Learn AI agents, tool calling, workflows, memory, planning, RAG agents, multi-agent systems, LangGraph, MCP and production agent engineering.
What an agent actually is — and clear lines vs chatbots, workflows and RAG.
The agent loop — goals, context, decisions, actions and observations.
Deterministic vs dynamic pipelines, and when a workflow beats an agent.
How an agent selects, validates and executes tools safely.
Short-term, long-term, semantic and episodic memory patterns.
State management, persistence, checkpoints and recovery.
Task decomposition, plan-and-execute, and reflection patterns.
Agentic retrieval — deciding when and how to search, not just retrieving once.
Supervisor patterns, handoffs, and when multiple agents beat one.
Routing, supervision and human-in-the-loop approval for high-impact actions.
Building stateful agent graphs — nodes, edges, checkpointing and memory.
The Model Context Protocol — what it standardizes, and what it doesn't.
LangGraph, LangChain agents and other frameworks compared conceptually.
Evaluating trajectories and tool use, not just the final answer.
Tracing agent trajectories, tool calls, cost and latency.
Prompt injection, least privilege, sandboxing and credential security for agents.
Preventing infinite loops, handling tool failures, retries and fallbacks.
Deploying, scaling, versioning and cost-optimizing agents in production.
Design patterns and engineering tradeoffs for agent systems.
Real-world agent roles — support, research, coding, sales and more.
Topic-wise Agentic AI interview questions with explained answers.
Implementation-oriented exercises across tools, memory, RAG and MCP.
End-to-end agent builds — research, support, SQL analyst, multi-agent systems.
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The shift from a single LLM response to a decision loop that can take actions.
The five minimum ingredients of an AI agent, with a simplified example loop.
The full agent loop from goal to final response, with a worked example.
Why agentic AI is a usage pattern built on top of generative AI, not a separate thing.
The real distinction: converses vs takes actions — and why it matters.
Fixed sequences vs dynamic decision-making, and when to choose each.
Rule-based automation vs LLM reasoning, and how they work together.
Fixed retrieval-then-generate vs an agent that decides when and how to retrieve.
Real use cases where the correct steps can't be known in advance.
Compounding errors, cost, and the real safety stakes of agents that take actions.
The full architecture diagram, with links to every individual piece.
The four structural software components of a real agent system.
The decide-act-observe iteration cycle, and why an iteration cap is essential.
What persists across loop iterations, and how it differs from memory.
What actually goes into the LLM's prompt at each decision point.
Why goal specificity drives agent reliability, and goals vs sub-goals.
The fixed action space an agent can choose from, and how to design it safely.
How tool results get formatted and fed back into the agent's context.
What actually drives an agent's next-action decision, and structured decision formats.
Plain loops vs state machines vs graph-based frameworks for running an agent.
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