AI Agents Resources

Curated, high-trust sources for the AI-agents mental models in this workspace. Knowledge in the lessons is drawn from here, not from parametric guesses.

Knowledge

• Article: “Building effective agents” — Anthropic Engineering The canonical, vendor-neutral framing. Defines the augmented LLM (LLM + retrieval + tools + memory), and the workflow vs agent distinction used throughout Level 7. Strong “start simple, add complexity only when it pays” guidance. Use for: Levels 1–7. Read this first.

• Article: “How we built our multi-agent research system” — Anthropic Engineering (Jun 2025) Real-world orchestrator-worker case study: lead agent + 3–5 parallel subagents, ~15× the tokens of a chat, 90.2% better than single-agent on their research eval. Use for: Level 9 (multi-agent), and the “don’t reach for multi-agent too early” trade-off.

• Docs: Model Context Protocol — modelcontextprotocol.io Primary source for MCP. “A USB-C port for AI applications.” Defines the open standard, MCP servers (expose tools/data/prompts) and clients (connect from the host app). Use for: Level 8. Architecture deep-dive: https://modelcontextprotocol.io/docs/learn/architecture.

• Docs: Anthropic — Tool use (function calling) How tool calling actually works on the wire: you advertise tools with JSON-schema, the model returns a structured tool_use request, your code runs it and returns a tool_result. Use for: Level 2. (OpenAI’s equivalent “function calling” guide is an alternative second source.)

• Docs: Claude Agent SDK The runtime/loop made concrete — the harness that holds history, dispatches tools, enforces limits. Use for: Levels 4–6 (agent loop, runtime, memory) when you want to see the loop in code.

Stage 2 sources

• Docs: Claude Code — Anthropic Canonical source for how Claude Code works: read-on-demand tool use, filesystem/bash/git tools, planning mode, MCP server connections. Use for: Module 4 (how Claude Code works).

• Docs: Cursor — cursor.com/docs Primary source for Cursor’s architecture: codebase indexing (embeddings), retrieval, context assembly, agent mode. Covers documented behavior only — no proprietary internals. Use for: Module 5.

• Spec: Model Context Protocol — modelcontextprotocol.io Canonical MCP spec: Tools (invocable functions), Resources (URI-addressed data), Prompts (templates), transports (stdio, SSE, WebSocket), server/client lifecycle, tool discovery. Use for: Module 6.

• OWASP LLM Top 10 The authoritative list of LLM/agent security risks — prompt injection (#1), insecure tool invocation, data exfiltration via agents. Use for: Module 8 (production security).

Wisdom (Communities)

• r/LocalLLaMA and r/AI_Agents High-volume practitioner discussion. Use for: sanity-checking vendor claims, seeing what breaks in production. Filter hard — signal-to-noise is mixed.
• MCP community / GitHub discussions Use for: how teams actually expose and consume tools; reference server implementations to read.
• Not yet selected: a high-signal, low-hype venue for engineering-leadership discussion of agents (architecture, build-vs-buy). See Gaps.

Gaps

• No single high-trust source yet for agent evaluation / reliability in production (how you’d prove an agent is safe to ship). Mission lists eval as out-of-scope for now, but a VP will need it soon — flag for a future search.
• No vetted leadership-level community found yet (most venues skew IC/hobbyist or pure hype).