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Certified Agentic AI Engineering Program
Build production-grade AI agents, RAG pipelines and multi-agent systems that think, plan and execute.
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Program Highlights for Agentic AI Engineers

Live Sessions
61+ hours of live, instructor-led training, with every session taught by industry experts.
Industry Tools
Work with 10+ industry tools, including LangChain, CrewAI, LangGraph, MCP SDK and FastAPI.
Hands-On Projects
Build 12+ AI systems, from AI agents and RAG systems to orchestration workflows and enterprise AI platforms.
Advanced RAG Engineering
Go deep on embeddings and vector databases, chunking strategies and hybrid search for RAG systems.
Multi-Agent Systems
Build AI agents that reason, use tools, collaborate with each other and handle complex tasks.
Production Optimization
Learn fine-tuning, and how to improve the performance, cost efficiency and reliability of AI systems.
Enterprise Capstone
Design and present an enterprise-level AI agent system, with its architecture documentation.
Career Mentorship
Career mentorship, portfolio guidance and interview preparation for AI Engineer, GenAI Engineer and LLM Engineer roles.
The Techademy Edge

Guaranteed-to-Run Workshops
Our Agentic AI Engineering program is guaranteed to run on the scheduled dates, for uninterrupted learning and timely certification.

Fun-filled, Interactive Sessions
Learn through engaging live sessions focused on building real AI systems, autonomous workflows and enterprise-grade AI applications.

Hands-on Learning
Gain production-level experience by building RAG systems, AI agents, orchestration workflows, APIs and deployable AI applications.

Unlimited Support
Get continued access to updated learning resources, community support and mentorship as AI technology evolves.

Immediate Career ROI
Build portfolio-ready AI systems and practical deployment experience that strengthen your profile for AI engineering roles.

Credible & Trusted Name
With 750,000+ career transitions and 650+ enterprise transformations, Techademy is trusted globally for future-ready AI upskilling.
Tuition Fee
Live Online Classroom
61+ hours of live, interactive training
Hands-on projects and a capstone
10+ tools and frameworks
Lifetime access to learning resources
Career mentorship and interview prep
Lifetime access to session recordings
Our support system for AI engineers

