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AgentCheck
by WaseemGhanem98
Behavioral testing harness for AI agents and scenario-based failure discovery
Python
Updated Oct 1, 2026
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Overview
Runs behavioral tests against AI agents to surface failure modes and policy deviations. Provides scripted scenarios and assertion checks that exercise agent decision paths and interactions. Distinctive for focusing on behavior-driven tests (not just output benchmarks), making it easier to codify expected agent conduct.
Key Benefits
As agents take on more autonomous workflows, seeing how they behave in realistic scenarios is crucial to trust. The challenge of brittle or unsafe agent behaviors requires tests that simulate decision-making contexts rather than only measuring outputs. AgentCheck fills that gap by treating agent behavior as testable software, enabling continuous A2A evaluation and tracking of agent track record over time.
Ideal For
Teams building or evaluating agents who need repeatable behavioral tests and early detection of unsafe or incorrect decision patterns.
Real-World Examples
- Validate agent decision-making against safety and policy rules via scripted scenarios
- Detect regressions by running behavioral test suites after model or policy changes
- Simulate multi-step interactions to expose delegation and failure modes
Topics
agent-evaluationai-agentsai-safetydeveloper-toolsllmpythontesting
Similar Tools
agent-playgroundagent-arena
Keywords
multi-agent trustA2A evaluationagent track record