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LIVE ONLINE • PRODUCTION AI ENGINEERING

Agentic AI Course: Build Production-Grade AI Agents

Learn to design, orchestrate, evaluate and deploy reliable AI agents through live engineering sessions, architecture reviews, guided projects and a production-focused capstone.

12 weeks · Live instructor-led · 8+ projects · Capstone · Certificate

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For software, AI/ML, data and platform engineers worldwide.

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₹35,000
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What Is the Agentic AI Course?

The Agentic AI Course is a 12-week live online program that teaches software, AI/ML, data and platform engineers to design, orchestrate, evaluate, secure and deploy reliable AI agents and multi-agent systems. Learners work with LangGraph, AutoGen, CrewAI, MCP, Agentic RAG, evaluation, guardrails and deployment tooling through architecture reviews, guided projects and a production-focused capstone. On completion, learners receive a School of Core AI course completion certificate.

Agentic AI Course Overview

Duration12 weeks
Delivery formatLive online, instructor-led
RecordingsIncluded — review for up to 2 years
Number of projects8+ hands-on projects + capstone
CapstoneProduction-grade Agentic AI system
Course levelIntermediate to Advanced
Intended audienceSoftware, AI/ML, data and platform engineers
PrerequisitesBasic Python helpful. Foundations built before advanced topics.
Certificate typeSchool of Core AI course completion certificate
MentorshipLive architecture and code reviews with instructors
Weekly commitment8–10 hours (live sessions + projects)
Course fee₹35,000 (one-time)
LanguageEnglish
International availabilityOpen to learners worldwide

Why Agent Demos Fail in Production

Creating a simple agent is easy. Operating a dependable agent system requires state management, orchestration, memory, retrieval, tool access, protocols, evaluation, guardrails, human approval, observability, durable execution, failure recovery, deployment and cost control.

Agent demo

Single prompt flow

Production requirement

Stateful orchestration

Agent demo

Happy-path execution

Production requirement

Retries, timeouts and recovery

Agent demo

Basic tool call

Production requirement

Permissioned and auditable tools

Agent demo

Final-answer review

Production requirement

Trajectory and tool-use evaluation

Agent demo

Temporary context

Production requirement

Persistent and governed memory

Agent demo

Local testing

Production requirement

Monitored deployment

Agent demo

No approval controls

Production requirement

Human-in-the-loop decisions

Agent demo

No operational limits

Production requirement

Cost, latency and loop budgets

Modern Agentic AI Systems Architecture

A production Agentic AI system combines user interfaces, agent orchestration, agent-to-agent communication, context and memory, secure tool access, evaluation and operational controls. The diagram below shows how the curriculum covers the complete system.

1

Experience and Interaction

Conversational application·Web application·Mobile application·Voice interface·Browser/computer-use interface
2

Agent–UI Interaction

AG-UIA2UI
Streaming events·Shared application state·Agent-generated forms and cards·Human interactions and approvals
3

Agent Orchestration

LangGraphAutoGenCrewAIOpenAI Agents SDK
Supervisor·Planner–executor·Handoffs·Parallel agents
4

Agent Communication

A2A
Agent discovery·Task communication·Messages·Artifacts·Remote agents·Internal agent communication
5

Context, Retrieval and Memory

Agentic RAG
Hybrid retrieval·Reranking·Semantic memory·Episodic memory·Procedural memory·Context compression
6

Tools and Enterprise Systems

MCP
APIs·Databases·Search systems·Business applications·Browser tools·Code execution·External services
⚙

Persistent Control Plane

Tracing ·Evaluation ·Guardrails ·Human approval ·Security ·Cost control ·Checkpoints ·Retries ·Rollback ·Audit logs

A production Agentic AI system combines user interfaces, agent orchestration, agent-to-agent communication, context and memory, secure tool access, evaluation and operational controls. AG-UI connects agent runtimes with applications, A2UI supports structured agent-generated interfaces, A2A enables communication between independent agents, and MCP provides standardised access to tools and enterprise systems. The persistent control plane — tracing, evaluation, guardrails, human approval, security, cost control, checkpoints, retries, rollback and audit logs — applies across all architecture layers.

Who Should Join This Agentic AI Course?

Software and Backend Engineers

Background
Builders with Python, API and backend experience who want to move into agent engineering.
What they learn
Stateful orchestration, tool integration, multi-agent architecture, evaluation and deployment.
Project outcome
A production-grade agent system with tracing, recovery and approval controls.

