LLM function calling explained: JSON-schema tool definitions, parallel tool calls, the agent loop, routing, validation and failure handling in a production tool-use architecture.
How an AI scientist works: an LLM planner that generates hypotheses, decomposes goals, calls simulators and tools, dispatches to a lab, and verifies results — with memory and guardrails.
MCP server security architecture: tool poisoning, indirect prompt injection, OAuth 2.1, sandboxing, least privilege, and a defense-in-depth model for Model Context Protocol.
Context engineering patterns for production LLM agents in 2026 — retrieval, compaction, memory tiers, tool-result pruning, and what breaks at long horizons.
How Anthropic Claude Skills inject narrow, on-demand capabilities into production LLM agents without bloating the system prompt. Architecture, patterns, trade-offs.