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DTA-26AvailableAI & Agentic AIAgentsWorkflowsMCPMulti-agent patternsLangGraphPython

Agentic AI Engineering — Python

Build agents that carry multi-step work in Python — with state, plans and boundaries

Fee
₹6,000
Duration
40 guided hours
Mode
Online / Offline
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Designed for

  • Python developers who finished the stage-one course and want systems that act
  • Backend engineers whose teams are being asked "can an agent do this"
  • Teams that need agents inside real services, not notebook demos
New · Deesha AI

The AI line is open.

Course curriculum

10 progressive modules

Duration: 40 guided hours

  1. Foundation
  2. Intermediate
  3. Advanced

  1. FoundationSessions 1–3

    From AI features to agents

    • The agent loop — goal, decide, act, observe, continue, complete
    • What separates an agent from a workflow, precisely
    • The cost of agency, and when not to pay it

    What you will build

    Three real problems routed to prompt, workflow or agent — each decision defended in writing, most of them not agents, which is the lesson.

    Module 1

Batches

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Who teaches this

Learn with experienced practitioners

All trainers and mentors

Each batch is assigned its own trainer from this group, named before you enrol.

  • Vishal Shah

    Vishal Shah

    Founder & Principal Trainer

    Two decades building and teaching commerce, banking and cloud platforms — still writing code

    • Java & Spring Boot microservices
    • TypeScript, React, Angular & Next.js
    • Composable commerce (commercetools)
    Full profile →
  • Shrenik Shah

    Shrenik Shah

    Principal Trainer

    Cloud and AI architect who has upskilled over 5,000 engineers in Java, React, Python and cloud

    • Python — Django, Flask, GenAI & agentic workflows
    • Java & Spring Boot
    • React & Angular, micro-frontend architecture
    Full profile →

Detailed course information

Full course brief

Stage one taught your Python stack to think; this course teaches it to act. The loop comes before the framework: you build a bare agent you can explain line by line, then let LangGraph and friends argue for their complexity against your own baseline — with persistent state, reviewable plans, human approval and boundaries throughout.

Who this is for

You need the stage-one course — AI Application Engineering with Python — or equivalent: working model APIs, tool calling and RAG. The Agent Builder Lab is the two-day on-ramp that precedes this course on the pathway.

Where it leads

The agentic stage of the AI Application Engineer pathway — followed by the AI Reliability Lab and the production stage.

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