maxim updates

Feature spotlight 🔌 MCP Clients on Maxim Maxim now supports the Model Context Protocol (MCP), enabling your agents to interact with external tools, access real-time data, and perform actions. Here's what you can do with MCP clients: * Connect to popular MCP providers like Composio and Gumloop, or use your

Feature spotlight 🔗 Prompt chains: Now more powerful Maxim’s revamped Prompt Chains lets you prototype every step of your complex agentic workflow with greater clarity and control—right from our more intuitive UI. Key highlights: * 🔀 Create parallel chains to execute concurrent or conditional tasks. * 🔁 Flexibly transfer data between ports—choose

Feature spotlight 🚀 Agent simulation Teams today spend countless hours manually going back and forth with their agents to assess quality and identify failure modes. And it’s not a one-time thing— unintended regressions happen all the time, whether due to model updates or optimizations based on user feedback. Introducing Maxim’

Feature spotlight 🔍 Evaluate and monitor AI agents using Maxim It is a constant challenge for AI teams to debug, monitor, and understand what's going wrong in the complex workflows of AI agents, which involve multiple steps, i.e., LLM calls, tool calls, data retrievals, etc. To ensure the

Feature spotlight 👩‍🏫 Use human annotation queues for data curation To optimize the quality and performance of AI applications, teams need to continuously inspect traces, annotate examples, and use these logs as datasets for evaluation, fine-tuning, or in-context learning. This is a manually intensive process where various components are siloed and