AI & GenAI Engineering Expert

Agentic AI & AI Engineering Expert
Curriculum for Agentic AI Engineers
Ten modules, from Python and LLM foundations to MCP and modern AI infrastructure.
Pre-Module: Foundations Track
Hands-on: practice Python and API workflows, explore embeddings and AI pipelines, and learn cloud and database integrations. Tools: Python, APIs, GitHub and SQL.
Python fundamentals for AI engineering
Variables, functions, OOP basics & working with APIs
Statistics & math foundations for ML
Probability, distributions, correlation & variance basics
Supervised vs unsupervised ML fundamentals
LLMs, transformers, tokens, embeddings & prompting basics
SQL, NoSQL, cloud, APIs, storage & compute fundamentals
Python, APIs & LLM SDK Foundations
Hands-on: build an LLM API client with async calls, streaming responses and a structured response parser. Tools: Python, OpenAI SDK, Anthropic SDK, Gemini SDK and httpx.
Functions, OOP, async programming & type hints
REST APIs, HTTP methods, authentication & rate limiting
OpenAI, Anthropic & Gemini SDK integrations
Streaming, token cost management & safe API handling
Retry logic, timeout handling & logging strategies
Async concurrency & event-driven execution workflows
Prompt Engineering & Structured Output Systems
Hands-on: build a structured output generator, add schema validation, create evaluation workflows and simulate prompt injection attacks. Tools: Pydantic, PromptLayer and LangChain PromptTemplate.
Prompt anatomy & instruction hierarchy
Context engineering & role prompting
Few-shot, chain-of-thought, tree-of-thought & ReAct prompting
JSON schema enforcement & typed outputs
Pydantic integration & output validation
Prompt injection defence & jailbreak detection
Safety boundaries & red-team testing
RAG Engineering Foundations
Hands-on: build a document ingestion pipeline and a vector search workflow, apply chunking strategies and build a RAG prototype. Tools: FAISS, ChromaDB, Pinecone and LangChain.
RAG architecture & retrieval pipelines
Knowledge freshness & query transformation
Embedding generation & semantic retrieval
Vector similarity & distance metrics
FAISS, ChromaDB & Pinecone indexing strategies
ANN concepts & retrieval optimization
Fixed, recursive & semantic chunking
Metadata enrichment & context optimization
Advanced RAG & Evaluation Systems
Hands-on: build a hybrid RAG workflow, evaluate it with RAGAS, add reranking and trace workflows with LangSmith. Tools: RAGAS, LangSmith, LangFuse and Pinecone.
BM25 & semantic retrieval systems
Hybrid search architectures & reranking pipelines
Cross-encoder workflows & context filtering
Retrieval caching & dynamic retrieval systems
Latency & cost optimization
RAGAS evaluation framework
Faithfulness, relevance & context precision scoring
LangSmith tracing & LangFuse monitoring
Failure analysis & workflow debugging
AI Agent Engineering
Hands-on: build a tool-using AI agent with memory, multi-step reasoning, and retry and fallback mechanisms. Tools: LangChain Agents, OpenAI Function Calling, Anthropic Tool Use and Mem0.
Agent lifecycle & execution architectures
ReAct framework & goal-oriented systems
Function calling & tool orchestration
Dynamic tool selection & multi-step execution
Short-term, long-term & episodic memory systems
State management & context continuity
Retry strategies & timeout handling
Failure recovery & safe execution workflows
Multi-Agent Systems & Orchestration
Hands-on: build a coordinator-worker workflow and collaborative AI systems, and implement distributed orchestration. Tools: CrewAI, LangGraph, Google ADK and LangSmith.
Sequential, parallel & hierarchical agent systems
Coordinator-worker architectures
Context passing & inter-agent communication
Shared memory & workflow synchronization
Task delegation & distributed orchestration
CrewAI, LangGraph & Google ADK workflows
Workflow state management & execution tracing
Scaling workflows, reliability & cost optimization
Harness Engineering
Hands-on: build a task-planning agent, context compression, permissioned tools, checkpointed recovery, human approval and cost dashboards. Tools: LangGraph, Mem0, MCP SDK, LangSmith, LangFuse, Guardrails AI and Helicone.
Task planning & dependency decomposition
Priority queues & execution sequencing
Prompt, context & memory orchestration
Context-window budgeting & memory routing
Context packing & compression strategies
Tool routing & permission control systems
Sandboxed execution & capability registries
Execution loops, retries & fallback systems
Circuit breakers & graceful degradation
Checkpointing, resume & state recovery
Persistent execution state & crash recovery
Human approval gates & escalation workflows
MCP-based enterprise integrations
Observability hooks & trace capture
Structured logging & anomaly detection
Evaluation hooks & regression testing
Cost governance & token budgeting
Safety controls, guardrails & off-switch systems
Reliability, Safety & Observability
Hands-on: build observability dashboards and automated evaluation pipelines, and simulate failure recovery. Tools: LangSmith, LangFuse, Helicone and Guardrails AI.
Retry strategies & resilience engineering
Idempotency patterns & fallback mechanisms
Prompt injection defence & moderation systems
Tool permission control & AI guardrails
Workflow tracing & latency monitoring
Cost analytics & debugging pipelines
Automated evaluations & benchmarking
Regression testing & continuous improvement loops
MCP & Modern AI Infrastructure
Hands-on: build an MCP server, connect agents to enterprise tools and create interoperable AI workflows. Tools: MCP SDK, OpenAI SDK and Google ADK.
MCP ecosystem & architecture fundamentals
MCP clients, servers & interoperability
MCP vs traditional tool calling
Service registration & context exchange
Capability discovery & transport layers
Database connectivity & API orchestration
Internal enterprise system integrations
Distributed AI ecosystems & emerging standards
Multimodal & Voice AI Systems
Hands-on: build a voice-enabled assistant, create multimodal AI workflows and implement real-time streaming. Tools: OpenAI SDK, voice APIs and WebSockets.
Vision models & document understanding
Audio processing & cross-modal reasoning
Speech-to-text & text-to-speech systems
Real-time voice orchestration workflows
Streaming responses & WebSocket architectures
Low-latency AI interaction pipelines
Deployment & Production AI Systems
Hands-on: deploy an AI application with FastAPI, dockerize AI workflows and build scalable pipelines. Tools: FastAPI, Docker, Azure Container Apps, Azure Monitor.
FastAPI for AI systems & backend deployment
Docker & container orchestration
Async architectures & queue systems
Caching strategies & load balancing
Performance optimization & reliability engineering
CI/CD pipelines for AI systems
Monitoring, security & cost optimization
Deployment checklists & incident response
Rollback strategies & SLA considerations
Testimonials from AI Engineering Learners
What learners say about the projects, the capstone and moving into AI engineering.
Built My First Production Multi Agent System
The program helped me move beyond prompting into building production-grade AI systems with orchestration, observability and enterprise RAG workflows. The capstone became the strongest part of my portfolio.
Built Real AI Systems Instead of Demo Projects
I implemented autonomous AI agents, observability pipelines, MCP integrations and scalable deployment systems that I can now showcase professionally in interviews and client engagements.
Transitioned Into an AI Engineering Role
I learned how enterprise AI systems are designed, evaluated, monitored and deployed in production. The hands-on projects and mentorship gave me confidence to move into AI engineering roles.
Finally Learned Enterprise AI Engineering
Most AI programs stop at prompting. This program covered orchestration, MCP, observability, deployment, evaluation and scalable AI infrastructure in a very practical way.
The Capstone Changed My Career Positioning
Building and presenting a production-grade AI architecture with deployment workflows and governance systems gave me a completely different level of confidence in enterprise discussions.

