13 docs tagged with "ai-agents"

Understand what AGENTS.md is, which AI coding agents support it, and why keeping it short produces better agent outcomes than exhaustive files.

Common questions about the AGENTS.md standard, placement, length, and how it compares to CLAUDE.md.

Common questions about monitoring AI agents with Atlan, including how it compares to LLM observability tools and what data Atlan can surface.

Understand why monitoring production AI agents requires two complementary layers, and how Atlan's data-side observability complements LLM observability tools.

Understand what a context layer is, how MCP fits into the delivery model, and what changes when you build one at enterprise scale.

Common questions about context layers, enterprise rollouts, memory layers, and how they compare to RAG, semantic layers, and vector databases.

Build your first context layer for one domain, one team, and one AI client using Atlan connectors, context agents, and the Atlan MCP server.

Build an enterprise context layer for AI agents across multiple domains, governed for compliance, using Atlan Context Studio and the Atlan MCP server.

Add data-side observability to production AI agents with Atlan, covering lineage, access enforcement, and audit logs. Pair with an LLM observability tool.

Build long-term memory for AI agents with Atlan context repositories and the Atlan MCP server. Covers prerequisites, MCP wiring, and validation.

Write an AGENTS.md file for AI coding agents (Codex, Cursor, Copilot, Aider, Windsurf, Zed). Spec, examples, required and optional sections, and pitfalls.

Understand how a memory layer maps to Atlan's architecture, and how context repositories and the Atlan MCP server compare to vector databases and RAG.

Connect Claude, Cursor, ChatGPT, VS Code, Copilot, Gemini, Snowflake, Databricks, and other AI clients to the Atlan MCP server. One page, pick your client, get step-by-step setup.