or when validators disagree. Trigger: multi-agent validation, independent validators, cross-check with another tool, second opinion, validation crew.
metadata	{"type":"protocol","invocation":"both","practice":null}

Multi-Agent Validation

Validate artifacts (images, code, documents, designs) using multiple independent AI tools. Each tool brings different strengths; disagreement surfaces real issues.

When to Use

• Visual output validation (rendered images, UI screenshots, design mockups)
• Code review requiring multiple perspectives (logic, security, performance)
• Documentation quality checks (accuracy, completeness, clarity)
• Design decisions needing independent opinions before committing
• Any artifact requiring qualitative judgment where a single perspective has blind spots

NOT suitable for: purely objective checks (linting, type checking, test pass/fail) where a single authoritative tool suffices.

The Pattern

1. Produce the artifact
2. Run N independent validators (minimum 2, recommended 3)
3. If ANY validator fails → investigate and fix
4. Record all verdicts with evidence

Validator Roles

Role	Strength	Weakness	Example Tools
Visual Inspector	"Does this look right?" Qualitative impression	Can't measure, may miss technical issues	codex, human eye
Technical Auditor	Measures actual values, reads source code	May over-report invisible issues, can misdiagnose root causes	agy, linters, static analysis
Contextual Judge	Domain knowledge, intent awareness, breaks ties	Bias toward "knowing what it should do"	kiro, domain expert

Disagreement Resolution

• All agree PASS → proceed with confidence
• All agree FAIL → fix before proceeding
• Split verdict → investigate the discrepancy:
  • Technical auditor wins on measurable criteria (pixel values, code correctness)
  • Visual inspector wins on subjective quality (does it look right to a human?)
  • Contextual judge breaks ties using domain knowledge and intent
• Auditor finds a bug → verify against authoritative documentation before acting (tools can misdiagnose)

Image Validation (Primary Use Case)

Tool	Role
kiro (direct image inspection in conversation)	Contextual judge
codex (codex exec -i <image> --sandbox read-only)	Visual inspector
agy with --sandbox	Visual inspector + native measurement
agy full (reads code)	Technical auditor

Full command syntax, flags, and per-tool quirks: references/tool-invocation.md.

Prompt Engineering

Critical for toon/NPR/stylized renders — without this context, tools flag intentional stylization as defects:

This is an intentional toon/cel-shaded render where hard shadow edges
are a DESIRED feature (not a bug). PASS if [criteria].
FAIL only if [failure condition].

Structure prompts as checklists for structured, comparable output across tools:

For each [object]:
1. criterion? YES/NO
2. criterion? YES/NO
Overall PASS/FAIL.

Code and Document Validation

Same pattern, different prompts: codex for logic/readability review, agy for security/accuracy audits, kiro for architecture review against project conventions. Command examples: references/lessons-learned.md.

Known Tool Behaviors

Validated characteristics, failure patterns, and token costs live in references/lessons-learned.md. Highlights:

• agy flags intentional styles as defects unless the prompt declares them desired
• agy identifies symptoms correctly but may misattribute root causes — verify against docs
• kiro's standards drift down after many iterations (context fatigue)

Applicability

This pattern works for any domain where quality is partially subjective, no single tool catches all issues, false confidence from one perspective is dangerous, and the cost of shipping a defect exceeds the cost of 2-3 extra checks.