Agentic Ai
Enterprise Agentic Workflows with PydanticAI & LangGraph

Harness PydanticAI’s type-safe agents and LangGraph’s graph-based orchestration to design, build, and deploy scalable, enterprise-ready AI workflows.

5/5

Level

Advanced

Duration

8 weeks

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About Enterprise Agentic Workflows with PydanticAI & LangGraph?

Enterprise Agentic Workflows with PydanticAI and LangGraph empower organizations to build scalable, reliable, and production-ready AI agents. By combining structured data validation, state management, and intelligent orchestration, businesses can automate complex enterprise processes efficiently. At jast tech, we provide hands-on training to design, deploy, and optimize enterprise-grade agentic AI workflows for real-world applications.

Job Roles You Can Achieve
After completing this course
  • Solutions Architect
  • Technical Consultant
  • Implementation Specialist
  • System Administrator
  • IT Professional

Enterprise Agentic Workflows with PydanticAI & LangGraph Curriculum

1
Module 01

Introduction to Agentic Workflows

Understand what agentic systems are, the need for structured workflows in enterprise AI, and how agents differ from simple LLM prompts.

What is an AI agent?
Agentic vs traditional LLM use
Enterprise needs and challenges
Course roadmap
2
Module 02

PydanticAI Fundamentals

Learn the core of PydanticAI: type safety, structured outputs, agent classes, and Pythonic design for production workflows.

Installing and setup
Agents and type models
Dependency Injection and tools
Observability: LogFire integration
3
Module 03

LangGraph Fundamentals

Explore LangGraph’s graph-oriented paradigm — nodes, edges, shared state, loops, branches, and memory — to build orchestrated workflows.

Graph basics: nodes & edges
Persistent state and memory
Branching and cycles
Debugging and visualization
4
Module 04

Building Your First Agent

Hands-on with PydanticAI to create your first autonomous agent with structured outputs and tool integration.

Define agent instructions
Model integration (OpenAI, Anthropic, etc.)
Running and evaluating agent outputs
Handling errors and retries
5
Module 05

Orchestration with LangGraph

Learn how to design multi-step workflows where PydanticAI agents coordinate via LangGraph state and logic.

Mapping workflows to graphs
State propagation through nodes
Conditional transitions
Tool and API integration

Related Courses

Training Roadmap

Seven intentional milestones — from first session to dream job.

Onboarding

01
  • Meet your industry mentor
  • Define your goals
  • Skill gap assessment

Core Learning

02
  • Live interactive classes
  • AI-curated content
  • Recorded sessions

Hands-on Practice

03
  • Weekly assignments
  • MCQ evaluations
  • Module quizzes

Real Projects

04
  • 3 live industry projects
  • Portfolio building
  • Case studies

Mentorship

05
  • 1:1 doubt sessions
  • Peer collaboration
  • Expert feedback

Certification

06
  • Exam preparation
  • Practice dumps
  • Industry-recognised certificate

Career Launch

07
  • Resume crafting
  • Mock interviews
  • Job placement support

Key Projects

01

Enterprise AI Research & Decision Orchestration System

This project focuses on building an enterprise-grade AI research and decision orchestration system using PydanticAI and LangGraph. It manages the complete workflow from user query intake to multi-step research, reasoning...
02

Automated Enterprise Document Review & Compliance Workflow

This project focuses on developing an automated document review and compliance management system using agentic workflows powered by PydanticAI and LangGraph. It handles the end-to-end lifecycle from document ingestion to...
03

Intelligent Customer Support Case Resolution System

This project focuses on building an intelligent customer support case resolution platform using coordinated AI agents with PydanticAI and LangGraph. It manages the complete support lifecycle from ticket intake to categor...

Available Course Schedules

Select a schedule that works best for you

Next batch starting soon — Inquire for exact dates
Weekend

Starts

10 Oct 2026

Time

09:30 AM – 12:30 PM

Duration

8 weeks

Weekdays

Starts

12 Oct 2026

Time

07:00 AM – 09:00 AM

Duration

8 weeks

Weekend

Starts

17 Oct 2026

Time

02:00 PM – 05:00 PM

Duration

8 weeks

Weekdays

Starts

19 Oct 2026

Time

08:00 PM – 10:00 PM

Duration

8 weeks

Need a custom schedule?

Our team will craft the perfect batch for you.

What Our Happy Students Say

Real Feedback from our candidates

What We Offer Beyond Courses

24/7 Support

Round-the-clock assistance

LinkedIn Profile

Professional profile building

Resume Writing

Expert resume crafting

Alumni Guidance

Mentorship from graduates

Interview Prep

Mock interviews & tips

Live Projects

Real-world experience

Tejas Kumar

Tejas Kumar

Guidewire Developer • Cognizant

"The practical hands-on scenarios and real-world project assignments made all the difference. I was able to clear the technical interviews on my first attempt."

Verified PlacementJastTech Alumni
Sakshi Singh

Sakshi Singh

PolicyCenter Specialist • Deloitte

"Excellent mentors with deep industry domain knowledge. The LMS access and 24/7 doubt resolution gave me the confidence to transition into a high-paying role."

Verified PlacementJastTech Alumni
Sanjay Patel

Sanjay Patel

ClaimCenter Consultant • PwC India

"This course completely transformed my career trajectory! From resume review to mock interviews, the placement team supported me every step of the way."

Verified PlacementJastTech Alumni

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Certification Details

Enterprise Agentic Workflows with PydanticAI & LangGraph – Associate

  • Exam Name

    Enterprise Agentic Workflows with PydanticAI & LangGraph – Associate

  • Duration

    130 minutes

  • Format

    Multiple Choice & Multi-Response

  • Passing Score

    720 (Scale: 100–1000)

  • Level

    Associate

Certificate of Completion

Prepare

Top Interview Questions

Curated questions with expert answers to help you ace your next interview.

1. What advantages does PydanticAI offer over traditional LLM wrappers?

It provides type safety and structured output validation, reducing runtime errors and improving developer confidence.

2. How does LangGraph manage state across a workflow?

LangGraph uses shared state objects that flow through nodes, enabling persistent and contextual information through multi-step execution.

3. Why use graph-based orchestration for agentic workflows?

Graph orchestration supports loops, branching, and complex decision logic — critical for real-world, enterprise workflows.

4. What mechanisms enable human-in-the-loop control?

Workflows can pause at designated nodes awaiting approval or feedback before moving forward.

5. How would you debug a complex multi-agent workflow?

Use observability tools (logs, traces), state inspection, and replay runs — plus integrated dashboards (e.g., LangSmith + LogFire).

Support

Frequently Asked FAQs

Can't find what you're looking for? Reach out to our support team anytime.

1. What is the difference between PydanticAI and LangGraph?

PydanticAI is a Python agent framework focusing on type-safe agent building with structured outputs, while LangGraph is a graph-based orchestration engine designed for complex, stateful workflows.

2. Do I need to know Python to take this course?

Yes — familiarity with Python (especially Python typing and async) is essential since both PydanticAI and LangGraph are Python centric.

3. Can PydanticAI work without LangGraph?

Yes — simple agents and workflows can be built with PydanticAI alone, but LangGraph adds orchestration power for multi-step enterprise workflows.

4. Are these tools production-ready for enterprise use?

Yes — PydanticAI and LangGraph are designed for production use with observability, state persistence, and robust workflow control.

5. Can I deploy agentic workflows to cloud services?

Absolutely — workflows can be deployed to cloud servers (e.g., AWS, GCP, Azure) using standard Python deployments and containerization strategies from Module 9.

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