Workflow or agent?
Use fixed orchestration when steps are known; add agentic choice only where runtime reasoning provides measurable value.
Live-online developer training · Technovids Bengaluru
Agentic AI Course Bangalore
Learn to design, build and evaluate controlled AI-agent workflows—from tool calling and state to approval gates, traces and failure recovery.
- Audience
- Developers, AI engineers and technical leads
- Duration
- 8 hours · live online
- Bengaluru
- HSR Layout head office · not a classroom
- Engineering focus
- Tools, state, evaluation and approval gates
Direct answer
What is the agentic AI course in Bangalore?
This agentic AI course in Bangalore is a developer-focused course from Technovids, which has its head office in HSR Layout, Bengaluru. It covers when an agent is appropriate, tool calling, stateful workflows, retrieval, human approval, evaluation and operational safeguards using current frameworks such as LangGraph and, when relevant, CrewAI. Public-cohort availability, dates and fees are confirmed before enrolment; public classes run live online rather than at the head office. Private corporate delivery can be scoped separately for Bangalore teams. It is an AI agents course for Bangalore developers, taught live online.
Updated
Who this page is for
Relevant audiences
- Python developers building LLM applications
- AI and data engineers moving beyond single-turn prompts
- Technical leads evaluating agent architecture
- Bangalore teams requesting a private technical programme
Useful when
- A workflow needs tools, state or multiple controlled steps
- An agent demo works but failure modes and evaluation are unclear
- Teams need to compare LangGraph, CrewAI and simpler alternatives
- Developers must add approval and observability before production
Scope boundary: The course does not promise autonomous operation, production readiness or a fixed public start date. Framework access, API costs, projects and delivery are confirmed for the selected option; the standard course runs 8 hours.
Decision guide
Decide whether the problem needs an agent at all
A deterministic function, retrieval pipeline or human workflow is often safer and easier to operate than an open-ended agent.
What may the model do?
List allowed tools, arguments, data, side effects, budgets and actions requiring approval.
How is state managed?
Define what persists, who can access it, how retries work and when the workflow stops.
How will it be evaluated?
Create representative tasks and inspect outcomes, trajectories, tool calls, latency, cost and unsafe behaviour.
Hands-on agent patterns
Build the control loop, not just the happy path
The exact labs depend on duration and access; each pattern includes a visible engineering check.
Tool-calling assistant
Define a small tool set with structured inputs, outputs and failure responses.
- Practise
- Validate arguments, handle errors and log every attempted action.
- Control
- The model does not gain permissions beyond the calling application.
Stateful LangGraph workflow
Model steps, state, branching and stopping conditions as an inspectable graph.
- Practise
- Test loops, retries, checkpoints and invalid state transitions.
- Control
- Graph structure does not make model decisions correct.
Human approval gate
Pause before a consequential tool call and resume with an explicit decision.
- Practise
- Preserve context, approver identity, decision and resulting action.
- Control
- Approval must be meaningful, not a decorative click after execution.
Retrieval-enabled agent
Allow an agent to retrieve approved sources before drafting an answer or plan.
- Practise
- Measure retrieval quality and show source support in the final output.
- Control
- Retrieval reduces neither the need for evaluation nor access control.
Multi-agent pattern
Compare delegation or handoff with a simpler single-agent design.
- Practise
- Define responsibilities, shared state, termination and conflict handling.
- Control
- More agents can add cost and failure points without adding value.
Trace and failure review
Inspect a run across model calls, tools, branches, retries and final response.
- Practise
- Classify failures and connect each category to a test or control.
- Control
- Observability records behaviour; it does not make the workflow safe by itself.
Options
Choose the depth that matches your goal
A focused agent course assumes stronger foundations than a general AI introduction.
This page
Focused Agentic AI course
Concentrate on tool use, state, orchestration, evaluation and human control.
