Simulation and evaluation platform for your agents

Evaluation is how you systematically measure and improve the quality and performance of your AI agents.

Maxim AI provides end-to-end evaluation across the entire agent development lifecycle, from prototype to production.

• Pre-Production Evaluation (Offline Evaluation): Before deployment, teams can evaluate their AI agents using curated datasets or simulated real-world scenarios across different user personas. 
• In-Production Evaluation (Online Evaluation): Assess AI performance in real-time at different granularities to maintain quality and reliability in production.

By supporting both offline and online evaluations, Maxim enables you to ship AI agents with the quality and speed required for real-world use.

You can run offline evaluations on multi-turn agent trajectories in two common ways:

• With traces: The first one is by bringing in partial traces, and evaluating the next step. For instance, if you want to evaluate the nth step, you bring in the previous n-1 traces. In other words, you can replay n-1 steps of the conversation and then evaluate the next one. 
• AI-powered simulations: Generate realistic user interactions at scale to evaluate your agents across diverse real-world scenarios and user personas. Simulations help you identify behavioral trends, spot potential failure points, and validate agent performance without requiring live user traffic.

(See: Learn more about simulation here.)

Maxim’s unified evaluation framework supports both pre-built evaluators and custom evaluators. 

Custom evaluators are quality metrics tuned to your specific outcomes and can be created across multiple types:

• AI-based Evaluators: Build LLM-as-judge evaluators using different models and parameters based on your requirements. 
• Human Evaluators: Set up human raters to review and assess AI outputs, capturing nuanced quality control across the full lifecycle.
• Programmatic/API-based Evaluators: Integrate code-based or API-driven checks for objective, deterministic assessments.

Maxim allows teams to version custom evaluators to tune outcomes and align them to human preferences as AI agents evolve.

Maxim provides a collection of pre-built evaluators in the Evaluator Store that you can use immediately for your AI evaluation needs. These include high-quality evaluators from Maxim and popular third-party evaluators like Google, Vertex, OpenAI. 

(See: Learn more about the evaluator store here.)

Maxim enables teams to build automated evaluation pipelines that integrate directly into CI/CD workflows to validate quality on every code or prompt change. The integration is powered by Maxim's SDKs (Python, TypeScript, Java, and Go) and REST APIs, allowing teams to programmatically trigger test runs. Maxim integrates with popular CI/CD systems, including GitHub Actions, Jenkins, and CircleCI. Teams can automate both prompt and agent evaluations to catch regressions and enforce quality checks before any change reaches production.

For implementation examples, step-by-step guides, and best practices, developers can reference the official documentation or GitHub repository.

Yes, Maxim provides three flexible ways to build and maintain evaluation datasets:

• Curate dataset from production: Filter real user interactions and human feedback to capture edge cases, failure modes, and high-value scenarios that reflect actual usage patterns.
• Generate synthetically: Create test datasets automatically with custom configurations for your use case including inputs, expected outputs, scenarios, personas, and expected steps. You can generate from scratch or use existing datasets as reference context.
• Import existing datasets: Bring in datasets from CSV files, external sources, or other evaluation platforms.

Yes, Maxim provides comprehensive support for human-in-the-loop workflows across the AI development lifecycle. You can leverage internal or external domain experts seamlessly on the platform to:

• Balance auto-evals with the last mile of human reviews: While LLM-judges or programmatic evals provide scale, human evaluations capture nuanced quality signals that auto evals might miss.
• Curate golden datasets: Human-annotated datasets are key to defining what "good" means for your specific use case, forming the foundation for effective offline evaluation.
• Align LLM judges:  LLM judges must be aligned with human preferences continuously to ensure they are tuned to your agent-specific outcomes.