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What you will learn
Six skills, from LLM foundations to multi-agent systems and the runtime controls that keep them safe.
LLM Foundations & AI Backend Engineering
Master Python, APIs, async workflows, SDK integrations and production AI backend development.
Advanced Prompt Engineering & Structured Outputs
Build reliable prompting systems with reasoning workflows, validation, guardrails and structured outputs.
RAG Engineering & Evaluation Systems
Design enterprise-grade RAG architectures using embeddings, chunking, reranking, hybrid retrieval and evaluation pipelines.
Autonomous AI Agent Engineering
Create AI agents with memory, reasoning, tool usage, execution loops and recovery mechanisms.
Multi-Agent Systems & Orchestration
Design collaborative AI workflows using LangGraph, CrewAI, Google ADK and distributed orchestration patterns.
Harness Engineering & Runtime Control
Implement checkpointing, task decomposition, human approval gates, cost controls and safe execution workflows.
Eligibility
Prerequisites for the Agentic AI Engineering Program
Basic familiarity with Python is recommended, and an understanding of functions, loops and APIs helps.
You do not need prior experience in AI, machine learning or agentic AI to join the program.
The program suits developers, analysts, technical professionals and AI enthusiasts with a passion to build and experiment with AI systems.

This course is for you
For software engineers, data professionals, AI/ML and DevOps engineers, architects, and professionals moving into AI roles.
Software Engineers and Developers
Data Professionals
Early-career AI Enthusiasts
AI/ML Engineers
DevOps & Platform Engineers
Technical & Automation Architects
Project Managers
Professionals transitioning to AI
Earn an Agentic AI Certificate
Industry-Recognized Credential
The Agentic AI Engineering credential validates your ability to build and deploy production-grade AI systems.
Showcase Your Expertise
Add the certification and the AI systems you build in the program to your LinkedIn profile.
Stand Out for AI Roles
Strengthen your profile for AI Engineer, GenAI Engineer and LLM Engineer roles.
Certification


