Live AI Agent Workshop
Build Your First AI Agent (MCP + Tools)
This workshop is for people who are done watching toy chatbot demos and want to build an AI agent that actually does work. If you have been searching build AI agent, MCP protocol, AI agent tutorial, or tool calling LLM, this page is written for that exact intent — and the session delivers on it with code, tools, and a complete agent loop.
You will build an agent that can inspect files, call APIs, execute code, and make progress across multi-step tasks. Along the way, you will understand why the Model Context Protocol matters, how tool schemas shape behavior, and where agents become unreliable if you skip the engineering discipline.
Real tool use
The agent will not just talk. It will read files, call APIs, execute code, and complete multi-step workflows.
MCP explained practically
You will learn the Model Context Protocol as an engineering interface, not as a vague buzzword.
High-value skill in 2026
Agent engineering is rapidly becoming a must-have capability for builders, platform teams, and technical founders.
2 hours
Code-first workshop
₹499 / $19
Geo-aware pricing
Live + recording
Useful even if you rewatch later
Search intent
Why this page is intentionally built to rank for high-intent workshop searches
Search visibility is strongest when a page resolves the real question behind a keyword. This section turns the page into a stronger resource for people actively searching for this exact topic.
This workshop is for people who are done watching toy chatbot demos and want to build an AI agent that actually does work. If you have been searching build AI agent, MCP protocol, AI agent tutorial, or tool calling LLM, this page is written for that exact intent — and the session delivers on it with code, tools, and a complete agent loop.
You will build an agent that can inspect files, call APIs, execute code, and make progress across multi-step tasks. Along the way, you will understand why the Model Context Protocol matters, how tool schemas shape behavior, and where agents become unreliable if you skip the engineering discipline.
The shift from chat interfaces to action-taking systems is real. Teams are exploring coding agents, research agents, internal copilots, and operations assistants. The engineers who understand tool calling, MCP, and agent reliability will have a major advantage because they can move from AI curiosity to automation that actually ships.
If you want your first AI agent to be something more impressive than a prompt chain, this workshop gives you the right starting architecture.
Keyword focus for this workshop page
- build ai agent
- mcp protocol
- ai agent tutorial
- tool calling llm
- model context protocol
- llm agents with tools
What you will build
What you will build in this AI agent tutorial workshop
This is not a vague webinar page. You should know exactly what you are getting before you register. The bullets below are intentionally specific so the workshop attracts serious learners with real engineering intent.
A real agent loop
Define task boundaries, instructions, tool selection logic, and output contracts so the agent behaves like a system instead of a lucky prompt.
MCP-aware tool layer
Expose tools and resources through the Model Context Protocol so the agent can discover and use capabilities cleanly.
API-calling workflow
Let the agent fetch external data, transform it, and use it as part of longer reasoning chains.
File and workspace intelligence
Enable the agent to read files, inspect context, and reason across a project workspace like a useful developer assistant.
Code execution path
Give the agent a safe way to verify outputs, run snippets, and solve tasks more reliably through computation.
Debug and guardrail patterns
Learn how to diagnose tool misuse, weak prompts, and agent drift before they become product problems.
1const tools = [readFile, runCode, fetchApi, searchDocs]2 3while (!task.done) {4 const plan = await model.respond({5 messages,6 tools,7 instructions,8 })9 10 if (plan.toolCall) {11 const result = await executeTool(plan.toolCall)12 messages.push(resultToMessage(result))13 continue14 }15 16 return plan.finalAnswer17}This preview is representative, not decorative. The workshop is built around code, architecture reasoning, and the exact implementation details that make these workflows usable in real projects.
Outcome and social proof
Why builders are aggressively learning AI agent engineering right now
The shift from chat interfaces to action-taking systems is real. Teams are exploring coding agents, research agents, internal copilots, and operations assistants. The engineers who understand tool calling, MCP, and agent reliability will have a major advantage because they can move from AI curiosity to automation that actually ships.
Outcome
Your first serious agent project
A working artifact that proves you understand more than chat UI wrappers.
Outcome
MCP fluency
You will know what the Model Context Protocol is solving and how it fits into modern agent tooling.
Outcome
Tool schema intuition
You will understand how function signatures and outputs shape LLM behavior more than most people realize.
Outcome
Agent debugging confidence
You will know how to reason about failures, drift, and guardrails instead of saying “the model was weird.”
Practical application
How you can use Build Your First AI Agent (MCP + Tools) after the workshop ends
The goal is not just to attend a live session. The goal is to leave with a mental model and implementation pattern you can reuse in interviews, portfolio work, internal projects, or customer-facing products.
Implementation takeaways
A real agent loop
Define task boundaries, instructions, tool selection logic, and output contracts so the agent behaves like a system instead of a lucky prompt.
MCP-aware tool layer
Expose tools and resources through the Model Context Protocol so the agent can discover and use capabilities cleanly.
API-calling workflow
Let the agent fetch external data, transform it, and use it as part of longer reasoning chains.
File and workspace intelligence
Enable the agent to read files, inspect context, and reason across a project workspace like a useful developer assistant.
