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EvaluationExperimental
money-agent
by ImmortalDemonGod
Experimentally verify agent money-making claims with a separated verifier
Python
Updated Jul 26, 2026
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Overview
Explores whether an autonomous agent can reliably make money while keeping its reports honest. verifier pattern. Runs an agent that attempts financial actions and isolates a separate verifier (which the agent cannot call) to independently confirm facts and outcomes. separation-of-duties between actor and verifier to detect false claims and measure real-world accuracy.
Why It Matters
As agents get more autonomous and can take actions with monetary impact, you need concrete ways to check if their reports reflect reality. The challenge of untrusted self-reporting is acute for reputation and financial workflows; this repo experiments with a verifier pattern that prevents agents from gaming their own truth signals. A2A evaluation design
When to Use
Researchers and engineers testing agent self-reporting, separation-of-duties, and basic A2A evaluation scenarios involving financial actions. Through the MCP Pattern.
Applications
- Validate whether an agent's reported financial actions match independent facts
- Test separation-of-duties patterns where an actor agent cannot influence its verifier
- Surface attack and failure modes from self-reporting agents before production deployment
Works With
anthropicclaudestripepython
Topics
agent-evaluationagentic-aiai-agentai-agentsai-evaluationai-safetyai-verificationanthropicautonomous-agentscase-study+8 more
Similar Tools
agent-playgroundagent-arena
Keywords
multi-agent trustA2A evaluationagent track record