AI/ML and Data Engineers

Background
Practitioners who know ML pipelines and want practical depth in agentic systems.
What they learn
Agentic RAG, memory architecture, evaluation harnesses, AgentOps and cost control.
Project outcome
An AgentOps evaluation and control platform with automated quality loops.

Platform Engineers and Solution Architects

Background
Engineers responsible for reliable AI infrastructure and production systems.
What they learn
MCP security, deployment patterns, guardrails, durable execution and failure containment.
Project outcome
A deployed agent system with monitoring, rollback and operational runbooks.

Technical Leads Building AI-Native Products

Background
Leaders who need to understand agent architecture, failure modes and trade-offs.
What they learn
Multi-agent design patterns, protocol landscape, evaluation strategy and team handoff.
Project outcome
A capstone system demonstrating architecture decisions, trade-offs and production readiness.

This Program May Not Be the Right Fit If…

  • The learner wants only a short introduction to AI agents.
  • The learner wants a no-code-only course.
  • The learner does not intend to work with Python.
  • The learner only wants passive recorded content without live sessions.
  • The learner expects guaranteed employment without completing assessed work.
Check If This Course Fits Your Background

Why This Is a Production Agentic AI Engineering Program

What you get in this program — and why each piece matters for real agent work.

Multi-Agent Architectures

Included: Build orchestrated systems with LangGraph, AutoGen, and CrewAI — context sharing, handoffs, and state.
Why it matters: Real products need coordinated agents, not single prompt calls.

Agent Reasoning (PromptOps)

Included: Apply CoT, ReAct, ToT, and ReWOO patterns for step-by-step planning and tool use.
Why it matters: Structured reasoning is what makes an agent reliable instead of random.

Secure Deployment (MCP)

Included: Use Model Context Protocol for scoped tool calling, sandboxing, and authenticated execution.
Why it matters: Production agents touch real systems, so access has to be controlled and auditable.

RAG with Memory & Retrieval

Included: Ground agents with long-term memory, vector search, and contextual retrieval.
Why it matters: Agents that remember and cite context give accurate, trustworthy answers.

Live Mentorship & Reviews

Included: Architecture reviews, use-case mentoring, and code-level debugging from practitioners.
Why it matters: Feedback on your own build is where the real learning happens.

Capstone & Certificate

Included: Ship versioned agent projects to a GitHub portfolio plus a course completion certificate.
Why it matters: You leave with proof of work you can walk an interviewer through.

Placement Support

Included: Resume review, project narration, mock interviews, and referral connections.
Why it matters: Skills plus a clear job path, not just a recording library.

Engineering Skills You'll Build

Develop the technical capabilities required to architect, ground, evaluate, secure and operate production Agentic AI systems.

01Agent Architecture and Orchestration

8 skills · Design and orchestrate stateful, tool-using, multi-agent systems.

  • Agent architecture design
  • Tool calling and structured outputs
  • Stateful workflow engineering
  • Conditional routing
  • Supervisor and handoff patterns
  • Parallel and sequential execution
  • Multi-agent coordination
  • Human-in-the-loop workflows
02Retrieval, Knowledge and Memory

10 skills · Ground agents in enterprise knowledge and manage persistent memory.

  • Agentic RAG
  • Hybrid retrieval
  • Query rewriting
  • Reranking
  • Vector retrieval
  • Graph-based retrieval
  • Context engineering
  • Semantic, episodic and procedural memory
  • Context compression
  • Persistent memory design
03Evaluation, Safety and AgentOps

12 skills · Measure, protect and control agent behaviour in production.

  • Agent tracing
  • Task-completion evaluation
  • Tool-selection evaluation
  • Tool-argument evaluation
  • Trajectory evaluation
  • Groundedness testing
  • LLM-as-a-judge
  • Human evaluation
  • Guardrails and permissions
  • Cost and latency optimisation
  • Regression testing
  • Failure recovery
04Deployment and Production Operations

10 skills · Deploy, monitor and operate agent systems with production controls.

  • API and MCP integration
  • Agent-to-agent communication
  • Docker packaging
  • Managed agent runtimes
  • Agent identity and credential handling
  • Tool gateway and access policies
  • Browser and code execution
  • Observability and monitoring
  • Checkpoints, retries and rollback
  • Production release controls

Technology Stack Used Across the Program

The program covers agent frameworks, vector and graph retrieval, observability platforms, interoperability protocols and managed production infrastructure. Learners work most deeply with LangGraph, Langfuse and Amazon Bedrock AgentCore while gaining applied exposure to other major Agentic AI ecosystems.

