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proofagent-harness
by ProofAgent-ai
Adversarial multi-turn test harness for production agents
Python
Updated Oct 6, 2026
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What It Does
Runs adversarial, multi-turn test scenarios against production AI agents to surface failure modes before deployment. Uses scripted red-team scenarios and reusable test fixtures so tests can run in CI and reproduce agent interaction failures. Includes tooling for prompt-injection, hallucination checks, and stress-testing conversation flows, guided by LLM-as-Judge Pattern and reinforced by prudent Guardrails Pattern.
Why It Matters
As agents interact with other systems and agents, subtle failure modes and trust regressions appear only in multi-turn settings. ProofAgent fills the gap by treating agent interactions as first-class test targets and enabling continuous, adversarial A2A evaluation in CI. This makes it easier to build an agent track record and catch delegation or hallucination issues before they reach users. Leveraging established practices like the Model Context Protocol (MCP) Pattern helps standardize context handling across tests.
Target Use Cases
Teams that need continuous, CI-friendly adversarial testing of agent interactions and multi-turn failure detection. The integration can align with established tool ecosystems via the Agent Protocol to standardize agent communication.
How It's Used
- Run adversarial multi-turn scenarios in CI to catch regression and hallucination bugs
- Validate agent delegation flows and multi-agent interaction sequences before production
- Automate prompt-injection and safety checks against agent responses
- Stress-test agent reliability under repeated, stateful conversations
Works With
pytestpythonmcp
Topics
adversarial-testingagent-evaluationagent-testingagentsai-agentsai-red-teamingai-safetyevalsgenaihallucination-detection+9 more
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agent-playgroundagent-arena
Keywords
A2A evaluationmulti-agent trustagent-to-agent evaluationadversarial-testing