I Directed AI Agents to Build a Tool That Stress-Tests Incentive Designs. Here’s What It Found.

2026-04-10 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

Agent 006, an open-source tool developed by directing AI coding agents, stress-tests incentive designs by simulating economic systems with AI-generated adversarial agents. It takes a natural-language specification of resources, actions, constraints, and win conditions, then uses a four-step Claude API pipeline to generate a sandboxed JavaScript simulation, adversarial agent archetypes, and executable decision logic. The tool runs simulations to identify boundary conditions and failure modes, such as an underspecified contribution cap in a public goods scenario or an execution-order bug in an ultimatum game. It operates in sandboxed Node 22+ VM contexts with robust security measures and supports multi-run campaigns where agents adapt strategies. Agent 006 is designed for early-stage prototyping, complementing formal analysis rather than replacing it.

Key takeaway

For AI Engineers or Directors of AI/ML designing new economic systems or incentive structures, Agent 006 offers a rapid, low-code method to pre-flight stress-test your designs. You should use this tool to quickly surface ambiguities and failure modes in natural language specifications before committing to formal analysis or production deployment, treating its non-deterministic output as a feature for exploration rather than a bug.

Key insights

AI agents can build tools to stress-test economic incentive designs, revealing hidden flaws and ambiguities.

Principles

• Non-determinism can surface design flaws.
• LLM-generated code requires an investigation loop.
• Sandbox generated code for security.

Method

Define an economic scenario in natural language, use LLMs to generate a simulation and adversarial agents, run the simulation, and analyze results to identify design flaws or code bugs, iteratively refining the specification or generator prompts.

In practice

• Prototype token economies with natural language specs.
• Test bonus structures for unexpected agent behaviors.
• Identify resource allocation policy weaknesses early.

Topics

• AI Agents
• Incentive Design
• Economic Simulation
• Stress Testing
• Natural Language Specification

Code references

• selfradiance/agentgate
• selfradiance/agent-004-red-team
• selfradiance/agent-005-recursive-verifier

Best for: AI Product Manager, AI Engineer, Director of AI/ML, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.