L1

Agent Frameworks and SDKs

Orchestration frameworks and agent SDKs used to build, coordinate and deploy agent workflows.

Used in depth
LangGraph·LangChain
Applied lab
CrewAI·AutoGen·OpenAI Agents SDK·Amazon Strands Agents
Introduced
Google ADK·Microsoft Agent Framework
L2

Data, Retrieval and Memory

Vector databases, similarity-search libraries, graph databases and operational data stores for retrieval and persistent memory.

Used in depth
Qdrant (vector database)·FAISS (similarity-search library)·PostgreSQL / pgvector·Redis (operational data and caching)·Persistent memory·Hybrid retrieval and reranking
Applied lab
Neo4j (graph database)
L3

Observability, Tracking and Evaluation

Tracing, evaluation, experiment tracking and human annotation for measuring agent behaviour.

Used in depth
Langfuse·OpenTelemetry·AgentCore Observability·Evaluation datasets·LLM-as-a-judge
Applied lab
LangSmith·CloudWatch·Experiment tracking·Human annotation
L4

Protocols and Interfaces

Agent interoperability protocols for tool connectivity, remote-agent communication and agent-generated interfaces.

Applied lab
MCP (tool connectivity)
Introduced
A2A (agent-to-agent protocol)·AG-UI (agent-UI event streaming)·A2UI (agent-generated interfaces)
L5

Deployment and Runtime

Containerisation, API serving, managed agent infrastructure and production runtime services.

Used in depth
Docker·FastAPI·AWS·Amazon Bedrock AgentCore·AgentCore Runtime·AgentCore Memory·AgentCore Gateway·AgentCore Identity·AgentCore Evaluations
L6

Controlled Execution

Browser automation, sandboxed code execution, credential isolation and human approval controls.

Used in depth
AgentCore Browser·AgentCore Code Interpreter·Sandboxed execution·Human approvals·Credential isolation
Applied lab
Browser automation tooling

Agentic AI Curriculum: 14 Modules Across 6 Engineering Phases

The 12-week program is organised into six engineering phases. Every module name is visible below and crawlable in HTML. Each phase ends with a project gate that tests the capability developed.

01

Generative AI, RAG and Agent Foundations

Modules 01–04 · Build a grounded, tool-using Agentic RAG application.

LLM application architecture, structured outputs, tool calling, retrieval fundamentals, agent loops

Module 01 — Fundamentals of Generative AI

  • LLM application architecture, tokens and context
  • Structured outputs and tool/function calling
  • Context engineering and prompt design
  • Retrieval pipeline fundamentals: chunking, embeddings, vector retrieval, hybrid retrieval, reranking
  • RAG evaluation
  • Agent loop, planning and execution
  • Deterministic workflow versus agentic workflow
  • When not to use an agent
  • RAG versus Agentic RAG

Module 02 — Prompt Engineering

  • LLM application architecture, tokens and context
  • Structured outputs and tool/function calling
  • Context engineering and prompt design
  • Retrieval pipeline fundamentals: chunking, embeddings, vector retrieval, hybrid retrieval, reranking
  • RAG evaluation
  • Agent loop, planning and execution
  • Deterministic workflow versus agentic workflow
  • When not to use an agent
  • RAG versus Agentic RAG

Module 03 — Retrieval-Augmented Generation

  • LLM application architecture, tokens and context
  • Structured outputs and tool/function calling
  • Context engineering and prompt design
  • Retrieval pipeline fundamentals: chunking, embeddings, vector retrieval, hybrid retrieval, reranking
  • RAG evaluation
  • Agent loop, planning and execution
  • Deterministic workflow versus agentic workflow
  • When not to use an agent
  • RAG versus Agentic RAG

Module 04 — Agentic AI Foundation

  • LLM application architecture, tokens and context
  • Structured outputs and tool/function calling
  • Context engineering and prompt design
  • Retrieval pipeline fundamentals: chunking, embeddings, vector retrieval, hybrid retrieval, reranking
  • RAG evaluation
  • Agent loop, planning and execution
  • Deterministic workflow versus agentic workflow
  • When not to use an agent
  • RAG versus Agentic RAG

Project gate

Build a grounded, tool-using Agentic RAG application.

02

Stateful Agent Engineering

Modules 05–06 · Build a persistent agent that can pause, resume and recover.

