EVALUATE & IMPROVE AGENTS
Ship agents that work.
Simulate real-world agent behavior, evaluate changes before you ship, and monitor every run after launch.
Teams running AI/ML in production with Chalk
Powering the continuous loop
of agent development.
Chalk gives your team a complete platform for taking AI agents from idea to production.
Simulate point-in-time, real-world environments.
Recreate exactly what an agent would have seen at a specific point in time, including its data, tools, and conditions. Chalk controls tool access through a secure gateway, so you can enforce knowledge cutoffs and run a historical scenario at scale.
Evaluate agents before you ship.
Score outcomes, compare versions, and inspect traces to understand exactly what changed when you iterate on prompts, models, tools, and context. Identify whether changes make the agent better.
Monitor every agent run in production.
Inspect the complete execution path, including tool calls, queries, timing, and outputs. Catch failures and unexpected behavior, and then improve your agent fast.
Evaluate and improve agents
on the AI data platform for inference.
Sandboxes
Run agent tasks in isolated, gVisor-hardened environments with their own filesystem, network namespace, and resource limits. Use sandboxes for agent work, like generated-code execution or data analysis.
Agent traces
See the complete path an agent took to complete a task. Expand any run to inspect tool calls, queries, operations, timing, and outputs. Identify slow or failed steps quickly.
Evaluations, scorers, and runs
Define evaluations. Run large evaluation sets in parallel. Score results with Python or an LLM judge, compare models and versions, and drill from a high-level score into an individual agent trajectory.
Context Engine
Generate structured, time-consistent context for historical evaluation. Create versioned evaluation datasets from your historical data. Use offline queries to recreate the context an agent had at a specific point in time. Then run repeatable evaluations against the same scenarios whenever you change your agent.
Go deeper on agent evaluation.
Building an agent is easy. Deploying it confidently is hard.
Chalk was the only integrated compute and context engine for agents… What would have taken months took weeks.
AJ Balance CPO, Grindr
Bring us the agent proof-of-concept that needs to perform reliably in production.
We'll help you set up an evaluation harness that gives you the confidence to deploy your agent to production.
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