LLM app development frameworks, tutorials, and updates.
15 articles
Arcade is the MCP runtime for production agents, delivering secure agent authorization, reliable tools, and governance. This integration gives your agents access to Arcade’s collection of 7,500+ agent-optimized tools through a single secure gateway.
Most discussions of continual learning in AI focus on one thing: updating model weights. But for AI agents, learning can happen at three distinct layers: the model, the harness, and the context. Understanding the difference changes how you think about building systems that improve over time. The thr
I built a self-healing deployment pipeline for our GTM Agent. After every deploy, it detects regressions, triages whether the change caused them, and kicks off an agent to open a PR with a fix, with no manual intervention needed until review time.
💡 TL;DR: Open models like GLM-5 and MiniMax M2.7 now match closed frontier models on core agent tasks — file operations, tool use, and instruction following — at a fraction of the cost and latency. Here's what our evals show and how to start using them
It feels like spring has sprung here, and so has a new NVIDIA integration, ticket sales for Interrupt 2026, and announcing LangSmith Fleet (formerly Agent Builder).
Build production AI agents on MongoDB Atlas — with vector search, persistent memory, natural-language querying, and end-to-end observability built in.
A practical checklist for agent evaluation: error analysis, dataset construction, grader design, offline & online evals, and production readiness.
Discover how Kensho, S&P Global’s AI innovation engine, leveraged LangGraph to create its Grounding framework–a unified agentic access layer solving fragmented financial data retrieval at enterprise scale.
💡 TLDR: The best agent evals directly measure an agent behavior we care about. Here's how we source data, create metrics, and run well-scoped, targeted experiments over time to make agents more accurate and reliable. Evals shape agent behavior We’ve been curating evaluations to measure and
Agent harnesses are what help build an agent, they connect an LLM to its environment and let it do things. When you’re building an agent, it’s likely you’ll want build an application specific agent harness. “Agent Middleware” empowers you to build on
Fleet now supports shareable skills, so you equip agents across your team with knowledge for specialized tasks.
Moda uses a multi-agent system built on Deep Agents and traced through LangSmith to let non-designers create and iterate on professional-grade visuals.
If you're attending Google Cloud Next 2026 in Las Vegas this year and working on agent development, here's what we have planned. Visit Us at Booth #5006 We'll be at Booth #5006 in the Expo Hall at the Mandalay Bay Convention Center, April 22-24.
LangSmith Fleet introduces two types of agent authorization: Assistants, which use the end user's own credentials, and Claws, which use a fixed set of credentials.
Agent Builder is now Fleet: a central place for all of your teams to build, use, and manage agents across the enterprise.