AutoGen agent roles, LangGraph state schemas, persistence, checkpoints, human approval, retry policies

Module 05 — First AI Agent with AutoGen

  • AutoGen: agent roles, conversational workflows, delegation, tool execution, group interactions, termination conditions
  • LangGraph: state schemas, nodes and edges, conditional routing, reducers, commands, subgraphs
  • Parallel branches, persistence, checkpoints, interrupts
  • Human approval, retry policies, middleware, error handling
  • Time-travel debugging

Module 06 — AI Agent with LangGraph

  • AutoGen: agent roles, conversational workflows, delegation, tool execution, group interactions, termination conditions
  • LangGraph: state schemas, nodes and edges, conditional routing, reducers, commands, subgraphs
  • Parallel branches, persistence, checkpoints, interrupts
  • Human approval, retry policies, middleware, error handling
  • Time-travel debugging

Project gate

Build a persistent agent that can pause, resume and recover.

03

Multimodal and Multi-Agent Systems

Modules 07–10 · Coordinate specialised agents across multimodal workflows.

Multimodal routing, multi-agent architecture, reliability patterns, memory systems

Module 07 — Multimodal Agents

  • Multimodal systems: text, documents, images, audio, vision agents, voice agents, multimodal routing
  • Multi-agent architecture: supervisor, planner–executor, handoffs, sequential, parallel, hierarchical agents
  • Blackboard architecture, peer-to-peer collaboration, shared vs isolated state, context isolation, message contracts
  • Reliability: agent loops, deadlocks, duplicate work, conflicting updates, timeouts, cascading failure, failure containment, retry budgets
  • Memory: working, semantic, episodic, procedural, user, shared team memory, retrieval, consolidation, compression, retention, forgetting, memory poisoning controls

Module 08 — Multimodal Multi-Agent Systems

  • Multimodal systems: text, documents, images, audio, vision agents, voice agents, multimodal routing
  • Multi-agent architecture: supervisor, planner–executor, handoffs, sequential, parallel, hierarchical agents
  • Blackboard architecture, peer-to-peer collaboration, shared vs isolated state, context isolation, message contracts
  • Reliability: agent loops, deadlocks, duplicate work, conflicting updates, timeouts, cascading failure, failure containment, retry budgets
  • Memory: working, semantic, episodic, procedural, user, shared team memory, retrieval, consolidation, compression, retention, forgetting, memory poisoning controls

Module 09 — Agentic AI Design Patterns

  • Multimodal systems: text, documents, images, audio, vision agents, voice agents, multimodal routing
  • Multi-agent architecture: supervisor, planner–executor, handoffs, sequential, parallel, hierarchical agents
  • Blackboard architecture, peer-to-peer collaboration, shared vs isolated state, context isolation, message contracts
  • Reliability: agent loops, deadlocks, duplicate work, conflicting updates, timeouts, cascading failure, failure containment, retry budgets
  • Memory: working, semantic, episodic, procedural, user, shared team memory, retrieval, consolidation, compression, retention, forgetting, memory poisoning controls

Module 10 — Advanced Agentic Patterns

  • Multimodal systems: text, documents, images, audio, vision agents, voice agents, multimodal routing
  • Multi-agent architecture: supervisor, planner–executor, handoffs, sequential, parallel, hierarchical agents
  • Blackboard architecture, peer-to-peer collaboration, shared vs isolated state, context isolation, message contracts
  • Reliability: agent loops, deadlocks, duplicate work, conflicting updates, timeouts, cascading failure, failure containment, retry budgets
  • Memory: working, semantic, episodic, procedural, user, shared team memory, retrieval, consolidation, compression, retention, forgetting, memory poisoning controls

Project gate

Coordinate specialised agents across multimodal workflows.

04

Protocols, SDKs and Agent Interfaces

Modules 11–12 · Connect agents with applications, remote agents and enterprise tools.

MCP, Agentic SDKs, A2A, AG-UI, A2UI, OpenAI Agents SDK, tool permissions, agent interoperability

Module 11 — Model Context Protocol

  • MCP: host, client, server, capability negotiation, tools, resources, prompts, notifications, transport, authentication, authorization, tool permissions, tool poisoning, audit logs, multi-server architecture
  • Agentic SDKs: OpenAI Agents SDK, LangGraph, AutoGen, CrewAI, Google ADK, framework comparison, handoffs, sessions, tracing, guardrails, computer use
  • A2A: agent discovery, Agent Cards, skills, tasks, messages, artifacts, streaming, framework interoperability
  • AG-UI: agent runtime events, text streaming, tool-call events, state snapshots, state deltas, human-in-the-loop actions, frontend tools
  • A2UI: declarative UI, component catalogues, forms, cards, tables, progressive rendering, safe rendering
  • Tool permissions and agent interoperability across frameworks

