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Compare plain code, retrieval, workflow automation and an agent on the same task.
Private training for developer teams · Across India
Agentic AI Training India — Build Autonomous AI Agents with LangGraph & CrewAI
Take your engineering team from agent demos to safe, controlled workflows, with clear tools, state, testing, approval steps and an owner for running them.
- Audience
- Developer, AI, data and platform teams
- Delivery
- Private, live online or at your office
- Scope
- Design, building, testing and control
- Evidence
- A working workflow and reviewed failure cases
Direct answer
What is Agentic AI Training India?
Agentic AI Training India is private training for developer, AI engineering, data and platform teams. It covers the engineering choices behind AI agents: when an agent is the right answer, what tools and data it may touch, how it keeps track of state, where a person approves, how you test and trace it, and how you hand it over to production. LangGraph and CrewAI can be used as current examples, but the course is about design, not one framework. Training runs live online across India, or at your office when agreed in the proposal. Teams looking for an agentic AI course in India get private AI agents training built around their own stack.
Updated
Who this page is for
Relevant audiences
- Software and AI engineering teams
- Data and ML engineers adding tool-driven workflows
- Platform and architecture teams setting agent standards
- Engineering leaders judging an agent project
Useful when
- Several teams are experimenting with no shared agent design
- Tools and approvals are added after the prototype instead of designed in from the start
- You need a way to test agents that works whatever the framework
- A pilot needs answers on production, security and who runs it before you invest
Scope boundary: Training does not deploy an autonomous system to production, or approve a framework, a security setup or a business case. Your engineering, security and operations owners keep the authority to release.
Decision guide
Turn “build an agent” into an engineering decision
A good programme links the design to a real task, the known cost of mistakes and a clear owner for running it.
Which tools and data are allowed?
Decide identity, least access, input formats, checks, logging and protected information before connecting any system.
Where must a person decide?
Put approval, escalation and override at each important action, not only at the end.
What has to pass before release?
Agree realistic tests, failure limits, monitoring, rollback and owners before a pilot grows.
Team engineering practice
Use agent patterns on safe, realistic business scenarios
We use your own examples only when access and confidentiality allow. Safe practice systems are the default.
Research and summary workflow
Plan, find and combine evidence from approved sources.
- Practise
- Trace each claim, test what happens when evidence is missing and require a cited final answer.
- Control
- The agent does not make sources reliable or answers true on its own.
Support triage workflow
Sort a request, find the relevant knowledge and suggest a routed reply.
- Practise
- Test unclear requests, wrong priorities, escalation and unsafe or unsupported answers.
- Control
- A model does not close or prioritise an important case on its own.
Document review workflow
Combine pulling out details, comparing documents, raising questions and human review.
- Practise
- Keep the document version, citations, missing information and approval.
- Control
- The output is not legal, compliance or expert sign-off.
Data-analysis agent
Connect a controlled query or analysis tool to an agent workflow.
- Practise
- Limit what it can run, check its queries and keep data apart from interpretation.
- Control
- No open database access and no unchecked business decisions.
Approval and recovery
Pause, resume, retry and escalate while keeping state and evidence.
- Practise
- Simulate a tool failure, a half-finished job and a reviewer saying no.
- Control
- Retries need limits, and a paused workflow still needs an owner.
Testing with traces
Look at each step, tool call, answer quality, speed and cost.
- Practise
- Group failures by type and write tests that catch them again after changes.
- Control
- A dashboard is no substitute for review by experts on realistic cases.
Options
Choose the right scope
The proposal can focus tightly on agents, or link them to a wider AI engineering skills plan.
Agent design workshop
Frame one use, the alternatives, the controls and the tests before building.
Best for: Architects, leads and mixed engineering groups
- Deciding on the use
- Design and controls
- Test plan
Hands-on agent engineering
Build and test controlled agent workflows in one agreed main framework.
Best for: Python and AI application developers
- Tools and state
- Human approval
- Debugging with traces
Production AI Engineering
Combine agents with RAG, MCP, APIs, testing and deployment.
Best for: Teams that need end-to-end AI application skills
- Joined-up design
- Five project patterns
- Deployment
AI training for developers
Compare coding with AI and building AI applications across a larger engineering team.
