With Maxim, you can identify hallucinations in LLM outputs using structured evaluations and by comparing outputs across different model configurations. The platform also supports human-in-the-loop feedback, helping you detect inaccuracies and improve response reliability before deploying to production.
Maxim enables production-grade deployment of prompts using its SDK. You can configure dynamic deployment variables, apply conditional logic, and integrate prompts directly into your application stack. A/B testing tools allow you to compare prompt variants in live settings, with observability features to monitor behavior and performance post-deployment.
AI agents are autonomous workflows composed of prompts, logic, and tools. Maxim’s AI workflow builder (Agents) lets you prototype and evaluate your agents in a drag-and-drop interface.
(See: Overview, Prompt Chains)
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)
Yes. Maxim supports native integrations with leading agent orchestration frameworks and LLM stacks. You can add monitoring and observability to your workflows without needing to refactor application logic.
Yes. Maxim is OTel-compatible, allowing you to forward traces, logs, and evaluation data to third-party observability platforms like New Relic, Grafana, or Datadog. This helps unify traditional and AI observability under a single pane of glass.
(See: Maxim OTel Blog)
Yes. Maxim offers online evaluators that continuously assess real-world agent interactions. You can evaluate sessions or spans using automated metrics like faithfulness, toxicity, helpfulness, or define your own criteria. These scores help identify drift or emerging quality issues without waiting for batch test runs.