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Best Agentic AI Course in Mohali

Master Autonomous
AI Agent Development

Go beyond simple chatbots. Learn to build custom AI agents, collaborative multi-agent teams, and self-directed automation workflows that plan, decide, and execute complex business goals autonomously.

View Syllabus

Techcadd works with Generative AI Companies, U.S. Government Agencies, Enterprises & Startups

Inside The Agent Loop

Watch an Autonomous Agent Think, Act & Adapt

This Thought → Action → Observation → Result loop is the exact reasoning pattern you'll learn to design, debug, and deploy.

agent_session.trace — goal: "Refund order #4892 and notify the customer"
THOUGHTI need the order details before I can validate refund eligibility.
ACTIONcall_tool( get_order, id = 4892 )
OBSERVATIONOrder found — status: delivered, eligible for refund window.
THOUGHTEligible. I'll process the refund, then draft a confirmation email.
ACTIONcall_tool( process_refund, id = 4892 ) → call_tool( send_email, template = "refund_confirmed" )
RESULTRefund processed and customer notified — no human hand-off required.▏
Thought — internal reasoning Action — tool / API call Observation — tool output Result — final outcome
Why Learners Choose This Program

Hands-On Agent Engineering. Real Multi-Agent Labs. Career-Ready Skills.

Reason 01

End-to-End Agent Ownership

Keep the source code, prompts, and agent architectures you build — ready to extend into real products.

Reason 02

Multi-Agent Lab Access

Hands-on lab time inside LangGraph, CrewAI, and AutoGen building teams of cooperating agents.

Reason 03

Framework-Agnostic Curriculum

Learn the underlying agent patterns first, so you can pick up any framework — current or future — with confidence.

Reason 04

Mentor-Led Code Reviews

Engineers review your agent logic, tool schemas, and prompts, catching failure modes before production.

Reason 05

Placement & Portfolio Support

Graduate with a deployed multi-agent project, a GitHub portfolio, and 100% placement assistance.

Course Details

Agentic AI Course Details at a Glance

Duration

3 to 5 months, depending on batch pace

Mode of Learning

Classroom training in Mohali & Chandigarh, plus live online batches

Eligibility

Developers, data professionals & career switchers — some coding background helpful

Batch Options

Weekday, weekend and evening batches available

Certification

Industry-recognized Techcadd Agentic AI Certificate

Fees

Affordable fee structure with EMI options — ask your counsellor

Core Pillars of Autonomy

Autonomous Planning & Reasoning

Agents break high-level goals into ordered sub-tasks, evaluate intermediate progress, and dynamically re-plan when a step fails — instead of following a fixed script.

Goal decomposition Self-correction loops ReAct & Chain-of-Thought
"An agent that can't re-plan when reality disagrees with its plan isn't autonomous — it's just automated."

Multi-Agent Orchestration

Coordinate teams of specialized agents — planner, coder, reviewer, support — that share context, delegate sub-tasks, and critique each other's output before it reaches a user.

Role-based agent teams Shared memory & handoff Supervisor patterns
"The hardest bugs in agent systems aren't in one agent's logic — they're in how agents hand work off to each other."

Autonomous Tool & API Use

Define function-calling schemas so agents can execute code, call REST APIs, search the web, and operate external software — safely, and with structured, validated outputs.

Function-calling schemas Sandboxed code execution External API orchestration
"Give an agent a well-typed tool schema and it stops guessing — it starts operating."

Memory & Retrieval Grounding

Give agents short-term working memory for a task and long-term vector memory across sessions, so answers stay grounded in real documents instead of the model's imagination.

Vector memory stores Conversation summarization RAG-grounded answers
"Memory is what turns a stateless chatbot into an agent that actually remembers your business."

Multimodal & Voice Agents

Extend agents beyond text with speech recognition, natural voice synthesis, and image understanding — so they can see, hear, and respond in real-world formats.

Speech-to-text pipelines Vision-enabled reasoning Real-time voice agents
"Most real business inputs aren't clean text — they're calls, screenshots, and scanned forms. Multimodal agents meet users where they are."

Evaluation & Guardrails

Score agent traces for task success, monitor for drift, and enforce guardrail policies that block unsafe or off-policy tool calls before they ever execute.

Trace-level evaluation Guardrail policies Human-in-the-loop review
"You don't ship an autonomous agent on faith — you ship it on evaluation data and a guardrail policy that's been stress-tested."
Get Started!
The Architecture Behind The Craft

One Orchestrator. A Mesh of Specialist Agents & Tools.

This is the orchestration pattern you'll design in the program — a central agent delegating to specialists and calling real tools and services.

Orchestrator Agent
Researcher Agent
Coder Agent
Reviewer Agent
Support Agent
REST APIs
Vector Memory
Databases
Webhooks
Researcher Agent
Coder Agent
Reviewer Agent
Support Agent
REST APIs
Vector Memory
Databases
Webhooks
Real-World Case Study

A Glimpse Into Our AI-Powered Innovations

Discover the actual production-grade agents you will design and deploy during the training program.

Case Study

E-commerce Autonomous Support Agent

AI-based customer behavior analysis, dynamic user interaction, and real-time backend updates integrated into a unified e-commerce platform workflow to scale support capacity.