Best for: Developers already comfortable with Python and model APIs
- Agent architecture
- LangGraph and framework comparison
- Evaluation and guardrails
Production AI Engineering
Cover RAG, agents, MCP, APIs, evaluation and deployment as one integrated programme.
Best for: Developers building a broader AI engineering portfolio
- 40 live hours
- 40 guided project hours
- Five hosted projects
AI Engineering Course
Use the public 48-hour weekend route for a complete transition into AI application engineering.
Best for: Working developers seeking a structured long-form programme
- Live online
- Technovids LMS
- Portfolio projects
Private Agentic AI training
Adapt the agenda, stack and exercises to one organisation and engineering context.
Best for: Bangalore or distributed developer teams
- Role and stack discovery
- Approved environment
- Team proposal
Focused agent course versus full AI engineering programme
Choose based on missing capability, not whichever title sounds more advanced.
| Decision point | Agentic AI Course Bangalore | Production AI Engineering |
|---|---|---|
| Scope | Agents, tools, state, orchestration and evaluation | RAG, agents, MCP, APIs, evaluation and deployment |
| Best fit | Developers with LLM foundations needing agent depth | Developers needing an end-to-end AI engineering route |
| Duration | 8 hours, live online | 48 live hours over 12 weekends |
| Decision | Choose for a focused architecture problem | Choose for broader production capability |
Practical value
What participants should be able to demonstrate
The outcome is an inspectable agent workflow with known limits—not an autonomous-system claim.
A justified architecture
Explain why the task needs agentic choice and where deterministic logic remains.
Constrained tool use
Tools have schemas, validation, permissions, error handling and approval boundaries.
Representative evaluation
Tests include success, ambiguity, tool failure, unsafe requests and stopping behaviour.
A production-gap brief
Security, data, reliability, cost, monitoring and ownership gaps are recorded honestly.
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.
Possible course artefacts
The confirmed agenda states which artefacts fit the available hours and lab environment.
Agent decision canvas
Task, alternatives, tools, state, stopping rules, risk and human owner.
Working controlled workflow
A bounded agent or graph implemented in the approved practice environment.
Evaluation set and failure log
Representative cases, observed failures, fixes and remaining limits.
Production-readiness checklist
Security, access, reliability, monitoring, cost and release questions.
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
Framework APIs change. Verify the selected versions and lab requirements against current official documentation before delivery.
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
From the training gallery
Technovids training in Bangalore
Photos from real Technovids sessions with teams in Bangalore. They show how we teach, not a promised result for your group.
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.
How long is the Agentic AI course?
The standard Agentic AI course runs 8 hours, delivered live online. A private team version can be shortened or extended, and the written proposal confirms its final duration.
What programming background is recommended?
Working Python, APIs and basic LLM application familiarity are recommended for hands-on agent development. If those foundations are missing, AI Programming with Python or the broader AI Engineering Course is a better starting point.
Does the course cover both LangGraph and CrewAI?
The published scope can compare both. The confirmed delivery identifies the primary framework and exercises based on participant goals, available hours and current versions; it does not promise equal implementation depth in every framework.
Will I build a production-ready autonomous agent?
No course can establish production readiness from a classroom demo. You can build and evaluate a controlled workflow, then document the additional security, data, reliability, integration and operational work required.
Are public Agentic AI classes held at the HSR Layout office?
No. HSR Layout is the Technovids head office. When a public batch is scheduled, classes run live online on Zoom. The Technovids LMS gives you notes, assignments, lab guides, a batch discussion group, a certificate when you meet the requirements, and recordings uploaded within 24 hours that you can watch for three months.
Are there confirmed public batch dates and fees?
They are provided only when a cohort is scheduled. Submit an enquiry to receive the current option, timetable, prerequisites, inclusions and fee before making a booking decision.
Is private corporate training available in Bangalore?
Yes. Private training can be live online or, when agreed, onsite at the client’s workplace. The proposal confirms scope, participant assumptions, environment, duration, venue and commercials.
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.
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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.