Module 12 — Agentic SDKs

  • MCP: host, client, server, capability negotiation, tools, resources, prompts, notifications, transport, authentication, authorization, tool permissions, tool poisoning, audit logs, multi-server architecture
  • Agentic SDKs: OpenAI Agents SDK, LangGraph, AutoGen, CrewAI, Google ADK, framework comparison, handoffs, sessions, tracing, guardrails, computer use
  • A2A: agent discovery, Agent Cards, skills, tasks, messages, artifacts, streaming, framework interoperability
  • AG-UI: agent runtime events, text streaming, tool-call events, state snapshots, state deltas, human-in-the-loop actions, frontend tools
  • A2UI: declarative UI, component catalogues, forms, cards, tables, progressive rendering, safe rendering
  • Tool permissions and agent interoperability across frameworks

Project gate

Connect agents with applications, remote agents and enterprise tools.

05

Agent Evaluation, Safety and Operations

Modules 13–13 · Evaluate, control and operate agent systems reliably.

Observe, evaluate, protect, recover, deploy

Module 13 — AgentOps

  • Observe: run tracing, node traces, tool-call traces, state transitions, handoff traces, tokens, cost, latency, error classification
  • Evaluate: task success, tool-selection accuracy, tool-argument accuracy, trajectory evaluation, handoff accuracy, groundedness, loop detection, regression datasets
  • Protect: prompt injection, tool poisoning, memory poisoning, data exfiltration, permission boundaries, human approval, sandboxing, rate limits, spending limits, auditability
  • Recover: checkpoints, retries, backoff, fallbacks, timeouts, circuit breakers, idempotency, replay, compensation, rollback, dead-letter queues, partial completion
  • Deploy: FastAPI, queues, workers, Redis, PostgreSQL, containers, environment separation, versioning, release management, monitoring

Project gate

Evaluate, control and operate agent systems reliably.

06

Browser and Computer-Use Agents

Modules 14–14 · Build controlled browser agents and complete a production capstone.

DOM interaction, vision-based computer use, browser state, safety controls, capstone

Module 14 — Browser-Native Agents

  • DOM-based interaction, accessibility-tree interaction, vision-based computer use, hybrid DOM and vision systems
  • Authentication sessions, file handling, multi-tab workflows, browser state, page-change recovery
  • Approval before sensitive actions, credential isolation, sandboxing, anti-prompt-injection controls
  • CAPTCHA escalation, audit trails, AG-UI progress streaming, A2UI approval interfaces

Project gate

Build controlled browser agents and complete a production capstone.

Agentic AI Projects You Will Build

Agentic RAG Research Assistant

Users need grounded, citation-backed answers from enterprise knowledge bases.

Single agent with retrieval tools and structured output.

  • RAG pipeline design
  • Reranking and grounding
  • Citation-aware outputs
  • Evaluation loops

Evaluation

Groundedness score, citation accuracy, task completion rate

Stateful Customer-Support Agent

Support teams need an agent that maintains context and escalates when needed.

LangGraph state machine with persistence, interrupts and human approval.

  • Stateful orchestration
  • Checkpoint persistence
  • Conditional routing
  • Human escalation

Evaluation

Task success rate, escalation accuracy, latency

Multimodal Multi-Agent Operations System

Operations teams need parallel agents handling text, documents and images.

Supervisor with parallel specialised agents and handoffs.

  • Multi-agent coordination
  • Failure isolation
  • Handoff patterns
  • Multimodal routing

Evaluation

Trajectory evaluation, handoff accuracy, failure containment

AI Software Engineering Agent

Development teams need an agent that reads, writes and tests code safely.

Code-execution agent with tool boundaries, review gates and sandboxed execution.

  • Code generation and review
  • Sandboxed execution
  • Test generation
  • Approval before merge

Evaluation

Test pass rate, code correctness, tool-boundary compliance

MCP-Based Enterprise Tool Agent

Enterprises need agents that safely access internal tools and systems.

MCP server with permissioned tools, audit logs and authentication.

  • MCP protocol implementation
  • Tool permissioning
  • Audit logging
  • Prompt injection defense

Evaluation

Tool-selection accuracy, permission compliance, audit trail completeness

Agent Evaluation and Control Platform

Teams need visibility into agent behaviour, cost and quality.

Observability and evaluation layer over agent runtime.

  • Tracing and observability
  • Regression datasets
  • Cost monitoring
  • Loop detection

Evaluation

Trajectory evaluation, tool-argument accuracy, regression testing

Production Agentic AI Capstone

Demonstrate end-to-end production agent engineering capability across the full system lifecycle.