Best for: L&D and engineering leaders planning skills
- Mapping roles
- Choosing routes
- Evidence of adoption
Framework workshop versus full production programme
A framework lab speeds up learning, but production skills need wider engineering evidence.
| Decision point | Focused agent training | Production AI Engineering |
|---|---|---|
| Main question | How should this agent workflow be designed and tested? | How should the whole AI application be built and run? |
| Scope | Tools, state, orchestration, approval and traces | RAG, agents, MCP, services, deployment and LLMOps |
| Best for | Teams already strong with LLMs | Teams building complete AI systems |
| Price | Confirmed for the chosen modules and exercises | Confirmed for the full team programme |
Practical value
What your team should be able to show
Skills show in the design, the code and the evidence, not just in knowing the terms.
A written agent decision
The team can explain why the design uses an agent, and which alternatives it rejected.
A controlled workflow
Tools, permissions, state, approval, errors and stopping rules are all clear.
A test setup
Realistic tasks and failure cases can be run again after every change.
A production handover note
Owners for security, platform, monitoring, cost, support and release are named.
Method
From the right course to working code
We confirm the route before training starts, so everyone practises at the right level and nobody mistakes a tutorial demo for something ready for production.
- 01
Agree the goal
Confirm whether you need a public course, a private team workshop or a wider engineering programme.
- 02
Check skills and setup
Agree the coding level, accounts, model access, practice data, repository and any security limits.
- 03
Confirm the scope in writing
Dates, length, topics, exercises, format, fees and what is included are set out before you book.
- 04
Build and review
Short explanations, then building, testing, debugging and feedback from the trainer.
- 05
Plan the next step
Everyone leaves with reviewed work, its known limits and a plan for more practice or a real build.
What your team can take away
How deep we go depends on prerequisites, time and access.
Use and design worksheet
Task, alternatives, tools, state, data, authority and limits.
Reference workflow
A controlled build in the agreed framework and practice setup.
Tests and failure pack
Test cases, results, traces, known gaps and pass rules for future changes.
Team engineering checklist
Questions on security, data, review, monitoring, cost and release.
Safe application
Production safeguards are taught as you build
In every exercise, you can see who has access, how the work is tested, how failures are handled and where a person decides.
Use approved data and accounts
Labs use approved model access, safe documents and practice connections. Passwords, keys and private data never go in prompts or source control.
Test on realistic tasks
A demo that works is tested on normal, hard and failing cases before anyone calls it reliable.
Limit tools and actions
Permissions, input checks, timeouts, budgets and approval steps limit what an agent or connection can do.
Keep a person reviewing
A qualified person checks sources, outputs, tool calls and important actions before release or use.
Primary sources
Current framework references
The written agenda fixes the versions used, and relies on the makers’ own documentation for anything that changes.
Training in action
See Technovids training in practice
Real photos from Technovids training sessions. They show how we teach, not a promised result for your group.
Photo 1 of 10: Hands-on session 
Photo 2 of 10: Group photo 
Photo 3 of 10: Hands-on session 
Photo 4 of 10: Group photo 
Photo 5 of 10: Classroom session 
Photo 6 of 10: Group photo 
Photo 7 of 10: Computer-lab session 
Photo 8 of 10: Group photo 
Photo 9 of 10: Classroom session 
Photo 10 of 10: Group photo
Trainers
Meet the Technovids trainer panel
We choose the trainer once we know who is attending, how deep the subject goes and how it will be delivered.
Pankaj Rana
Co-founder & Lead Instructor, Technovids Consulting
Pankaj is a co-founder and the lead instructor at Technovids Consulting. His programme work spans practical AI adoption, AI engineering, evaluation, responsible production AI, data analytics and corporate learning design, with an emphasis on hands-on exercises connected to workplace tasks.
Expertise:
- Practical AI adoption
- Prompting and evaluation
- Responsible AI
- Corporate learning design
Junaid Khateeb
AI & Machine Learning Trainer, Strategist and Consultant
Junaid is an AI and machine-learning trainer, strategist and consultant with more than 22 years of training experience. His work combines enterprise AI education, business-centred adoption, responsible AI practices and applied solution design, helping professional audiences connect AI concepts to real organisational needs.
M.E. in Computer Engineering, specialising in Artificial Intelligence · author of programming books on Python, Java and C++
Expertise:
- Enterprise AI and ML training
- AI strategy and adoption
- Responsible AI
- Applied AI solution design
Pradeep Varadarajan
Technical Trainer & Curriculum Architect
Pradeep is a technical trainer and curriculum architect with more than 20 years of experience across enterprise technology, cloud infrastructure and advanced AI systems. He designs practitioner-focused programmes, labs and assessments covering AI, LLM systems, Python and machine learning, translating complex technical subjects into structured learning experiences.
