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2026·shipped·Forward Deployed Engineer

The Automaton Auditor: Multi-Agent Code Auditing Swarm

A LangGraph swarm that grades repositories against a structured rubric. Detective, Prosecutor, Defense, Tech Lead, and Chief Justice nodes produce a deterministic verdict.

10 Academy TRP · 1-week brief
PythonLangGraphLLM OrchestrationAST

Verdict flow

Detective
Forensic analysis
Prosecutor
Argues issues
Defense
Argues against
Tech Lead
Weighs arguments
↓ feeds into
Chief Justice
Deterministic verdict + remediation plan

Highlights

  • Adversarial multi-agent pattern: Prosecutor argues issues, Defense counters, Tech Lead adjudicates, Chief Justice returns a verdict.
  • Detective stage runs deterministic forensic analysis on the repo before any LLM reasoning happens.
  • Structured rubric produces reproducible verdicts and a concrete remediation plan.

The idea

Ordinary LLM code reviewers agree with themselves too much. If the model likes a pattern, it will keep liking that pattern across every file, and its “review” is really a repeated preference.

The Auditor forces disagreement into the loop. A Prosecutor node argues the negative case against every finding. A Defense node argues the positive. A Tech Lead mediates, and a Chief Justice returns the final verdict: deterministic, cited, and reproducible.

Pipeline

  1. Detective: deterministic forensic analysis over the repo (AST, dependency graph, file metadata). No LLM.
  2. Prosecutor: argues issues, grounded in Detective’s evidence.
  3. Defense: argues against, grounded in the same evidence.
  4. Tech Lead: weighs the arguments.
  5. Chief Justice: signs off on the verdict and produces a remediation plan.

Why it works

Structured disagreement produces more calibrated verdicts than a single reviewer prompt. The Detective stage keeps the reasoning grounded in actual code artifacts, not vibes.