Congratulations on completing the course.
You have now gained a comprehensive understanding of building agentic AI applications with LangChain and LangGraph.
You can start exploring opportunities to continue learning and applying your acquired skills.
But before that, let's review some key concepts you've learned throughout the course.
Generative AI is a reactive system that creates content like text or images based on prompts.
It stops once it generates a response.
Agentic AI, on the other hand, is proactive and uses prompts to pursue goals through a
loop of decision-making, action, and learning.
It can operate with minimal human input, making it far more dynamic and autonomous than traditional genAI.
LangGraph structures AI workflows as flexible graphs using nodes, edges, and shared state.
It supports looping, branching, state persistence, and human-in-the-loop functionality, making
it ideal for building complex, adaptive agents.
LangChain helps developers build LLM-powered applications using modular components like
prompts, memory, and tools.
It works best for linear, step-by-step workflows.
LangGraph, on the other hand, extends LangChain's capabilities by enabling stateful, multi-agent
workflows through graphs of nodes and edges.
It's ideal for handling complex tasks that involve branching, memory, and long-term context.
Here's a breakdown of how LangGraph structures and executes stateful workflows.
TypedDict is used to define a structured state.
State can include complex types like lists, nested dictionaries, or message sequences.
Nodes are linked to functions that either transform or observe state.
Edges define transitions between nodes.
Conditional edges evaluate the current state to control flow.
Functions use state unpacking to update state immutably.
And finally, StateGraph is compiled and executed with an initial state using invoke.
Here's a quick glance at reflection agents.
Reflection agents are AI systems that iteratively improve outputs by analyzing and refining previous responses.
The process involves two core LLM roles, a generator and a reflector.
Basic reflection agents operate in feedback loops to refine results over multiple iterations.
LangChain is used to set up structured prompts and memory for each agent role.
LangGraph's message graph tracks message flow and defines agent state across turns
using message types like human message and AI message.
Next, here's a look at reflexion agents.
Reflexion agents iteratively refine AI output using self-critique and external tools.
They differ from basic reflection by integrating real-time data and generating structured, cited responses.
Outputs follow a schema with labeled fields like response, critique, and citations.
This loop continues until a verifiable result is achieved.
Next up are ReAct agents, which reason iteratively and only use tools when necessary.
Their structured response flow is Thought, Action, Action Input, Observation, and Final Answer.
The agent maintains message history to inform each step of reasoning.
Execution stops when no further tool calls are required, yielding the final answer.
Here's a look at multi-agent systems, which consist of autonomous agents with specialized roles.
They interact through graph-structured workflows using collaboration patterns like pipelines
and hub-and-spoke models.
Generalist agents often coordinate specialized agents, balancing depth of expertise with broad task management.
Orchestration frameworks such as LangGraph, CrewAI, AutoGen, and BeeAI enable scalable and
modular agent collaboration.
Agentic RAG enhances RAG by letting an LLM act as a decision-making agent, not just a responder.
The agents select the most relevant data source based on query context.
It boosts accuracy, adaptability, and real-world applicability across industries.
If you haven't yet enrolled in the Professional Certificate Program, of which this course
is a part, we encourage you to do so.
Depending on your schedule and the number of courses in the program, you can complete
it in approximately two to six months.
We recommend that you continue applying the knowledge gained from this course in your
agentic AI development career.
We hope these principles will refine your skills and empower you to advance professionally.
Congratulations on completing this course!
We appreciate your participation in this learning journey and wish you all the best!