Architecture design

Learner-selected architecture justified with trade-off documentation.

Agent and tool boundaries

Define tool permissions, execution limits and approval gates.

Memory strategy

Design working, semantic and procedural memory with compression and poisoning controls.

Evaluation plan

Full evaluation suite: task success, trajectory, cost, latency and safety.

Reliability controls

Checkpoints, retries, fallbacks, circuit breakers, idempotency and rollback.

Security and human approvals

Guardrails, prompt injection defense, sandboxing and human-in-the-loop decisions.

Deployment approach

Containerised deployment with monitoring, versioning and release management.

Final technical demonstration

Live defense of architecture decisions, evaluation results and operational readiness.

Learn from Engineers Building Production AI Systems

K

Krishna

AI Engineer

Teaches the Fundamentals of Agentic AI — covering agent loops, tool calling, state management, and the architectural decisions that separate demos from production agent systems.

Areas of expertise

Agentic AI FundamentalsAgent LoopsTool CallingState Management

Modules taught

Agentic AI Foundation, Fundamentals of Generative AI

A

Ashutosh

Senior AI Engineer

Teaches RAG, Agentic RAG, and Multi-Agent systems — covering retrieval pipelines, reranking, grounding, multi-agent orchestration, and the reliability patterns needed when agents coordinate across workflows.

Areas of expertise

RAGAgentic RAGMulti-Agent SystemsRetrieval Pipelines

Modules taught

Retrieval-Augmented Generation, Multimodal Multi-Agent Systems, Agentic AI Design Patterns

V

Vivek

Applied AI Engineer

Teaches Agent System Design, AgentOps, MCP, and A2A — covering production observability, Model Context Protocol, agent-to-agent communication, and the operational controls that keep agent systems trustworthy after launch.

Areas of expertise

Agent System DesignAgentOpsMCPA2A

Modules taught

Model Context Protocol, AgentOps, Advanced Agentic Patterns

Your Agentic AI Course Certificate

Complete the program and receive a course completion certificate from the School of Core AI that reflects the agent systems you built — from autonomous agents and multi-agent orchestration to LangGraph workflows and production deployment.

Sample certificate

CERTIFICATE

OF ACHIEVEMENT

THIS IS TO CERTIFY THAT

SCHOOL
OF
CORE
AI

YOUR NAME

Date : DD MMM YY

Has Successfully Completed The

Comprehensive Agentic AI Engineering Program

Conducted By The School Of Core AI.

This Intensive Program Included Hands-On Training In LangGraph, CrewAI, AutoGen, Multi-Agent Systems, PromptOps (ReAct, CoT, ToT), RAG Pipelines, Model Context Protocol (MCP), Agent Memory Systems, LangSmith Observability, And Production-Grade Agentic AI Deployment.

Aishwarya Pandey

Founder and CEO

Share your certificate on LinkedIn, add it to your portfolio, or bring it to interviews as evidence of the agent systems you built during the program.

Certificate and Assessment

Certificate typeSchool of Core AI course completion certificate
Not a university degreeUnless officially accredited
Not an external vendor certificationUnless formally authorised
Required projects8+ hands-on projects and a production capstone
Evaluation methodProject reviews, architecture reviews, capstone assessment and code-level feedback
Resubmission policyContact the academic team for the current resubmission policy
Certificate credential IDIssued on completion — verify through the admissions team
Skills listed on certificateAgent frameworks, orchestration, RAG, MCP, multi-agent systems, evaluation, deployment and AgentOps

The exact assessment rubric is published on the academic team page.

What Engineers Actually Walked Away With

Not another "call the API" tutorial. Engineers who went through the framework internals, the failure modes, and a trainer who had shipped these systems before.

