12-Week Live Online Engineering Program
Engineer agents you can evaluate, control and operate.
Move beyond prototypes. Build production agent systems with state management, scoped tool access, evaluation pipelines, failure recovery and human-in-the-loop approval — the engineering discipline that separates demos from deployed systems.
Capstone System Preview
From request to controlled runtime
USER REQUEST / EVENT
STATEFUL AGENT WORKFLOW
KNOWLEDGE & MEMORY
Grounded context
PERMISSIONED TOOLS
Scoped actions
SPECIALIST AGENTS
Traceable handoffs
MONITORED AGENT RUNTIME
Program Overview
A 12-week live engineering program that teaches developers to turn LLM workflows into controlled agent systems. You build agents that preserve state, use tools within defined boundaries, expose their behaviour through traces, pass structured evaluations and recover from failures.
It is not a framework tour. Each phase introduces a production control, applies it in a guided project and validates it through technical review.
See the 6-phase curriculum →The Production-Readiness Test
A production-ready AI agent must maintain state, use tools within defined permission boundaries, ground its decisions in relevant context, expose execution traces, pass task and trajectory evaluations, recover from failures and remain within cost, latency and loop limits.
A working response is not enough. Projects are reviewed against the controls that make agent behaviour inspectable, bounded and recoverable.
Can the agent pause, resume and retain the correct context?
State schemas, persistence, checkpoints and human interrupts
Can the agent use tools without exceeding its permissions?
Authentication, validation, scoped access and approval gates
Can we determine whether the agent completed the task correctly?
Task success, trajectory, grounding and tool-use evaluation
Can the agent stop, retry, fall back and remain within operating limits?
Timeouts, retry and loop budgets, fallbacks, latency and cost controls
Every guided project demonstrates at least one of these controls. The production capstone must demonstrate all four.
Audience Fit
Current background
Builders with Python, API and backend experience who want to move into agent engineering.
Capability developed
Stateful orchestration, tool integration, multi-agent architecture, evaluation and deployment.
Relevant project outcome
A production agent system with tracing, recovery and approval controls.
Current background
Builders with Python, API and backend experience who want to move into agent engineering.
Capability developed
Stateful orchestration, tool integration, multi-agent architecture, evaluation and deployment.
Relevant project outcome
A production agent system with tracing, recovery and approval controls.
Current background
Practitioners who know ML pipelines and want practical depth in agentic systems.
Capability developed
Agentic RAG, memory architecture, evaluation tooling, AgentOps and cost control.
Relevant project outcome
An AgentOps evaluation and control platform with automated quality loops.
Current background
Engineers responsible for reliable AI infrastructure and production systems.
Capability developed
MCP security, deployment patterns, guardrails, durable execution and failure containment.
Relevant project outcome
A deployed agent system with monitoring, rollback and operational runbooks.
Current background
Leaders who need to understand agent architecture, failure modes and trade-offs.
Capability developed
Multi-agent design patterns, protocols, evaluation strategy and team handoff.
Relevant project outcome
A capstone system demonstrating architecture decisions, trade-offs and production readiness.
You should already be comfortable with
This Program May Not Be the Right Fit If…
Production Reality
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
Production Agent System
Demo: Single prompt flow
Production: Stateful orchestration
Demo: Happy-path execution
Production: Retries, timeouts and recovery
Demo: Basic tool call
Production: Permissioned and auditable tools
Demo: Final-answer review
Production: Trajectory and tool-use evaluation
Demo: Temporary context
Production: Persistent and governed memory
Demo: Local testing
Production: Monitored deployment
Demo: No approval controls
Production: Human-in-the-loop decisions
Demo: No operational limits
Production: Cost, latency and loop budgets
These production requirements shape the architecture used throughout the course.
