Foundations of Agentic AI
Master the foundations of Agentic AI — understand autonomy, goal-oriented planning, reasoning, adaptability, context awareness, and the core components of compiled graphs.
Agentic AI represents a massive shift from passive chat completions to active, goal-driven systems. Instead of generating a single output response based on a prompt, Agentic AI systems plan tasks, select and execute tools, handle errors, adapt to changing environmental conditions, and coordinate multiple processes. This module establishes the conceptual foundation of Agentic AI.
What is Agentic AI?
Why this matters
Agentic AI goes beyond one-shot generation — agents plan, use tools, and loop until a goal is met.
Agentic AI systems pursue goals through planning, tool use, and feedback loops — not just single prompt-response turns like classic chatbots.
- vs Generative AI: GenAI generates content; agents act in environments.
- vs AI Agent: "Agentic" emphasizes orchestration patterns (graphs, memory, HITL).
- Agentic RAG: retrieval becomes a tool the agent chooses when needed.
Common mistakes
- Forgetting to compile the graph with a checkpointer when persistence is required.
- List state fields without reducers — updates overwrite instead of append.
- Infinite loops in cyclic graphs with no max iteration or termination edge.
Interview checkpoints
- Q: Explain agentic ai in LangGraph. A: One-sentence definition + one API.
- Q: Common bug? A: State, checkpointer, or routing loop issue.
Practice
- Basic: Sketch a minimal agentic ai example.
- Intermediate: Run a notebook cell demonstrating Agentic AI.
- Advanced: Break Agentic AI intentionally and read the LangSmith trace.
Recap
- You can explain agentic ai clearly.
- You know one mistake to avoid.
- You see how this connects to the next lesson.
Next: Agent Components
Agent Components & Prerequisites
Why this matters
Understanding agent components (LLM, tools, memory, orchestration) maps directly to LangGraph node design.
Production agents combine an LLM, tools, memory, and an orchestrator (LangGraph).
- Prerequisites: Python, LangChain basics, API keys, async concepts.
- Recruiter flow example: parse JD → search candidates → score → draft outreach.
- Glossary: node, edge, state, checkpoint, thread, subgraph.
Common mistakes
- Forgetting to compile the graph with a checkpointer when persistence is required.
- List state fields without reducers — updates overwrite instead of append.
- Infinite loops in cyclic graphs with no max iteration or termination edge.
Interview checkpoints
- Q: Explain agent components in LangGraph. A: One-sentence definition + one API.
- Q: Common bug? A: State, checkpointer, or routing loop issue.
Practice
- Basic: Sketch a minimal agent components example.
- Intermediate: Run a notebook cell demonstrating Agent Components.
- Advanced: Break Agent Components intentionally and read the LangSmith trace.
Recap
- You can explain agent components clearly.
- You know one mistake to avoid.
- You see how this connects to the next lesson.
Next: StateGraph