Chronicle Labs
Chronicle Labs provides an AI agent testing platform that uses production data to map real business workflows, policies, and edge cases into test scenarios, then replays them in a simulator to validate agent behavior before deployment. The platform claims a 30x reduction in workflow mapping time and an 80% reduction in critical failures pre-launch, supporting multiple testing modalities including LLM-as-judge, sandbox simulation, and shadow mode.
- Developer Tools
- Artificial Intelligence
- AI Agents
- Software Only
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
AI agents that interact with real systems can cause real harm when they fail—deleting databases, making unauthorized legal commitments, or misleading customers. Most enterprises lack a reliable way to test agents under realistic conditions before deployment, leading to stalled projects: Gartner expects over 40% of agentic AI projects to be canceled by 2027 due to poor risk controls, and only 20% of executives trust agents to handle financial transactions.
Solution
Chronicle Labs uses production data to map how a business actually runs, capturing workflows, policies, and edge cases, then converts those patterns into tests and replays them in a simulator environment. This allows companies to evaluate agent behavior under real-world conditions—such as vague customer inputs, policy changes, API timeouts, or tool errors—before launch. The platform catches failure modes pre-launch and provides a "time machine" to test new agent versions against historical scenarios. By reducing critical failures by 80% and cutting workflow mapping time by 30x, Chronicle enables teams to ship agents with confidence that they are proven before production.
Target Audience
Primary customers are engineering and AI teams at enterprises scaling thousands of AI agents, particularly those deploying agents that touch customer-facing systems, financial transactions, or production databases and need pre-launch validation.
Features
- Production-derived scenario coverage that maps actual workflows, policies, and edge cases into test cases
- Replay engine that simulates production conversations and interactions safely in a sandbox environment
- Multi-modal testing framework supporting LLM-as-judge, golden set evals, trace evals, sandbox simulation, and shadow mode
- Continuous improvement loop that replays failures, learns from them, and redeploys improved agents
- 30x reduction in workflow mapping time and 80% reduction in critical failures pre-launch