What You Learn Across the Architecture
ORCHESTRATION
State, routing, retries, recovery
RETRIEVAL & MEMORY
Agentic RAG, grounding, persistent memory
TOOLS & PROTOCOLS
MCP, permissions, enterprise integrations
MULTI-AGENT
Delegation, handoffs, A2A
EVALUATION
Trajectory, tool use, regression
OPERATIONS
Tracing, security, deployment, cost
Systems Architecture
A production Agentic AI system connects user experiences, stateful orchestration, agent communication, retrieval and memory, permissioned tools and a persistent control plane. The control plane applies tracing, evaluation, security, human approval, recovery and operating limits across every layer.
Persistent Control Plane
Layer 1
Layer 2
Layer 3
Layer 4
Layer 5
Layer 6
Layer 7
Experience and Interaction
Why: Users interact with agents through chat, voice, UI components or agent-generated interfaces. The interaction layer must handle streaming, state display and human approval actions.
What can fail: Poor latency perception, confusing state transitions, missing approval prompts, unsafe content rendering.
What learners implement: AG-UI event streaming, A2UI safe rendering, approval interfaces and progress indicators.
Agent–UI Interaction
Why: Connects agent runtimes with frontends through structured events. Text streaming, tool-call events, state snapshots and deltas must be handled predictably.
What can fail: Lost events, stale state, race conditions between agent and frontend, unsafe UI injection.
What learners implement: AG-UI event handlers, state synchronization and human-in-the-loop action surfaces.
Agent Orchestration
Why: The core execution layer. LangGraph state machines, conditional routing, parallel branches, subgraphs and interrupts control how agents plan, execute and hand off tasks.
What can fail: Deadlocks, infinite loops, unhandled errors, state corruption, cascading failures across agents.
What learners implement: Typed state schemas, nodes, edges, reducers, checkpoints, interrupts and conditional routing.
Agent Communication
Why: Agents need to discover, message and share artifacts with each other across frameworks. A2A protocol enables interoperability between independent agent systems.
What can fail: Discovery failures, message format mismatches, lost artifacts, incompatible task contracts.
What learners implement: A2A agent cards, task delegation, message passing and artifact exchange.
Context, Retrieval and Memory
Why: Agents need grounded, persistent and governed memory. Retrieval grounds responses in external knowledge; memory systems maintain working, semantic, episodic and procedural state.
What can fail: Memory poisoning, unbounded growth, stale context, conflicting updates, hallucination from ungrounded generation.
What learners implement: Agentic RAG pipelines, reranking, memory consolidation, compression and poisoning controls.
Tools and Enterprise Systems
Why: MCP provides standardized tool and context connectivity. Agents access databases, APIs, files and enterprise systems through permissioned, auditable tool boundaries.
What can fail: Tool poisoning, prompt injection, unauthorised access, data exfiltration, missing audit trails.
What learners implement: MCP servers with authentication, authorization, permission boundaries, audit logs and multi-server architecture.
Persistent Agent Control Plane
Why: Every agent layer needs a persistent control plane that applies tracing, evaluation, guardrails, human approval, authentication, cost controls, latency controls, checkpoints, retries, rollback and audit logs.
What can fail: Without a control plane, agent failures are invisible, unbounded and unrecoverable.
What learners implement: A persistent control plane with tracing, evaluation gates, cost and latency limits, checkpoints, retry policies and audit logging.
Tools & Stack
The course uses a focused stack for orchestration, retrieval, observability, tool connectivity and deployment. Technologies are grouped as used in depth, applied in labs or introduced for comparison so learners understand both implementation and framework-selection trade-offs.
Stateful workflows, checkpoints, human-in-the-loop
Handoffs, sessions, guardrails
Role-based multi-agent coordination
AutoGen migration path and comparison
Agent development kit comparison
Tool servers, permissioned access, audit logs
Agent-to-agent discovery and task passing
Agent-to-frontend event streaming
Tracing, evaluation, regression testing
Task, trajectory and tool-use scoring
Agent runtime APIs and serving
Queues, caching, session state
Containerized agent deployment
Engineering Proof
Learners complete six guided projects covering Agentic RAG, stateful support workflows, multimodal multi-agent systems, software engineering agents, MCP-based enterprise tools and AgentOps evaluation, followed by a production capstone.
