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AI agents go beyond simple prompt-response interactions — they plan, reason, use tools, and take autonomous action. This session covers the full arc of working with agentic AI: from core concepts and architectural patterns to building functional agents with LangGraph, through to production deployment and enterprise governance. You'll see hands-on demos using LangGraph alongside practical coverage of how agents connect to external resources via MCP. Whether you're evaluating agentic AI for your organization or ready to start building, you'll leave with a clear, grounded picture of how these systems work in production.
- What makes AI "agentic" — core concepts and capabilities
- Anatomy of an agent: LLMs, tools, memory, and planning
- Architectural patterns: ReAct, plan-and-execute, and multi-step reasoning
- Why LangGraph: graph-based orchestration and stateful workflows
- Defining agent goals and system prompts effectively
- Implementing tool use: APIs, databases, and file systems
- Connecting agents to external resources with MCP
- Managing state, conversation history, and context windows
- Handling failures, hallucinations, and infinite loops
- Agent governance: observability, authorization, and drift detection
- Deployment strategies for production agentic systems
- Demo: building and running a functional LangGraph agent end to end
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