Ai-Agents

Claude Code Agent Teams is an experimental feature, live since v2.1.32. It lets you run 2-16 Claude Code sessions under one team lead. Each teammate gets its own context window and full tool access. They talk through a shared task list and direct peer-to-peer messages. You turn it on with one config change, then describe the team you want in plain language. Claude handles the spawning, the assignment, and the coordination. The feature shines on work you can split up: multi-file refactors, cross-layer feature builds, and research-and-review jobs. The catch is that it costs 3-7x more tokens than a single session, and it cannot resume a session.

Agentic RAG replaces the standard “retrieve-then-generate” pattern. The LLM gets tool-use powers to decide when to retrieve, which sources to query, how to rewrite queries, and whether the result is enough. Instead of fetching docs on every query, the model acts as an orchestrator. It runs targeted searches across vector stores, SQL databases, and web sources, then checks its own answers. This pattern lifts answer accuracy by 15-25% on multi-hop benchmarks and cuts wasted retrieval calls by about 35%.

The Model Context Protocol gives LLMs a standard way to call external tools, read files, and query databases. You skip the rewrite each time you switch models. You can build a working MCP server in Python with the official mcp SDK in under 100 lines. It runs with Claude Desktop or Claude Code in minutes. This guide walks the full path, from a tiny first server to production.

What MCP Is and Why It Changes Tool Use

MCP is a JSON-RPC 2.0 protocol. It lets an LLM client (like Claude Desktop , Claude Code, or Cursor) find and call tools exposed by a server process. The big shift from older function-calling is the discovery step. Instead of hard-coding tool defs into every prompt, the client sends a tools/list request when it connects. It gets back the full schema for everything the server exposes. Add a new tool, restart the server, and any client sees it on the next connect.

Five open source repositories dropped in March 2026 that expand what Claude Code can do. Karpathy’s AutoResearch runs overnight ML experiments without you. OpenSpace makes agent skills fix and improve themselves. CLI-Anything turns GUI software into agent-ready command-line tools. Claude Peers MCP lets many Claude Code sessions coordinate on one machine. And Google Workspace CLI opens Gmail, Drive, Calendar, and Sheets to agents. All five are free, open source, and plug right into Claude Code.

Software teams keep adding AI coding agents to their workflow. One popular trend: drop a repo-level context file, often named AGENTS.md or CLAUDE.md, to guide the agent. The idea sounds clean. Give the AI a map of the codebase and a few rules, and it should solve tasks faster.

But does it work? A new paper, “Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?” , says no. The results push back hard on the default advice.