How is this different from other agentic AI courses?

Most courses labelled "agentic AI" are two-day workshops with tool demos. I run this as a practitioner who has shipped multi-agent systems and MCP in production, including a persona-driven conversational system that held 10,000+ concurrent users in load testing, and MCP servers that let Claude act inside real internal systems. The course focuses on design, evaluation and governance rather than a demo.

What background do I need?

Comfort with software engineering and APIs. You do not need prior agent experience, but you do need to read and write code.

Can this be delivered to a corporate team?

Yes. Private or corporate, built around your use cases, remote or on-site in India.

MASTERCLASS · AI AGENTS

AI Agent Masterclass

This masterclass is for senior engineers and architects who want to design, build and govern real agents rather than sit through another tool demo. Agentic AI is among the fastest-growing skills in India’s 2026 hiring market, yet most courses on it are repackaged two-day workshops. I run this one as someone who builds multi-agent systems and MCP servers in production.

Updated 19 August 2026

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What you’ll learn

Agent design patterns
When an agent is the right tool, how to scope it, and the patterns that keep it reliable rather than impressive but brittle.
MCP & tool integration
Build and connect Model Context Protocol servers so agents act on your real tools and data.
Multi-agent systems
Coordinate several agents, manage state and avoid the failure modes that sink naive setups.
Evaluation & governance
Test, observe and put guardrails around agents so they are safe to run on real work.

What the masterclass covers

01
When an agent is the right tool
Most "agent" ideas are a workflow plus a prompt. Scoping the ones that genuinely need autonomy saves months of misdirected build.
02
Agent design patterns
Single-loop tool use, planner and executor splits, and the patterns that keep an agent reliable rather than impressive in a demo.
03
Building an MCP server
Exposing a real internal API to a model safely: auth, permissioning, and what never crosses the boundary.
04
Multi-agent coordination
State, handoffs and failure modes. This is where naive setups fall over, and where the production experience earns its keep.
05
Evaluation and observability
How you know the agent works: test harnesses, structured logging, and regression checks that run before users find out.
06
Guardrails and governance
Approval points, human review and permissioning, so autonomy is safe on real work rather than hopeful.

How it runs

Corporate team edition
MULTI-SESSION
An engineering org building agents for its own use cases, worked on live.
Private intensive
PACED TO YOU
A senior engineer or architect who wants the full arc one on one.

What you keep

A scoped agent design for one of your own use cases
An MCP server skeleton against a real internal API
An evaluation checklist your team can rerun
A governance one-pager leadership can actually sign

Who it’s for

For people who will actually ship agents.

Senior engineers and architects
Teams in GCCs building agentic products
Tech leads moving past prototypes into production agents

Where Claude sits in a team’s stack

Skills library
Brand voice, sales voice and tone, internal tone, and the repeated workflows each function runs
Cowork workflows
Drive, Gmail, Calendar, Slack and the project tools the team already lives in
Plugins and MCP
CRM, ticketing, project management and team-specific tooling
Automations
Scheduled and event-triggered runs: news into draft posts, weekly reports out of raw data
Adoption playbook
Review gates, security guardrails, prompt libraries and onboarding patterns

An organisation-wide Claude practice is five layers, not one tool: a Skills library for consistent output, Cowork inside Drive, Gmail, Calendar and Slack, plugins and MCP into CRM and ticketing, scheduled automations, and a written playbook holding the rest together.

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