Agents

Agent Skills are a portable, open-standard format for packaging procedural knowledge so any AI agent can load domain expertise on demand—turning a general-purpose model into a specialist without re-explaining workflows every time.

Prompt engineering describes the recipe; context engineering manages the kitchen. A technical playbook for the "mise en place" of AI agents: Write, Select, Compress, and Isolate.

The Model Context Protocol (MCP) is a standardized framework for integrating AI systems with diverse data sources and Applications. This post explores MCP’s architecture, core components, and best practices.

Explore the six essential elements that make multi-agent systems effective: Role Playing, Focus, Tools, Collaboration, Guardrails, and Memory. Learn how specialized agents working together can outperform single-agent solutions through clear roles, focused responsibilities, and powerful collaboration patterns.

Explore ReAct, a framework where language models observe, reason, and act in a continuous cycle. Learn how this three-step process enables AI to gather information, think through problems step-by-step, and take concrete actions - creating more capable and reliable AI systems that can adapt their approach based on real-world feedback.

MemGPT revolutionizes LLM capabilities by implementing operating system-like memory management, enabling persistent context and long-term learning across conversations.