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EvaluationExperimental
claimpilot-harness
by samarailly51-pixel
Crash-test insurance claim agents with scenario-driven adversarial evaluations
Python
Updated Jul 26, 2026
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Summary
Simulates and crash-tests insurance claim AI agents by running them through realistic, adversarial claim workflows. Uses scenario-based harnesses, prompt-injection checks, and automated scoring to surface failure modes before production. Includes Python test drivers and configurable evaluation scenarios tailored to insurance workflows. scenario-based harnesses
Why It Matters
As agents take on higher-stakes domain tasks like claims handling, knowing how they fail under realistic adversarial conditions is critical. This harness provides repeatable A2A evaluation patterns and prompt-injection probes so teams can quantify agent reliability and spot safety gaps. It helps shift focus from single-shot benchmarks to continuous pre-production vetting of agent behavior. A2A evaluation patterns safety gaps
Ideal For
QA engineers and ML teams building insurance claim agents who need structured pre-production stress tests and prompt-injection checks. insurance claim agents pre-production stress tests
Applications
- Validate claim-handling agents against adversarial or malformed inputs before deployment
- Detect and catalog agent failure modes and prompt-injection vulnerabilities
- Run repeatable scenario-based benchmarks to compare agent variants and track agent track record
Works With
openailangchain
Topics
agent-evaluationai-agentsinsurancellm-evalsprompt-injectionpythontesting
Similar Tools
agent-playgroundllm-evals
Keywords
agent-evaluationA2A evaluationagent failure modesprompt-injection