Agentic AI Masterclass
Teach your teams to design an AI “brain” for your organisation — a central agent layer that watches live data, picks the next best action, and routes work to the right system or person.
The Gap
The true cost of not having central intelligenceTime Lost
12.5 Hours
Every Week
Knowledge workers easily lose 12.5+ hours a week stitching data from different systems just to answer basic questions and prepare reports.
Financial Drag
$1.7M+
In Hidden Cost
For a mid-sized org, fragmented intelligence can translate into $1.7M+ in delays, rework, demurrage fees, compliance fire-drills and missed opportunities.
Decision Friction
5+ System
Per Decision
Teams log into TMS, MES, ERP, CRM, EHR, POS and more — each with partial truth. Static dashboards lag behind reality, so decisions stay reactive and slow.
Who this is for
Delivery Managers & Program Managers
Own day-to-day delivery and want agents to cut manual chasing, follow-ups and status work.
Boards, Chairs & Investors
Need a clear, shippable agent workflow they can break into stories and sprints.
Tech & Data Leads
Need crisp integrations, data flows and guardrails so the agent runs safely in production.
What your org walks away with
Agent playbook for your org
A simple, shared mental model for where agents sit in your stack, when to use them, and how they coexist with dashboards, RPA and other automations.
Shortlist of 3–5 Agentic AI bets
A prioritised list of Agentic AI opportunities across HR, Ops, Sales, Finance and IT — ranked by impact, feasibility and risk.
Agent playbook for your org
Signals, tools, human checkpoints, guardrails and KPIs for one high-value agent that your pod can start building immediately.
Agent playbook for your org
A concrete 90-day plan with milestones, owners and decision gates — so momentum from the room carries into execution.
The Build-Track Curriculum
The Feasibility Layer GenAI Foundations
“Can we actually build this, and what will it cost?”
Key Topics:
- Tokens, context windows, and cost estimation.
- Understanding hallucinations vs. creativity.
- RAG Architecture: Chunking, vector stores, etc.
- Risk management: Data leakage, drift, bias.
Focus:
Workshop Artifact
Build an AI Risk Register & Feasibility Matrix for a real client use-case.
The Tooling Layer LangChain Architecture
“How does the AI connect to our data and memory?”
Key Topics:
- Breaking down use-cases into chains.
- Memory design: Summary vs Entity memory.
- Defining safe APIs & tool boundaries.
Focus:
Workshop Artifact
Draft a RAG Technical Requirement Doc & Tool Inventory List.
The Orchestration Layer LangGraph & Agents
“Who makes the decisions, and when does a human step in?”
Key Topics:
- Graph theory: nodes, edges & gates.
- Human-in-the-loop interruptions.
- Retries, failures & fallback logic.
Focus:
Workshop Artifact
Sketch a Full LangGraph Workflow with human checkpoints.
The Connectivity Layer MCP & Integration
“How do we scale this across the enterprise safely?”
Key Topics:
- MCP fundamentals & standardization.
- Access constraints & permissions.
- Logging, audits & observability.
Focus:
Workshop Artifact
Build an Enterprise MCP Capability Map showing how agents talk to internal tools..
The Ecosystem Layer Agent-to-Agent Systems
“How do multiple agents work together?”
Key Topics:
- Agent roles: Manager, Worker, Reviewer.
- Message passing & task handoffs.
- Supervisor agents & quality control.
Focus:
Workshop Artifact
Create a Multi-Agent RACI Matrix & Supervisor Governance Sheet.
What leaders say after the Agentic AI Masterclass
No fluff, just sharp decisions and a real agent plan.
“Most AI workshops we’ve attended were tool demos. This was the opposite. In two days our leadership pod agreed on one flagship agent, a 90-day plan, and what ‘central intelligence’ actually means for our business. No hype, just sharp thinking.”
Diversified Infrastructure Company
We finally have an agent architecture my team can build.
“We walked in with vague ideas about ‘agents’. We walked out with a concrete architecture, system map and failure modes documented for one real workflow. My team could start designing the MVP the next week without waiting for another strategy deck.”
B2B SaaS Company
Operations reviews moved from defending spreadsheets to designing agents.
“Our ops reviews used to be 90 minutes of people defending their spreadsheets. After the masterclass, we re-framed the same problem as an agent workflow. Now the conversation is: what should the agent do, and where should humans decide? It’s a very different kind of alignment.”
National Logistics Player
We moved beyond dashboards to agents that close the loop.
“We’ve invested years into BI and dashboards, but decisions still ended in Excel. This was the first time someone showed us a practical path from ‘more reports’ to agents that actually close the loop. The Agent Design Canvas is now our default way of evaluating new AI ideas.”
Retail & E-Commerce Group
Business, tech and HR now share one language for AI.
“What I valued most was that business, tech and HR were forced to sit together and design one agent end-to-end. It exposed where our real decision bottlenecks are and gave us a shared language for AI that everyone can use — not just the tech team.”
1,000+ employee technology services firm
Six weeks later, our first agent pilot is already live.
“We’ve done AI trainings before that felt inspiring but didn’t move anything. This one ended with owners, milestones and guardrails for a real agent tied to revenue. Six weeks later, our first pilot is already running with internal users.”
Global SaaS Company
Meet the team
Yash Shah
Head of Emerging Tech
Aju Palleri
AI/ML Solutions Advisor & Innovation
Swapnil Kulkarni
Enterprise Architect
Frequently
Asked
Questions
How many people should attend?
12–25 people works best: a mix of business owners, product, data and engineering — ideally the pod that will own your first agent.
Is this technical or business-focused?
Both. We stay anchored in business and P&L while going deep enough for builders to translate the masterclass into shipped systems. Leaders get clarity on where and why; builders get clarity on how and with what.
Can this connect to a larger AI program with Swabhav?
Yes. Many partners use this as the front door into Tech & AI hyper-personalized programs— from first pilots to a full Agentic AI roadmap, internal AI pod and CoE.
How much customisation is possible?
The backbone stays consistent — core curriculum + one flagship agent + a 90-day plan — but examples, use-cases and depth are tailored to your industry, systems and AI maturity.