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Staging Environments for Enterprise AI Agents
Chronicle Labs is a staging environment for enterprise AI agents: we capture every event the agent sees in production and backtest it, so customers can safely test new behaviors without breaking anything.
Chronicle Labs provides an AI agent testing and validation platform that turns production data into staging environments to test AI agents before launch. It emphasizes catching more failure modes and validating readiness, backed by Y Combinator.
Chronicle connects to your production stack and 100+ integrations to reconstruct workflows from real data, then creates replayable tests and backtests agents against replicated production scenarios. It supports live production data capture, scenario discovery from existing data streams, backtesting in staging with historical, edge, and adjacent scenarios, and an autonomous recovery/patch flow for post-deployment monitoring and fixes. The platform aims to reduce time-to-mignition for agent readiness by providing monitoring, reproduction of real failures, and continuous improvement through a test-driven loop.
Who it’s for: enterprises deploying AI agents and workflows requiring validation and risk mitigation before production
Backed by Y Combinator; documented user testimonials and enterprise-focused messaging
Ayman Saleh, CEO of Chronicle Labs (YC P26) I’ve spent my career building at the edge of engineering, from working at NASA JPL on programs including the James Webb Space Telescope and the Mars 2020 Perseverance rover to later leading engineering at FlightWave. When Perseverance landed on Mars, it crystallized something for me: I wanted to spend my life building, so I left my graduate program at Stanford, and eventually led me to found Chronicle Labs.
Co-founder and COO at Chronicle Labs. Building the standard for shipping ai agents into production.
Staging environment for enterprise AI agents
Chronicle Labs provides a staging environment for enterprise AI agents by capturing production events and replaying them for testing. This enables teams to test new agent behaviors safely without deploying changes directly to production.
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Real-world sandboxes to test agents and agent-facing software
Monitoring and learning layer for long-running agents
