Agentic AI Engineering
Build agents that actually complete tasks - MCP, computer use, coding agents, multi-agent coordination, evaluation, safety, and production deployment at every layer.
10 Modules. Foundations to Production.
From the ReAct loop to multi-agent systems - every layer of agentic AI, explained with depth.
Agentic Foundations
What agents are, why they exist, and how the observe-think-act loop enables autonomous task completion.
What you'll master
- What are AI Agents?
- Agent Loop: Observe → Think → Act
- Tool Use & Function Calling
- ReAct Pattern
- Agent vs Chatbot vs Workflow
- Agentic Design Patterns
- When to Use Agents
7 lessons
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Model Context Protocol
The open standard for connecting AI applications to tools - solving the N×M integration problem once and for all.
What you'll master
- What is MCP & the N×M Problem
- MCP Architecture: Client-Server
- Tools, Resources & Prompts
- Building an MCP Server
- MCP Security & Permissions
- MCP Ecosystem & Servers
- MCP vs Function Calling
7 lessons
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Computer Use Agents
Agents that operate browsers, GUIs, and desktop environments - architecture, vision models, safety, and evaluation.
What you'll master
- Computer Use Architecture
- Browser Agents
- GUI Automation with Vision
- Web Scraping Agents
- Safety & Sandboxing
- Benchmarks: WebArena & OSWorld
6 lessons
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Coding Agents
Agents that read, write, and fix code - from SWE-bench evaluation to TDD loops and building your own.
What you'll master
- How Coding Agents Work
- SWE-bench & Evaluation
- Agentic Code Editing
- Tool Use for Coding
- Test-Driven Agent Loops
- Building Your Own Coding Agent
6 lessons
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Long-Horizon Planning
Multi-step task decomposition, planning with LLMs, checkpointing, and handling ambiguity in long-running agents.
What you'll master
- Task Decomposition
- Planning with LLMs
- Checkpointing & Recovery
- Handling Ambiguity & Clarification
- Interruption & Human-in-the-Loop
- Evaluation of Long-Horizon Tasks
6 lessons
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Agent Memory
In-context, episodic, semantic, and procedural memory - how agents remember, learn, and persist across sessions.
What you'll master
- Four Types of Agent Memory
- In-Context Working Memory
- Episodic Memory with Vector Store
- Semantic Memory & Knowledge Graphs
- Procedural Memory & Learned Skills
- Memory Compression & Summarization
- Cross-Session Persistence
7 lessons
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Multi-Agent Systems
Orchestrator-worker patterns, agent communication, parallel execution, and frameworks for multi-agent coordination.
What you'll master
- Why Multi-Agent?
- Orchestrator-Subagent Pattern
- Agent Communication Protocols
- Parallel Agent Execution
- Debate & Critique Patterns
- OpenAI Swarm
- AutoGen Deep Dive
- CrewAI
- LangGraph
9 lessons
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Agent Evaluation
Benchmarks, trajectory evaluation, LLM judges, human evaluation, and production monitoring for agentic systems.
What you'll master
- Challenges of Evaluating Agents
- Trajectory Evaluation
- GAIA Benchmark
- SWE-bench Verified
- LLM-as-Agent-Judge
- Human Evaluation for Agents
- Production Agent Monitoring
7 lessons
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Agent Safety
Risk taxonomy, minimal footprint, prompt injection defense, guardrails, sandboxing, and responsible deployment.
What you'll master
- Agent Risk Taxonomy
- Minimal Footprint Principle
- Prompt Injection in Agents
- Guardrails & Action Validation
- Human Oversight Mechanisms
- Sandboxing Agent Environments
- Responsible Agentic AI
7 lessons
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Agent Frameworks
LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, raw API patterns - how to choose and how to use them in production.
What you'll master
- When to Use a Framework
- LangChain Architecture
- LangGraph for Stateful Agents
- LlamaIndex Architecture
- CrewAI Multi-Agent
- AutoGen Conversational Agents
- Raw API Agent Patterns
- Framework Comparison
- Production Lessons
9 lessons
Start for Free →
Ready to build agents that complete real tasks?
From MCP to multi-agent systems - the complete agentic AI engineering curriculum.
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