AI/LLM Cheatsheet 06 — Agents
Cheatsheet: agent loop, ReAct, tool use, memory, multi-agent.
Cheatsheet: agent loop, ReAct, tool use, memory, multi-agent.
Practical MCP: building an MCP server, integrating with Claude / Cursor, when MCP wins, and the security pitfalls of remote tool access.
Honest take on AI coding agents: where Claude Code / Cursor shine, when they hurt, the discipline of using them well, and what stays human.
Honest agent framework comparison: LangGraph for stateful workflows, CrewAI for multi-agent, OpenAI Agents SDK, and where 200 lines of Python beats them all.
Practical agent memory: working memory in the prompt, episodic memory in append-only stores, semantic memory in vector DBs, and how to compose them.
Production agent error handling. Per-tool retries vs whole-agent retries, fallback paths, step caps, escalation, human-in-the-loop, and the patterns from real agent deployments.
Tool design for agents — names, descriptions as prompts, input schemas, error handling, idempotency, and the patterns that make agents call them correctly.
Why agents need memory beyond the context window, the 2026 tools (Mem0, Zep, custom layers), summary vs episodic memory, retrieval, and the patterns from production agents.
Why agents need sandboxed code execution, the 2026 platforms (E2B, Modal, Daytona, Fly Machines, custom microVMs), tradeoffs, and how to wire it into an agent.
Why agentic RAG often beats one-shot RAG. Tool-based retrieval, decomposition, query rewriting, self-reflection, citations, and the production patterns that ship in 2026.