Production patterns for structured prompting. How to use XML tags, JSON schemas, Pydantic output types, and chain-of-thought to get reliable, parseable output from AI agents in 2026.

Your MCP tool definitions are prompts too. How tool names, descriptions, input schemas, and error messages influence agent decision-making. Production patterns for writing tools that steer agent behavior.

How prompt engineering has evolved in the agent era. Compare system prompts, Kiro steering files, Claude Code .claude.md rules, Cursor .cursorrules, and Agent SDK instructions. Production patterns for each approach.

The definitive comparison of 5 major AI agent frameworks in 2026. Production benchmarks, decision matrix, code examples, and when to choose each framework for your specific use case.

Deep dive into the Claude Agent SDK — Anthropic's first-class agent framework with native MCP, tools-as-resources, computer use, and the agent loop pattern. Build agents that are MCP-native from the ground up.

Deep dive into the OpenAI Agents SDK — handoffs between specialized agents, built-in guardrails, input/output validation, safety filters, and production deployment with OpenAI's ecosystem.

Deep dive into Microsoft's AutoGen — rebuilt from scratch in 2025 for production. Agent conversations, code generation agents, group chats, tool use, and real-world deployment patterns with Azure.

Deep dive into CrewAI — the role-based multi-agent framework that went from zero to 32k+ GitHub stars in under two years. Agent roles, crews, task delegation, tool integration, and when to choose it over LangGraph.