Real Problem
Users need grounded, citation-backed answers from enterprise knowledge bases.
System Being Built
Single agent with retrieval tools and structured output.
Capabilities Demonstrated
Evaluation Categories
Groundedness score, citation accuracy, task completion rate
Demonstrate full-lifecycle agent engineering — design, build, evaluate, deploy and operate.
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.
Curriculum
The 12-week curriculum contains 14 modules organised across six engineering phases. Learners progress from Generative AI, retrieval and agent foundations to stateful orchestration, multi-agent systems, protocols, AgentOps, browser agents and a production capstone.
Phase 1
LLM application architecture, structured outputs, tool calling, retrieval fundamentals, agent loops
Project gate
Build a grounded, tool-using Agentic RAG application.
Phase 2
AutoGen agent roles, LangGraph state schemas, persistence, checkpoints, human approval, retry policies
Project gate
Build a persistent agent that can pause, resume and recover.
Phase 3
Multimodal routing, multi-agent architecture, reliability patterns, memory systems
Project gate
Coordinate specialised agents across multimodal workflows.
Phase 4
MCP, Agentic SDKs, A2A, AG-UI, A2UI, OpenAI Agents SDK, tool permissions, agent interoperability
Project gate
Connect agents with applications, remote agents and enterprise tools.
Phase 5
Observe, evaluate, protect, recover, deploy
Project gate
Evaluate, control and operate agent systems reliably.
Phase 6
DOM interaction, vision-based computer use, browser state, safety controls, capstone
Project gate
Build controlled browser agents and complete a production capstone.
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
Module 02 — Prompt and Context Engineering
Module 03 — Retrieval-Augmented Generation
Module 04 — Agentic AI Foundations
Project gate
Build a grounded, tool-using Agentic RAG application.
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 — Multi-Agent Foundations and Framework Migration
Module 06 — Stateful Agents with LangGraph
Project gate
Build a persistent agent that can pause, resume and recover.
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
Module 08 — Multimodal Multi-Agent Systems
Module 09 — Agentic AI Design Patterns
Module 10 — Advanced Agent Reliability and Memory
Project gate
Coordinate specialised agents across multimodal workflows.
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 and Secure Tool Integration
Module 12 — Agent SDKs and Interoperability
Project gate
Connect agents with applications, remote agents and enterprise tools.
Agent Evaluation, Safety and Operations
Modules 13–13 · Evaluate, control and operate agent systems reliably.
Observe, evaluate, protect, recover, deploy
Module 13 — AgentOps
Project gate
Evaluate, control and operate agent systems reliably.
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 and Capstone
Project gate
Build controlled browser agents and complete a production capstone.
Reviewed the curriculum? Take the next step.
Download the detailed syllabus, enroll directly, or talk to our team — whatever stage you are at.
Learning Process
The course combines live instruction, guided implementation, independent project work, trace-based debugging and architecture and code reviews. Learners should plan for approximately 8–10 hours per week across live sessions, review and project implementation.
Live concept and architecture session
Instructor explains the concept, architecture and trade-offs in a live session.
Instructor system walkthrough
Guided walkthrough of how the system is built, including failure points.
Guided engineering lab
Structured lab work where you implement the core patterns under guidance.
Independent implementation
You extend the system independently to solidify understanding.
Trace-based debugging and evaluation
Use tracing and evaluation to diagnose and improve your agent.
Architecture/code review and iteration
Live review of your work with feedback on architecture and code decisions.
