Bits

Microsoft Agent Framework (MAF) core concepts — Agents, Threads, Middleware, Memory, Observability — and advanced workflow patterns including Sequential, Concurrent, Handoff orchestration, Middleware injection, and Human-in-the-Loop with code examples.

Memory types for AI Agents (Working, Short-term, Long-term, Persona, Episodic, Entity, Structured RAG), implementation with Mem0, Cognee, and Azure AI Search, and the self-improving agent pattern with code examples.

How Context Engineering differs from Prompt Engineering, the five types of context in AI Agents, practical management strategies, and how to recognize and fix Context Poisoning, Distraction, Confusion, and Clash failures.

MCP, A2A, and NLWeb — three agentic protocols for standardized tool access, agent-to-agent collaboration, and natural language interfaces on the web.

How to move AI agents from prototype to production — observability with traces and spans, key metrics, OpenTelemetry instrumentation, offline/online evaluation, cost management, and a real expense claim demo.

Metacognition in AI agents — self-reflection, corrective RAG, pre-emptive context loading, goal bootstrapping, intent-aware search, code generation, and SQL as RAG.

Multi-agent design patterns — when to use them, building blocks of agent communication and coordination, Group Chat / Hand-off / Collaborative Filtering patterns, and a WorkflowBuilder pipeline walkthrough.

How to build planning agents that decompose complex goals into structured subtasks, route work to specialized agents, and iteratively re-plan based on real-world feedback.

How to build safe and trustworthy AI agents — system message framework, threat mitigation, and human-in-the-loop patterns for secure, reliable agentic systems.

2 + 2 = 5

Agentic RAG — how LLMs autonomously decide when and how to retrieve information, iterate with a maker-checker loop, self-correct, and deliver high-quality grounded responses.

How AI agents use tools via function calling — building blocks, tool schemas, approval modes, and practical examples with Microsoft Agent Framework and Azure AI Agent Service.

A guide to Agentic Design Principles (Space, Time, Core) and three practical patterns — Clear Instructions, Structured Output, and Single Responsibility — for building effective AI agents.

A comparison of Microsoft Agent Framework and Azure AI Agent Service — modular components, multi-agent collaboration, and when to use each for building production AI agents.

A breakdown of AI Agent core components (Environment, Sensors, LLM, Tools, Memory), 7 agent types, when to use agents, and a hands-on Python example with Azure AI Foundry.