"Most courses stop at 'call the API, get a response.' Ours went into how LangGraph actually manages state — checkpointing, conditional edges, human-in-the-loop interrupts — because our trainer had shipped agent systems that failed in production and built the curriculum around why they failed. By the capstone I had a multi-agent system I could walk an interviewer through — architecture, trade-offs, the parts that broke and how I fixed them. That's what got me through AI Engineer interviews, not a certificate."
Sneha M.
AI Engineer
ai_engineer → milestone: capstone → status: interview_ready
"Every other resource I'd tried was a Colab notebook that worked once and never explained why. Here I built a real RAG pipeline — chunking strategy, embedding choice, hybrid search — then watched it break on edge cases in code review. Our trainer had actually run vector search at production scale and knew exactly where analysts like me would get it wrong."
Nitin G.
Data Analyst → AI
from: data_analyst → agent_builder
"I'd used the OpenAI API for a chatbot and thought I understood agents. I didn't. The LangGraph pipeline had persistent memory, retry logic and full tracing built the way you'd actually need it in production — not the version most tutorials stop at. Our trainer pushed us to justify every design decision, not just make it run once."
Rahul S.
Software Engineer
constraint: full_time_job → shipped anyway
"Most 'agent' content online is one LLM call with a system prompt. This was different — we built systems where multiple agents route tools, hand off tasks, and recover from failures without a human stepping in. The MCP module went deep enough into the protocol internals that it was in my team's pull requests the same week."
Yusuf J.
Backend Engineer
module: mcp → shipped_to_prod
"The reasoning-pattern module wasn't 'here are three prompting tricks.' Our trainer broke down exactly when ReAct, Chain-of-Thought and Tree-of-Thought each fail, then paired it with LangSmith tracing so we could see the actual decision path an agent took — the kind of framework-level understanding most tutorials skip."
Om Y.
ML Engineer
before: guessing → debugging
"Anyone can build an agent that works once, for one input. This taught how to keep it working at volume — guardrails, cost ceilings, evaluation pipelines, defined fallback behavior when a tool call fails. Our trainer had run these in production and treated failure modes as the main event, not a footnote."
Rohit R.
Full-Stack Developer
frame: demo → production

Agentic AI Course Fee and Enrollment

One transparent fee for the complete 12-week live program, guided projects, capstone and course completion certificate.

Course Fee

₹35,000

Complete program fee

No hidden course charges.

Payment options available.

What Is Included

  • Live instructor-led sessions
  • Session recordings
  • Guided engineering projects
  • Production capstone
  • Completion certificate
  • Code and architecture reviews

Enrollment

Admissions open

Next cohort

Next cohort schedule will be announced shortly.

Apply for the Next Cohort
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Career support includes portfolio guidance, resume review and mock interview preparation. Employment is not guaranteed.

Agentic AI Course fee is ₹35,000 (INR) for a 12-week live online program. The fee includes live instructor-led sessions, session recordings, guided engineering projects, a production capstone, a course completion certificate, and code and architecture reviews. No hidden course charges. Payment options are available. Career support includes portfolio guidance, resume review and mock interview preparation. Employment is not guaranteed.

Agentic AI Course Frequently Asked Questions

Everything you need to know about the Agentic AI Course.

What is the Agentic AI Course, and what will I learn?

The Agentic AI Course is a 12-week live online production engineering program that teaches software, AI/ML, data and platform engineers to design, orchestrate, evaluate, secure and deploy reliable AI agents and multi-agent systems. You will learn agent development with AutoGen and LangGraph, multi-agent orchestration with CrewAI, Agentic RAG with reranking and grounding, memory architecture, Model Context Protocol for tool access, Agentic SDKs, evaluation, guardrails, safety controls and production deployment. The program includes architecture reviews, code-level feedback, eight guided projects and a production capstone. On completion, you receive a School of Core AI course completion certificate.

Is the Agentic AI Course live, and how is it delivered?

The course is delivered through live, instructor-led online sessions. You attend structured classes where instructors explain architecture decisions, review code, answer questions and provide project feedback in real time. Recordings of every session are included so you can review material at your own pace. The program also includes mentor support for doubt resolution, architecture reviews on your project work, and code-level feedback on assignments. All sessions are conducted in English and accessible online from anywhere. The format is designed for serious learners who want live teaching, not passive video consumption.

How long is the course, and what weekly commitment is required?

The program runs for 12 weeks. You should expect to commit approximately 8 to 10 hours per week, which includes live session attendance, guided project work, self-paced build time and architecture review participation. The capstone in the final phase may require additional focused effort as you design, deploy and defend a complete production agent system. The weekly commitment is structured to fit alongside a full-time role, but the program is not self-paced — live sessions are the core delivery format and active participation is expected throughout.

Can international learners join, and which timezone is used?

Yes, the program is open to learners worldwide. Sessions are delivered live online in English. The primary class timezone and exact session timing are confirmed with each cohort, so international learners should verify schedule fit with the admissions team before enrolling. Recordings are included so learners who miss a live session can review the material. International payment options and any applicable tax details should be confirmed with the admissions team during enrollment. The program does not require physical attendance or travel to any specific location.

What prerequisites are required, and is Python necessary?

