Agent Governance Research

Agent Governance Research

Research into governance patterns for multi-agent LLM systems — how to make AI agents reliably classify, route, and execute complex tasks.

This repository contains both empirical research (experiments, benchmarks, data) and architectural insights (patterns, theories, framework specs) developed through production use of [Claude Code](https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/overview).

Featured Paper

**Forced Depth Consideration Reduces Type II Errors in LLM Self-Classification** *Wiktor Potapczyk, 2026*

LLM task classifiers systematically misroute prompts that look simple but require deeper processing. We test whether prepending a single question before the classification decision reduces this failure mode, and run a mechanism ablation to identify why.

Key findings:

• Open-ended exploration ("What's really going on here?") reduces Type II errors to 1.25%, significantly outperforming directed extraction at 3.12% (p < 0.001, Bonferroni-corrected)
• A content-free metacognitive instruction ("Think carefully") achieves 1.0% — not significantly different from exploration (p = 0.77) — suggesting the mechanism is forced attention to complexity, not open-ended framing specifically
• Structured yes/no detection catastrophically harms some models (Claude Haiku: 330% error increase)
• Qualitative analysis reveals complementary failure modes: exploration forces committed implication statements; the metacognitive directive catches different trap subtypes via consequence framing

Paper (PDF + LaTeX), benchmark, and all experimental data: [`experiments/exploration-prompting-paper/`](experiments/exploration-prompting-paper/)

Research Threads

The full research index tracks 9 active threads: [`INDEX.md`](INDEX.md)

Thread	Summary
Exploration Prompting	Step-0 framing for self-classification (paper above)
Compound Task Neural Network	Tasks as continuous mixtures of 5 primitives
Qual