Crash-Testing Insurance Claim AI Agents Before Production
ClaimPilot Harness is an open-source evaluation framework designed specifically for testing insurance claim AI agents prior to deployment. Unlike other claim-processing agents, it functions as a controlled test environment where AI agents are subjected to adversarial scenarios that mimic real-world complexities. These scenarios include mismatched invoices, missing documents, policy exclusions, claimant contradictions, hidden prompt injections, and privacy-related pitfalls. The framework ensures that agents are rigorously evaluated for reliability under business pressure before being deployed in production.
The tool operates by initializing an agent test project, executing predefined adversarial claim cases, enforcing deterministic quality gates, and generating replayable reports and benchmark scorecards. It is structured to identify where agents pass, hesitate, or fail, providing measurable insights into their performance.
ClaimPilot Harness is maintained under the MIT license and is written in Python. It was first released in 2026 and has since gained traction within the AI safety and harm benchmarking community, accumulating 117 stars on its repository.
How It Works
The framework begins with the initialization of a test project using the command claimpilot init my-agent-eval. Users can then replace the starter logic with their own agent or configure it to interface with HTTP services or OpenAI-compatible endpoints. The core functionality is triggered by running claimpilot test, which executes the predefined adversarial cases and produces quality gate results, HTML reports, SVG scorecards, Markdown summaries, submission JSON files, and dataset SHA-256 hashes for reproducibility.
These baselines serve as reference points for evaluating the performance of custom agents.
This ensures that agents meet predefined reliability standards before deployment.
Use Cases
ClaimPilot Harness is tailored for organizations developing AI agents for insurance claim processing. Its primary use case is pre-deployment testing to identify vulnerabilities and ensure compliance with business and regulatory requirements. The framework addresses critical failure modes such as evidence conflicts, missing documentation, policy exclusions, claimant contradictions, prompt injections, and privacy violations, all of which can lead to costly errors or regulatory breaches in production environments.
Users can compare the performance of different agents across a standardized set of adversarial cases, generating leaderboards and benchmark scorecards. This allows teams to objectively assess which agents are most reliable and suitable for deployment. For example, the framework can compare a cautious agent that adheres strictly to evidence-based decision-making against a risky agent that may approve claims prematurely, highlighting the trade-offs between speed and accuracy.
The tool also supports the evaluation of tool-use trajectories, ensuring that agents follow required workflows before making claim decisions. This includes scoring tool coverage, evidence retrieval, action ordering, approval gates, forbidden actions, and step budgets.
Another use case is the generation of reproducible benchmarks. ClaimPilot Harness allows users to run named agent profiles against the same dataset and produce benchmark snapshots that include experiment IDs, dataset fingerprints, profile types, scores, pass rates, latency metrics, and case-level replays. This enables organizations to track performance over time and compare agents objectively, even as the underlying models or claim scenarios evolve.
The Human Review Workbench provides an interactive environment for reviewers to confirm findings, override agent verdicts, record rationale, and export dataset-bound JSON artifacts. The workbench operates locally, maintaining data privacy and security.
Key Features and Capabilities
ClaimPilot Harness includes a curated pack of 10 adversarial claim cases spanning six insurance lines: auto, health, travel, pet, property, and workers compensation. Each case is designed to test specific failure modes, such as bodily injury, causation gaps, prompt injections, evidence conflicts, and policy exclusions.
The framework employs an 18-tag risk taxonomy to categorize failure modes, including tags like bodilyinjury, causationgap, promptinjection, and evidenceconflict.
One of the standout features is the Trace Arena, which evaluates whether an agent follows the required workflow before making a claim decision. It scores tool coverage, evidence retrieval, action order, approval gates, forbidden actions, verdicts, and step budgets. For instance, the auto-bodily-injury-001 case verifies that an agent does not auto-approve a claim based solely on the claim amount if material evidence conflicts or is missing. This ensures that agents adhere to safe and compliant decision-making processes.
Users can run their own models against the adversarial case pack and compare them against the built-in baselines. The Model Arena feature allows for the creation of benchmark snapshots with detailed metrics, including dataset fingerprints, latency, scores, and pass rates. This enables organizations to conduct rigorous performance comparisons and select the most reliable models for deployment.
The framework also includes automation capabilities, such as configurable quality gates that enforce minimum performance thresholds. This ensures that only agents meeting predefined reliability standards proceed to production.
The replay reports include evidence timelines, agent verdicts, confidence levels, findings, requested documents, and scoring breakdowns.
The tool’s architecture is modular, with clear separation between case packs, agent adapters, scorers, replay mechanisms, and suite management. This design ensures that users can extend the framework to include additional cases, adapters, or scoring criteria as needed. The deterministic nature of the evaluation process guarantees reproducibility, making it easier to track performance over time and validate improvements.
ClaimPilot Harness has undergone iterative development, with notable updates in versions 0.1.1, 0.2.0, and 0.3.0. This version also added deterministic tool-use tracking, scoring for required tool coverage, evidence retrieval, action ordering, and approval gates.
Version 0.2.0 transformed the project from a static evaluation demo into a traceable claims-agent reliability product. It introduced the Model Arena for benchmarking, a Human Review Workbench for interactive auditing, and expanded the case pack to include 10 adversarial scenarios across six claim lines and 18 risk patterns. Version 0.1.1 refined the project into a polished, portfolio-ready release, with strengthened documentation, synchronized package metadata, and a public benchmark disclosure.
The framework’s documentation includes detailed explanations of its architecture, evaluation methodology, and system design. Users can access a live demo, machine-readable suite results, and architecture documentation to understand how the tool works and how to integrate it into their workflows. The interactive GitHub Pages demo provides a hands-on experience, allowing users to explore the product’s capabilities firsthand.