Basic Python knowledge is recommended because learners build tool integrations, LangGraph workflows, retrieval pipelines and evaluation logic during the program. You do not need to be an advanced machine learning engineer, but you should be comfortable with functions, APIs, dictionaries and basic debugging. Familiarity with APIs, general software development concepts and a willingness to complete applied assignments are also expected. Learners who lack a Python foundation should complete preparation material before the stateful-agent modules begin. The program builds foundations before advancing to multi-agent orchestration, evaluation and deployment.

Which frameworks, SDKs and Agentic AI protocols are covered?

The core curriculum covers LangGraph for stateful orchestration, AutoGen for conversational multi-agent workflows, CrewAI for role-based coordination, OpenAI Agents SDK, LangChain for tool use, Agentic RAG with retrieval and reranking, and Model Context Protocol for secure tool access. AgentOps is covered for tracing, evaluation and observability. Frontier labs introduce A2A for agent-to-agent communication, AG-UI for agent-frontend streaming and A2UI for agent-generated declarative interfaces. These frontier protocols are taught as evolving specifications with version tracking, not as permanent standards. The program teaches architectural judgement across frameworks, not brand memorisation.

What projects will I build during the Agentic AI Course?

You will build six guided projects: an Agentic RAG research assistant with grounding and citations, a stateful customer-support agent with persistence and human escalation, a multimodal multi-agent operations system with parallel execution and failure isolation, an AI software engineering agent with sandboxed code execution and approval gates, an MCP-based enterprise tool agent with permissioned tools and audit logs, and an agent evaluation and control platform with tracing and regression testing. The program culminates in a production capstone where you design, deploy and defend a complete production agent system with full operational controls.

How are projects, agent workflows and the capstone evaluated?

Projects are evaluated through architecture reviews, code-level feedback and structured assessment of task completion, tool-use correctness, state and memory handling, reliability controls and safety boundaries. The capstone includes a full evaluation suite covering task success, trajectory quality, cost, latency, guardrails and deployment readiness. The exact formal scoring rubric is published after academic team approval. Evaluation is designed to test whether you can explain and defend your architecture decisions, not just produce a working demo. Mentor reviews happen during live sessions and through project submissions throughout the 12 weeks.

What certificate will I receive after completing the course?

You receive a School of Core AI course completion certificate after meeting the program requirements, which include completing the required projects and the capstone assessment. The certificate represents the agent systems you built during the program, including orchestration, RAG, MCP, multi-agent systems, evaluation and deployment. It is not a university degree, government certification or externally accredited credential unless officially verified. A certificate credential ID is issued on completion and can be verified through the admissions team when available. The certificate does not imply recognition by any external vendor or standards body.

Is the course suitable for working professionals?

Yes, the program is designed for working professionals with full-time roles. Live sessions, recordings and project-based learning are structured so you can build seriously without depending on offline attendance or city commutes. The expected weekly commitment of 8 to 10 hours includes live attendance and project work. Project deadlines are aligned with the 12-week schedule, and mentor support is available between sessions for doubt resolution and architecture questions. The program is best suited for software, AI/ML, data and platform engineers who can dedicate focused weekly time to building real agent systems.

What is the Agentic AI Course fee, and what is included?

The course fee is ₹35,000 as a one-time payment. This covers all live instructor-led sessions, session recordings, eight guided projects, the production capstone, architecture and code reviews, mentor support and the course completion certificate. Career support includes resume review, portfolio guidance and mock interview preparation. Any applicable taxes, installment options and refund or transfer policies should be confirmed with the admissions team before enrollment, as these details may vary by cohort and payment method. The fee is the same for all learners regardless of location.

Does the course include placement and career support?

Career support includes resume review, portfolio guidance, project narration practice, mock interviews and referral connections where available. This is structured support to help you present your agent engineering work credibly in technical interviews and architecture discussions. The program does not guarantee employment, salary, placement or interview opportunities. Career outcomes depend on your existing background, project quality, portfolio depth, interview preparation and current hiring market conditions. Support means guidance and feedback, not a formal placement guarantee.

How is this Agentic AI Course different from a Generative AI course?

A Generative AI course focuses on LLM applications, prompting techniques and end-to-end GenAI system building. This Agentic AI Course goes deeper into autonomous agents that plan, use tools, maintain state, orchestrate multi-step workflows and operate reliably in production. You will work with stateful orchestration, memory architecture, multi-agent coordination, Model Context Protocol, evaluation, guardrails, human approval, observability and deployment. The focus is engineering reliable agent systems that survive contact with real workflows, not building demos. If you need broader GenAI foundations first, the Generative AI Course is a better starting point before this program.

Compare Before You Enroll

Check whether you need agentic depth or a neighboring path

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