Simulation and evaluation platform for your agents
Save days of manual testing and tedious processes to ship reliable agents
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Simulating multi-turn conversations allows you to evaluate how your AI agent performs in real-world, back-and-forth exchanges. Maxim enables developers to test agents across a wide variety of realistic user flows and edge cases using custom personas and goal-driven dialogue paths. This helps ensure agents respond contextually and consistently under various user intents.
(See: Simulate and evaluate multi-turn conversations)
Evaluating agent performance goes beyond simple output checks. Maxim supports both automated and human-in-the-loop evaluations using customizable scoring functions, regression checks, and benchmark datasets. You can combine metrics like correctness, coherence, latency, and satisfaction to comprehensively assess agent quality.
(See: Use pre-built Evaluators, Create human evaluators, Create custom AI evaluators)
Absolutely. Maxim enables you to automate evaluations via your CI/CD pipeline using its Python SDK or REST API. You can trigger test runs after each deployment, auto-generate reports, and catch regressions before changes hit production, ensuring reliability across iterations.
(See: Trigger test runs using SDK, Maxim API overview)
Yes. Maxim allows you to combine synthetic prompts, real user logs, and annotation workflows to curate high-quality datasets. These datasets evolve alongside your agent, helping ensure evaluations reflect your users' needs and edge-case behavior over time.
(See: Curate data from production, Curate a golden dataset)
Yes. You can incorporate human reviewers at any step of your evaluation pipeline. This helps validate nuanced criteria like helpfulness, tone, or domain-specific accuracy—especially important when automated metrics fall short.
(See: Create human evaluators)
Maxim is designed for large-scale agent testing. You can evaluate across thousands of simulations, personas, and prompt variations in parallel—dramatically accelerating iteration and improving reliability before shipping.
(See: Simulate and evaluate multi-turn conversations, Run your first test on prompt chains)