Bachelor of Engineering in Electrical Engineering · instructor-led, virtual and blended delivery
Expertise:
- AI and LLM systems
- Curriculum architecture
- Python and machine learning
- Hands-on lab design
Your proposal names the trainer. Not every trainer shown here teaches every programme.
To book a trainer for your team, call +91 86183 46384 or email [email protected].
Updated
Client & participant proof
Developer and participant perspectives
Feedback shared with permission and photos from our sessions show how Technovids trains. They are not a promised result from this course.
Organisations we have delivered training for
Hands-on training for people and teams in AI, data, cloud, productivity and workplace skills.
Participant voice
Participant experiences across workplace roles
What people we trained found useful in their day-to-day work.
- PriyanshaSoftware Developer
The AI training from Technovids helped me gain a better understanding of Generative AI, prompt engineering, and practical AI use cases for software development. The course was clear, interactive, and focused on real-world applications. It gave me confidence to explore AI tools more effectively in my work.
- MangeshSoftware Engineer · Siemens
Technovids conducted the AI training in a very effective and engaging manner. The sessions were practical, easy to follow, and focused on how AI can be used in real software engineering and business workflows. It was a valuable program for understanding the current direction of AI adoption in the industry.
- KrantiSenior Software Engineer · Siemens
The AI training delivered by Technovids was practical, structured, and relevant for software professionals. The sessions connected AI concepts with real industry use cases, making it easier to understand how AI can be applied in development, automation, and productivity improvement. It was a valuable learning experience.
- Rahul DesaiSenior Software Engineer
I had been trying to transition from traditional backend development to Generative AI, but self-learning was getting overwhelming. The AI Engineering course at Technovids gave me the exact structured roadmap I needed. The modules on LLM architectures and practical prompt engineering are brilliant. It’s not just theory; you actually build functional AI applications. Highly recommend this for working professionals looking to upgrade their tech stack.
- Sneha ReddyFull Stack Developer
What I loved most about the Technovids GenAI program is how up-to-date the curriculum is. Instead of outdated practices, we learned to integrate the Model Context Protocol (MCP) and practically apply tools like Claude and Cursor for AI-assisted coding. It felt like I was learning the exact skills that top tech companies are hiring for right now. The pace is perfect, and the support is great.
- Neha GuptaBackend Developer
I was initially intimidated by the math and complexity behind AI, but the trainers at Technovids explain things in such an accessible way. They break down everything from basic prompt engineering to complex integrations without losing you in the jargon. The session recordings and project materials are top-notch. If you are serious about becoming an AI Engineer, Technovids provides the perfect launchpad.
Frequently asked questions
These answers cover what this page offers. Your written proposal sets out the terms for your own programme.
Who is this corporate training designed for?
It is for software developers, AI engineers, data or ML engineers, tech leads and platform teams who already know programming and APIs. A separate design-only route can be built for technical decision-makers.
Is Python required?
We normally use Python for the hands-on work. Another supported language, or a design-first route without much code, can be considered, but the proposal must state the lab stack and prerequisites.
Can the training use our enterprise use case?
Yes, when the problem can be shared safely and the data, accounts and systems are approved. Otherwise the team uses a practice case with the same shape.
Are LangGraph and CrewAI both included?
They can be compared, but we don’t assume equal hands-on time for both. The main framework, versions, modules and exercises are confirmed to fit your design and the time available.
Can training be delivered onsite anywhere in India?
Private training can run live online or at your office when agreed. The proposal confirms location, travel, facilities, group size, lab readiness, length and price.
Does the programme include production implementation?
No. Training builds skills and the agreed practice work. Production discovery, building, security review, integration, testing and support need a separate scope.
Talk to Technovids
Get a training proposal for your team
Tell us who the training is for, roughly how many people, and what you want them to do better at work. The Technovids team will scope it and reply with a written proposal.
Request received
Thank you — your request has been sent to the Technovids corporate training team. If you would rather speak to someone directly, the phone and WhatsApp details are at the top and bottom of this page.
Technovids Consulting
Bengaluru head office. India-wide live delivery.
2nd Floor, Chandrodaya Complex, 19/19, 24th Main Rd, near Hanuman Temple, Agara Village, 1st Sector, HSR Layout, Bengaluru, Karnataka 560102.
Please contact us before visiting the head office. The HSR Layout address is a head office, not a walk-in public classroom.