Career and project leverage
Your first serious agent project
A working artifact that proves you understand more than chat UI wrappers.
MCP fluency
You will know what the Model Context Protocol is solving and how it fits into modern agent tooling.
Tool schema intuition
You will understand how function signatures and outputs shape LLM behavior more than most people realize.
Agent debugging confidence
You will know how to reason about failures, drift, and guardrails instead of saying “the model was weird.”
Who this is for
Who should join this build AI agent workshop?
The strongest SEO pages also help visitors self-qualify quickly. These personas make it obvious whether the workshop matches your current stage and goals.
Software engineers
You want a real build AI agent workflow you can adapt for coding, support, research, or internal productivity.
Platform or tooling teams
You care about MCP, tool discovery, reliability, and how to design an interface that models can use well.
Founders and product builders
You want to understand whether an AI agent can create leverage in your product without becoming uncontrollable.
Serious AI learners
You want a practical AI agent tutorial that goes beyond framework marketing and teaches durable concepts.
Agenda
Detailed agenda: how the live AI agent session unfolds
This is a detailed live workshop, not a teaser. The timeline below shows exactly how the two-hour session is structured so you can assess whether the depth matches what you need.
What actually counts as an AI agent?
We distinguish chatbots, tool-using agents, workflows, and multi-agent systems so your mental model starts clean.
Understand the Model Context Protocol
You will see what MCP standardizes, how tools and resources are exposed, and why this matters for interoperability.
Design the agent loop
We define instructions, task framing, completion criteria, and the conversation contract that guides tool usage.
Add tools for APIs and files
The agent gains the ability to fetch data and inspect workspace context — the moment it becomes far more than a chatbot.
Add code execution
We show how computation and validation make an agent more reliable, especially on technical tasks.
Debug failure modes and add guardrails
This section covers retries, schema design, tool boundaries, and how to prevent the agent from wandering.
Run end-to-end tasks
Watch the agent handle realistic multi-step tasks while we inspect what it is doing and why.
Q&A on your own agent ideas
Bring your coding, research, support, or workflow automation ideas and get grounded feedback.
Prerequisites
Short prerequisites, realistic expectations
You should not have to guess whether you are ready. This section keeps the bar honest and practical.
- Basic coding familiarity is enough.
- Helpful if you have seen APIs before, but not required.
- No prior MCP knowledge needed.
- Best for people who want to build, not just talk about AI.
Mid-page CTA
A fast decision checkpoint
If you want your first AI agent to be something more impressive than a prompt chain, this workshop gives you the right starting architecture.
You get live delivery, recording access, code-first teaching, and a clear path to applying the material immediately after the workshop.
What is included
What you get beyond the live session
Good workshop pages do not stop at the headline. These are the assets and follow-through pieces that make the purchase feel durable rather than disposable.
Live recording
Rewatch the agent build and reasoning walkthrough whenever you need.
Starter code patterns
Use a clean base for your own tool-calling and MCP experiments.
Architecture notes
Keep the mental models for agent loops, guardrails, and reliability.
Certificate of completion
Useful for documenting your learning journey.
Interactive Q&A
Ask questions about your own product or side-project ideas.
Why now
Why this topic deserves deliberate practice right now
A good workshop is not only about the content. It is about timing. These topics are becoming more valuable in hiring, product work, and AI engineering portfolios because they sit at the intersection of implementation skill and system judgment.
2 hours
Code-first workshop
₹499 / $19
Geo-aware pricing
Live + recording
Useful even if you rewatch later
This extra depth is intentional. High-intent visitors searching for a serious workshop should be able to understand the outcomes, the agenda, the delivery style, and the credibility of the instructor before deciding to register. That is how this page is structured.
Instructor
Learn from Debasish Maji
Debasish Maji built Atlassian's Rovo AI Agent and has 12+ years across Atlassian, PhonePe, and Infosys. That gives this workshop unusual credibility: you are learning AI agents from someone who has already built one in a serious product environment.
Why this matters
12+
Years of engineering depth across product, backend, and AI systems.
Rovo
Built Atlassian's Rovo AI Agent, bringing direct practitioner credibility.
PhonePe
Experience in high-scale engineering environments where reliability matters.
Infosys
Strong software-delivery foundation that shows up in how the material is taught.
Built Atlassian's Rovo AI Agent
You get direct insight from someone who has shipped agent behavior in the wild.
12+ years of engineering depth
The workshop reflects platform thinking, product reliability, and real-world constraints.
Teaches why, not just how
You will understand the logic behind agent architecture instead of copying snippets blindly.
Strong code-first delivery
The session is engineered to be practical, fast, and useful immediately after it ends.
FAQ
Questions people ask before registering
Each answer below is also marked up as JSON-LD FAQ schema so both humans and search engines understand exactly what this page resolves.
Final CTA
Reserve your seat and build an AI agent with tools, MCP awareness, and real engineering discipline.
This workshop page is intentionally detailed because serious learners want substance before they buy. If the depth here matches what you were hoping to learn, the live session will be even more valuable.