Assessment dimensions
Example evaluation scorecard (illustrative — not real student scores)
| Task completion | Meets expectation |
|---|---|
| Architecture justification | Production demonstration |
| State and failure handling | Needs work |
| Tool selection and argument quality | Meets expectation |
Feedback mechanisms include live architecture review, code-level feedback, project review and capstone assessment. Current resubmission policy is confirmed during admissions.
Mentorship
Reviewers examine your agent architecture decisions: state design, orchestration patterns, tool boundaries and recovery strategies.
What learners produce: Learners produce architecture diagrams with annotated trade-offs and receive live feedback during sessions.
Reviewers examine your implementation: checkpointing, error handling, retry logic, guardrails and test coverage.
What learners produce: Learners submit code with traces, test results and a reliability checklist for each guided project.
Reviewers evaluate the production capstone: task success, trajectory quality, cost, latency, guardrails and deployment readiness.
What learners produce: Learners defend their capstone with a complete evaluation suite, deployment manifest and operational runbook.
Credential
Learners who complete the required projects and capstone receive a School of Core AI course completion certificate. It is not a university degree or external vendor certification unless separately accredited. Career support includes only the services explicitly listed on this page.
Course completion certificate plus structured career support — included in the program fee.
Course Completion Certificate
This certifies that
[Learner Name]
has successfully completed the
Agentic AI Course
Credential ID
Issued on completion
Aishwarya Pandey
Founder & CEO
Not a university degree or external vendor certification. Awarded after completing required assessed work.
Portfolio & Narration
Frame capstone and guided projects for hiring panels.
Resume Review
Targeted feedback for AI engineering roles.
Mock Interviews
System design and architecture rounds.
Referral Support
Where openings match your profile.
Employment is not guaranteed.
What learners built, what engineering decisions they improved and what reviewers observed.
Senior Data Scientist
I joined mainly to go deep into transformers and their variants. The specialization helped me understand LLM systems from the inside out — not just how to use them, but how to reason about and architect them.
GenAI Specialization
Prithvi
Marketer
I come from a marketing background and wanted to build AI products around growth. This course gave me the technical grounding to actually turn those ideas into working products.
GenAI Specialization
Ajit
Lead Analytics
I was leading analytics teams and wanted to go deeper into NLP and neural networks. The specialization gave me the depth I was missing — I can now build and deploy AI solutions with real confidence.
GenAI Specialization
Praveen
Senior Software Developer
I wanted to learn AI development and agentic AI — how to build and deploy intelligent applications end-to-end. The track gave me hands-on exposure to the full pipeline, from idea to production.
AI Developer
Deepak
Senior Cybersecurity
I work in cybersecurity and wanted to upskill with agentic AI to build solutions in my own domain. The course helped me integrate AI into real security workflows — it was directly applicable to what I do.
Agentic AI
Senior Manager
I joined to learn the AIOps side of things — how ML systems actually run in production. The course gave me a solid grasp of operations, monitoring, and managing AI deployments at scale.
MLOps SpecializationEnrollment
The Agentic AI Course fee is ₹35,000 for the complete 12-week live online program. It includes live sessions, recordings, six guided projects, the production capstone, technical reviews and the School of Core AI course completion certificate.
One transparent fee for the complete 12-week live program, six projects, capstone and certificate.
Complete Program Fee
₹35,000
12-week live program · 6 projects · capstone · certificate
What Is Included
Career support includes portfolio guidance, resume review and mock interview preparation.
Agentic AI Course fee is ₹35,000 (INR) for a 12-week live online program. The fee includes live sessions, recordings, 6 guided projects, a production capstone, architecture reviews, a course completion certificate and career support.
FAQ
Compare Before You Enroll
These comparisons help you decide whether your next step is agent systems, broader GenAI foundations, or application-building first.
Decide whether you need application-building foundations or a dedicated agent systems track first.
Open comparisonChoose between broad GenAI foundations and agent-focused orchestration depth.
Open comparisonCompare three agent frameworks by speed, collaboration style, and orchestration control.
Open comparisonGot More Questions?
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