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The week-by-week curriculum, the schedule and the fee, as a PDF you can read offline or forward to whoever signs it off.
Frequently Asked Questions
01How can I enroll for the Certified Agentic AI Engineering Program?
Please follow the steps below to enroll for the Certified Agentic AI Engineering Program:
Step 1: Click on the View Schedules & Pricing button on this page. Step 2: Fill in the required details and click on Proceed. Step 3: Review your Order Summary, enter your Promo Code if any, and click on Proceed. Step 4: Choose your mode of payment and complete the transaction.
Alternatively, you can get in touch with our support team for assistance with enrollment.
To connect with our support team:
Call: +1 646 437 8780 or +91 9019173047 Email: hello@techademy.com Chat: Click here
02What will I accomplish after completing this program?
Upon completing the Certified Agentic AI Engineering Program, you will be able to build, deploy and optimize production-grade AI systems, including RAG pipelines, AI agents and multi-agent workflows. You will gain hands-on experience with enterprise AI engineering frameworks and create a portfolio of real-world projects and capstone work and unlock one of the most in-demand roles of 2026 and the years ahead.
03Are there prerequisites?
Basic familiarity with Python is recommended. Understanding functions, loops and APIs can be helpful. However, prior AI, Machine Learning or Agentic AI experience is not required.
04Who can join this program?
This program is designed for:
- Software Engineers and Developers
- Data Professionals
- AI/ML Engineers
- DevOps & Platform Engineers
- Technical Architects & Automation Engineers
- Project Managers
- Early-career AI Enthusiasts
- Professionals transitioning into AI roles
05What is the duration of the program?
The program includes 61+ hours of instructor-led training, hands-on projects, labs and an enterprise capstone project.
06What projects will I build during the program?
You will build 12+ hands-on AI systems and enterprise-grade projects, including:
- Production RAG Systems
- Tool-Using AI Agents
- Multi-Agent Workflows
- Voice and Multimodal AI Applications
- MCP-Based Enterprise Integrations
- Enterprise AI Capstone Project
07Which tools and frameworks will I learn?
You will gain hands-on experience with 10+ industry tools and frameworks, including:
- LangChain
- LangGraph
- CrewAI
- MCP SDK
- FastAPI
- Pinecone
- ChromaDB
- LangSmith
- LangFuse
- Docker
and other modern AI engineering tools
08Whom should I contact to get more information about the Certified Agentic AI Engineering Program?
You can contact our support team to know more about the program.
To contact our support team:
Call: +91 9019173047 or +1 646 437 8780 Email: hello@techademy.com Chat: Click here
01What is the mode of training for the Certified Agentic AI Engineering Program?
The program is delivered through live online instructor-led training sessions.
02Is this a live program or pre-recorded?
All sessions are conducted live by industry experts and include interactive discussions, guided exercises and hands-on implementation.
03What is the structure of the Certified Agentic AI Engineering Program?
The program follows a progressive learning path covering:
- AI and LLM Foundations
- Prompt Engineering
- RAG Engineering
- AI Agent Development
- Multi-Agent Systems
- Harness Engineering
- Reliability & Observability
- MCP & AI Infrastructure
- Multimodal AI
- Production Deployment
- Enterprise Capstone Project
Each module includes practical exercises and project-based learning.
04Will I work on an enterprise capstone project?
Yes. You will design and present an enterprise-grade AI system incorporating RAG architectures, AI agents, orchestration workflows, observability pipelines, MCP integrations and deployment strategies.
05What should I do if I miss a class?
Please contact our support team if you foresee missing a class. Our team will assist you based on availability.
Call: +1 646 437 8780 or +91 9019173047 Email: hello@techademy.com
06Is it possible to reschedule my classes?
Yes, rescheduling may be possible depending on availability. Please reach out to our learner support team for assistance.
07Will there be other learners in the class?
Yes. Each cohort includes professionals from diverse technical backgrounds such as software engineering, data analytics, DevOps, AI, automation and technology leadership, creating a collaborative learning environment.
08How can I get my doubts about the Certified Agentic AI Engineering Program cleared?
Most common questions are covered in our FAQ section. For additional assistance, you can reach out to our support team.
Call: +1 646 437 8780 or +91 9019173047 Email: hello@techademy.com
09Will I receive recordings of the sessions?
Yes. Learners receive lifetime access to session recordings and learning resources for continued learning and revision.
10In which cities does Techademy conduct Agentic AI Engineering training?
Techademy conducts the Certified Agentic AI Engineering Program completely online, enabling professionals worldwide to participate.
01How much does the Certified Agentic AI Engineering Program cost?
Please visit the program page or contact our support team to learn about the latest pricing, payment plans and available offers.
02Can I cancel my enrollment and receive a refund?
Yes. You may cancel your enrollment subject to Techademy's Cancellation and Refund Policy. Please refer to our Terms & Conditions for detailed refund eligibility and timelines.
03Does Techademy offer a money-back guarantee?
Yes. Techademy offers a money-back guarantee under specific terms and conditions as outlined in the applicable policy.
04Does Techademy provide scholarships or special offers?
Scholarships, promotional offers and limited-time discounts may be available for eligible learners.
Please contact our support team for the latest information.
Call: +1 646 437 8780 or +91 9019173047 Email: hello@techademy.com
05Does Techademy provide discounts for groups or corporate teams?
Yes. Techademy offers corporate training solutions, private cohorts and group enrollment discounts for organizations looking to upskill teams in Agentic AI Engineering.
Please contact our enterprise solutions team for customized pricing and training plans.
Call: +1 646 437 8780 or +91 9019173047 Email: hello@techademy.com
06Is career support included in the program?
Yes. The program includes career mentorship, portfolio guidance and interview preparation support to help learners strengthen their profiles for AI Engineer, GenAI Engineer and LLM Engineer roles.
07Will I receive a certification after completing the program?
Yes. Upon successful completion, you will earn the Agentic AI Engineering Certification, validating your ability to build and deploy production-grade AI systems using modern AI engineering frameworks and enterprise AI practices.








