parallax-investigation

name	parallax-investigation
description	Use when you need true understanding of a complex system — not a quick answer. Parallax Discovery is a multi-agent, multi-pass investigation methodology that combines three perspectives (code tracing, behavior observation, integration mapping) to produce verified, evidence-backed findings.

Parallax Discovery Methodology

Overview

Parallax Discovery is a multi-agent, multi-pass investigation methodology named after astronomical parallax — just as astronomers triangulate depth by observing stars from multiple positions, this methodology triangulates truth by examining code from multiple perspectives.

Use Parallax Discovery when you need true understanding, not a quick answer:

• Unfamiliar or complex codebases
• Debugging subtle, intermittent, or cross-cutting issues
• Architecture review before major refactoring
• Onboarding investigation to map system structure
• Security audits requiring evidence-backed findings

A single agent reading code produces a single perspective. Parallax produces three, then reconciles them.

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The Three-Perspective Model

Each perspective is a distinct agent role. Together they form a triplicate team. All agents in a team run with fresh context (context_depth='none') — this is non-negotiable. Prior session state contaminates findings.

Agent 1: Code Tracer (HOW)

• Traces actual execution paths through source files using LSP
• Uses goToDefinition, incomingCalls, outgoingCalls, findReferences
• Requires file:line evidence for every significant finding
• Produces findings.md, evidence.md, diagram.dot

Agent 2: Behavior Observer (WHAT)

• Examines 10+ real instances minimum — patterns emerge from many, not one
• Catalogs structure, quantifies prevalence, identifies anti-patterns
• Distinguishes what actually exists vs. what documentation claims
• Produces catalog.md, patterns.md, findings.md, diagram.dot

Agent 3: Integration Mapper (WHERE/WHY)

• Maps how mechanisms integrate across component boundaries
• Finds composition effects — emergent behavior at boundaries
• Identifies cross-cutting concerns that single-perspective agents miss
• Produces integration-map.md, findings.md, diagram.dot

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The No-Fiat Rule

Discrepancies between agents are valuable signals, not failures.

When two agents disagree:

1. Never reconcile by picking the more plausible answer — this is the no-fiat rule
2. Assign a tracking ID (D-01, D-02, D-NN)
3. Record both claims with full evidence citations
4. Mark OPEN if unresolvable from available evidence
5. Unresolved discrepancies become recommendations for the next wave

A discrepancy marks exactly where design and reality have separated. Suppressing it destroys the investigation's most valuable finding.

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Wave Structure

Wave 1 — Discovery

Goal: Cast a wide net. Identify patterns, mechanisms, and boundaries.

• Dispatch triplicate teams per topic (code-tracer + behavior-observer + integration-mapper)
• Each team investigates independently with zero shared context
• DOT diagrams are discovery tools, not just outputs — draw what you find
• After all teams complete, lead-investigator reconciles findings across teams

Wave 2 — Verification

Goal: Focused deep dives to resolve discrepancies from Wave 1.

• Targeted investigation of specific D-NN discrepancy items
• evidence.md with file:line citations is required for every finding
• Wave 2 produces RESOLVED / PARTIALLY RESOLVED / UNRESOLVED verdicts

Wave 3 — Adversarial (optional)

Goal: Execution-based stress testing of consolidated findings.

• Dispatches antagonist agents with no prior findings context
• Writes test scripts and runs actual code to prove or disprove claims
• Code reading identifies mechanisms; execution proves impact
• Claims that survive adversarial testing are verified; refuted claims are corrected

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Fidelity Tiers

Choose fidelity based on investigation depth required:

Tier	Agents	Use When
quick	1 agent: code-tracer only	Fast initial scan, time-constrained
standard	2 agents: code-tracer + integration-mapper	Default for most investigations
deep	3 agents: full triplicate (all three)	High-stakes, unfamiliar, or complex systems

Default fidelity is standard. Use the fidelity context variable to override.

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Investigation Frames (Lens)

The investigation lens context variable focuses agent attention:

Frame	Lens Value	Focus
Architecture	architecture	System structure, module boundaries, coupling
Security	security	Attack surface, data flow, trust boundaries
Quality	quality	Code health, anti-patterns, technical debt
Onboarding	onboarding	Entry points, key concepts, learning path
Debugging	debugging	Failure modes, error paths, state transitions

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Artifact Strategy

Each agent writes to an isolated subdirectory — no shared mutable state between agents.

.discovery/
  investigation/
    modules/
      {slug}/
        agents/
          code-tracer/
            findings.md
            evidence.md
            diagram.dot
            unknowns.md
          behavior-observer/
            catalog.md
            patterns.md
            findings.md
            diagram.dot
          integration-mapper/
            integration-map.md
            findings.md
            diagram.dot
  output/
    combined.md
    overview.dot
    synthesis.md

Artifacts are written before reconciliation. The lead-investigator reads all artifacts after the wave completes and produces the reconciliation document.

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Deep Reference

For full protocol specifications, see:

• @parallax-discovery:context/methodology.md — complete methodology reference
• @parallax-discovery:context/triplicate-pattern.md — triplicate team patterns
• @parallax-discovery:context/artifact-strategy.md — artifact directory conventions
• @parallax-discovery:context/wave-protocol.md — wave execution protocol