Learn enterprise agentic AI workflows using PydanticAI and LangGraph. Build scalable, multi-agent systems with hands-on, production-ready training by Tech Pratham.
Level
Advanced
Duration
8 Weeks
Placement Client
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About AgentOps & Production Reliability (LLM-Ops 2.0)
Enterprise Agentic Workflows with PydanticAI & LangGraph is a hands-on course by Tech Pratham that teaches professionals to build scalable, autonomous AI systems for enterprises. Learn to design intelligent agents using PydanticAI for structured outputs and reliable validation, and LangGraph for graph-based workflow orchestration and multi-agent collaboration. The course covers enterprise automation, integration with business systems, production deployment, security, and governance, with real-world projects that prepare learners to implement agentic AI workflows that enhance efficiency, decision-making, and business productivity.
Training Plan
About trainer
Working professional who is carrying more then 10 years of industry experience.
Decks & Updated Content
Access to updated presentation decks shared during live training sessions.
e-Book
E-book provided by TechPratham. All rights reserved.
Assignments & MCQs
Module-wise assignments and MCQs provided for practice.
Video Recording
Daily Session would be recorded and shared to the candidate.
Projects
Live projects will be provided for hands-on practice.
Resume Building
Expert-guided resume building with industry-focused content support.
Interview Preparation
Comprehensive interview preparation with real-time scenario practice.
AgentOps & Production Reliability (LLM-Ops 2.0) Course Curriculum
Introduction to Agentic AI
Understand the fundamentals of Agentic AI and its role in enterprise workflow automation with Tech Pratham.
Python & AI Prerequisites
Learn essential Python, APIs, and LLM basics required to build scalable agentic AI systems.
PydanticAI Fundamentals
Master PydanticAI for structured outputs, reliable validation, and enterprise-ready AI agents.
Designing Intelligent AI Agents
Build autonomous AI agents with decision-making, tool integration, and multi-step task planning.
LangGraph Core Concepts
Use LangGraph to orchestrate graph-based workflows, conditional logic, and agent coordination.
Multi-Agent Orchestration
Design and manage collaborative multi-agent systems for large-scale enterprise workflows.
Agentic Ai Courses
No related courses found
Additional Program Highlights
Upcoming Batches
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Who Should Take AgentOps & Production Reliability (LLM-Ops 2.0)
IT Professionals
Non-IT Career Switchers
Fresh Graduates
Working Professionals
Ops/Administrators/HR
Developers
BA/QA Engineers
Cloud / Infra
IT Professionals
Non-IT Career Switchers
Fresh Graduates
Job Roles For AgentOps & Production Reliability (LLM-Ops 2.0)
AI Observability Engineer
LLM Deployment Engineer
AI DevOps Engineer
AI Infrastructure Engineer
MLOps Engineer
AI Platform Engineer
AI Production Reliability Engineer
AgentOps Engineer
AI Observability Engineer
LLM Deployment Engineer
AI DevOps Engineer
Key Projects
AgentOps & Production Reliability (LLM-Ops 2.0)
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Workday – Workday AgentOps Monitor
Scenario: AgentOps framework deployed to monitor AI agents in HR workflows, capturing failures, latency and drift while enforcing SLAs and ensuring production stability.
Live Work:
- Implemented agent health checks
- Built latency and error dashboards
- Defined rollback for failed agents
Microsoft – Microsoft AI Reliability Ops
Scenario: Production AgentOps solution ensuring AI reliability by tracking execution metrics, validating outputs, and managing safe recovery across enterprise workloads.
Live Work:
- Tracked agent execution metrics
- Added output quality validation
- Automated incident alert handling
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Salesforce – Salesforce Agent Stability Lab
Scenario: AgentOps pipeline built to supervise CRM AI agents, detect performance drops, log decisions, and maintain reliable automation across sales and service flows.
Live Work:
- Logged agent decisions centrally
- Detected drift in AI responses
- Tuned agents for peak load use
UiPath – UiPath Agent Reliability Ops
Scenario: AgentOps layer integrated with RPA bots to monitor AI decisions, manage failures, and ensure reliable automation execution in production environments.
Live Work:
- Monitored AI driven bot actions
- Implemented safe fallback logic
- Analyzed failures for root cause
Placement Process
Live Class with Industry Use Cases
- Interactive learning
- Real-world examples
- Expert guidance
- Doubt resolution
Daily Assignment & Weekly Assessments
- Hands-on practice
- Concept reinforcement
- Weekly evaluation
- Timely feedback
Project Testing & Deployment
- Project development
- Testing practice
- Deployment exposure
- Portfolio creation
Interview Preparation (Mock, PD, Alumni Session)
- Mock interviews
- Personal development
- Alumni guidance
- Confidence building
Resume Building
- Resume crafting
- Industry-focused format
- Skills highlighting
- Professional presentation
Placement
- Job assistance
- Interview support
- Career guidance
- Opportunity access
Knowledge Center
Recently Placed Candidates
We’re Hiring
News Highlights
Our Success Mantra
Commitment
- Ensuring quality training every day
Fulfillment
- Meeting learning goals with confidence
Accomplishment
- Students achieving industry-ready expertise
Our Learner Voice
Beyond Courses:
Additional Support We Provide
24/7 Support
LinkedIn Profile
Resume Writing
Alumni Sessions
Interview Preparation
Live Projects
What Makes TechPratham Training Different?
What is AgentOps & Production Reliability (LLM-Ops 2.0)?
Why is production reliability important for LLM-based systems?
Who should learn LLM-Ops 2.0 and AgentOps?
How does AgentOps improve multi-agent workflows?
Is prior AI or MLOps experience required?
What enterprise problems does LLM-Ops 2.0 solve?
Industry-Recognized Certification
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