Agentic AI & Multi-Agent Systems | From Tools to Autonomous Multi-Agent Systems

By Techobytes Technologies

Eligibility: Students and Working professionals

Open to all: B.Tech, B.E., BCA, MCA, B.Sc IT, M.Sc IT, MBBS, MS, MD, Biomedical, Biotechnology, BBA, MBA, B.A., and hobbyists from all backgrounds

Course Content

Duration: 2 Days (14-16 HRS)

Fee includes 2 days workshop pass with Lunch during the workshop.

Go from a single chatbot to a team of AI agents that plan, act, use tools, and collaborate. Why this workshop?

You might have already built an AI that talks. Now you'll build an AI that acts. You'll give your models real tools, let them query live data, reason and self-correct, and team them up into multi-agent systems that tackle tasks no single model could. Every module is hands-on - you'll watch working agents in action, then prompt the AI to build and harden your own. By the end you'll have a production-style, multi-agent application that's truly yours.

What you'll build

● An AI that calls real tools and live APIs - and resists misuse.

● An assistant that answers questions from a real database by writing its own SQL.

● Autonomous agents that plan, act, and catch their own mistakes.

● A team of specialized agents (planner, researcher, writer, reviewer) working together.

● An observable, cost-aware multi-agent system as your capstone.

How we'll work - "vibe coding"

● See it run first: every module opens with a live demo of working agents.

● Then build yours: you prompt the AI in AntiGravity to design, extend, and harden it.

● Ship something every session - real systems, not slideware.

Before you start

●You know (Generative AI Essentials), or equivalent - you should be comfortable building a chatbot and a basic RAG system.

● Python 3.x, Anaconda, and AntiGravity installed

● No database to install - we'll use SQLite (built into Python) with a sample database we

provide.

● Access to a foundation-model API (we'll help you set up keys at the start).

Course modules

Module 1 - Tools & Tool-Oriented Agents

Give your AI hands - then make those hands production-tough.

● Introduction to tool (function) calling.

● Structured outputs and JSON schema design.

● Tool routing, orchestration, and dynamic tool selection.

● Building custom tools: calculator, database query, web search.

● API-based tool integration.

● Handling tool failures and recovery; testing prompt-injection and misuse.

● Demo: An LLM deciding to call a tool and using the result in its answer.

● Project: Extend your Level 1 chatbot with external API tools, then harden it.

Module 2 - Talk to Your Data: SQL Agents

Ask a question in plain English - your agent writes the SQL and answers from a real database.

● Why text-to-SQL: natural language as the interface to structured data.

● Giving the agent a database schema as context.

● The SQL query tool: generate, run, and return results via function calling.

● Reading results and recovering from errors - invalid queries, empty results, retries.

● Guardrails: read-only access, blocking destructive SQL, validating output.

● Demo: Ask a question in English and watch the agent query a SQLite database live.

● Project: Build a natural-language SQL assistant over a sample database.

Module 3 - From Chatbot to Agent

Stop just answering - start acting. Turn your chatbot into an agent that decides and does.

● Transform a chatbot into an autonomous agent.

● Implement perception, planning, and action loops.

● Add memory and tool-decision logic.

● Project: Build a functional autonomous AI agent.

Module 4 - Planning & Reflection Systems

Teach your agent to think before it acts - and to catch its own mistakes.

● Implement reasoning loops.

● Add reflection and self-correction mechanisms.

● Compare single-shot vs reflective outputs.

● Project: Build a self-improving agent that reviews and fixes its own work.

Module 5 - Multi-Agent Collaboration & Design Patterns

One agent is smart. A well-structured team of agents is unstoppable.

● Define agent roles: planner, researcher, writer, and reviewer.

● Enable structured message passing between agents.

● Core patterns: simple, sequential pipeline, parallel, critique/judge, and hybrid.

● Choose the right pattern for the task.

● Demo: Design a multi-agent workflow visually and trace its execution flow.

● Project: Build a working multi-agent workflow using the core patterns.

Module 6 - MCP & A2A Protocols

Plug agents into anything - and let them talk to each other - using open standards.

● Introduction to the Model Context Protocol (MCP).

● Composable AI systems.

● Agent-to-Agent (A2A) communication concepts.

● Context servers and modular design.

● Project: Build a modular AI assistant using MCP principles.

Module 7 - Capstone: Your Own Multi-Agent System

Bring it all together into a real system you designed end to end.

● Combine tools + a SQL/data agent + multi-agent patterns into one system.

● Design a team of agents for a real domain task.

● Add planning, reflection, and failure recovery.

● Build in observability: logging, tracing, token/cost monitoring, and single- vs multi-agent benchmarking.

● Deliver a production-style, shareable multi-agent application.

By the end of Workshop, you will be able to

● Build tool-using, autonomous agents that plan, act, and self-correct.

● Build agents that query real databases from natural language - safely.

● Design multi-agent systems using proven agentic patterns.

● Connect and compose agents using open protocols (MCP / A2A).

● Make agent systems reliable, secure, observable, and cost-efficient.