LangGraphRAG GroundingShipping APIDatabase Tool Calls
Challenge:

Scaling customer support teams to resolve complex multi-step user inquiries (order returns, inventory status, custom coupon queries) without human agent intervention.

Agile Approach

  • Implemented LangGraph for stateful dialog flow routing.
  • Designed tool calls connected to shipping & database APIs.
  • Grounded answers with RAG over private customer policies.

Impact & Outcome

  • 60% reduction in support ticket response latency.
  • 35% increase in automated user query resolution.
  • Under 40% human supervisor escalation rate.
AI Agent Chat
Online
Hi, I want to return my order #4892. It's too small.
Checking database for order #4892...Tool Call: queryDatabase()
Found your order! I've initiated the return request. I'm emailing your shipping label now.Tool Call: sendEmail()
Course Curriculum

A Structured Path From LLM Basics to Multi-Agent Systems

01

Foundations of Agentic Systems

LLM reasoning fundamentals, tokens, and how agents differ from simple chat completions.

02

Prompt Engineering for Agents

System prompts, ReAct, and Chain-of-Thought patterns that drive reliable reasoning.

03

Tool Calling & API Integration

Function schemas, sandboxed execution, and connecting agents to real services.

04

Memory & Retrieval Grounding

Vector memory, conversation summarization, and RAG-grounded responses.

05

Single-Agent Frameworks

Building production agents with LangChain and stateful LangGraph flows.

06

Multi-Agent Orchestration

Coordinating specialist agent teams with CrewAI and AutoGen.

07

Evaluation & Guardrails

Trace-level evaluation, safety policies, and human-in-the-loop review.

08

Deployment & Capstone Project

Shipping a monitored agent API and presenting your capstone build.

How We Teach

A Learning Process Built for Real Agent Delivery

1

Foundations & Environment Setup

Python refresher, LLM APIs, and the tooling you'll use throughout the program.

2

Prompting & Reasoning Pattern Labs

Hands-on practice with ReAct, Chain-of-Thought, and self-correction loops.

3

Tool & API Integration Workshops

Wire agents to real APIs, databases, and code execution sandboxes.

4

Single-Agent Build Sprint

Ship a complete single-agent application from scratch, end to end.

5

Multi-Agent Orchestration Lab

Design a team of specialist agents that delegate, review, and merge work.

6

Capstone: Deploy & Present

Deploy your agent team and walk away with a reviewed, portfolio-ready project.

Ready to Engineer the Future of AI?

Enroll in the premier Agentic AI developer certification in Mohali today. Start designing production-grade autonomous agent networks from scratch under expert guidance.

Got Questions?

Frequently Asked Questions

What exactly is covered in this Agentic AI course?

You will learn autonomous agent planning and reasoning (ReAct, Chain-of-Thought), tool/function calling, memory and RAG grounding, single-agent frameworks like LangChain and LangGraph, multi-agent orchestration with CrewAI and AutoGen, and evaluation and guardrails.

Do I need prior experience with prompt engineering or LLMs?

Basic familiarity with how LLMs work is helpful but not required. The first module covers LLM reasoning fundamentals and prompting patterns before you touch any agent framework.

How long does the course take and what batch options are available?

The program typically runs 3 to 5 months, with weekday, weekend and evening batches so working professionals and students can both attend comfortably.

How is Agentic AI different from the Prompt Engineering or Generative AI courses at Techcadd?

Prompt Engineering focuses on directing a single model well; this course goes further into autonomy — agents that plan, call tools, remember context, and coordinate with other agents to complete multi-step goals with minimal human intervention.

Which frameworks and tools will I actually build with?

You will get hands-on lab time in LangChain, LangGraph, CrewAI, and AutoGen, plus vector databases for memory, REST API tool calling, and evaluation tooling for scoring agent traces.

Will I build a real multi-agent project, or is this mostly theory?

The program is lab-driven. You will ship a single-agent build sprint, then design a full multi-agent team (planner, coder, reviewer, support) for your capstone, deployed and portfolio-ready.

Is job placement assistance included?

Yes. Techcadd provides 100% placement assistance, including resume building, mock interviews, and direct connections with hiring partners across Mohali and Chandigarh.

What certificate do I receive after completing the course?

You receive an industry-recognized Techcadd Agentic AI Certificate, validating your practical ability to design, orchestrate and deploy autonomous multi-agent systems.

What job roles can I target after this course?

Graduates typically move into roles such as AI Agent Developer, Agentic AI Engineer, AI Automation Engineer, or LLM Application Developer, building on existing developer or data skill sets.

Will I learn how to keep agents from taking unsafe or unintended actions?

Yes. The evaluation and guardrails module covers trace-level evaluation, drift monitoring, and guardrail policies that block unsafe or off-policy tool calls before they execute — this isn't an afterthought, it's a full module.

Is there support for beginners with limited coding background?

Yes. The foundations module covers the Python and API basics you need before agent-specific content begins, and mentors are available throughout for one-on-one doubt-clearing.

How do I get pricing and the detailed syllabus?

Fees, the detailed module-wise syllabus and current batch schedules are shared during a free counselling session — book a free demo class to get the full breakdown.

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