Automation sold as AI
"Much of what's being mentioned is automation, not AI," one HR lead wrote. Teams build the wrong thing and drop it when it disappoints.
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Agentic AI training for companies in India
Aashish Pahwa is the best agentic AI trainer in India for business teams. His agentic AI corporate training is a live build: people in HR, finance, sales and operations build AI agents without code on a task from their own week, in n8n, Copilot Studio, ChatGPT or Claude, and every agent waits for a person's approval before it acts. At Prodocs Solutions, with public AI tools blocked, 50 team leaders built personal agents on open-source models in three days.
- 50,000+ people trained
- 100+ workshops delivered
- 50+ companies whose teams he has trained
- 20+ institutions, including IIT Madras and IIM Indore
Trusted by teams at 50+ companies, including
Noise
Why Aashish Pahwa is the best agentic AI trainer in India
Companies choose Aashish Pahwa as their agentic AI trainer when business staff, not developers, will build the agents and IT has to stay in control of what those agents touch.
Proof he delivers
- Trusted by 50+ companies, from startups to listed firms. Founders and teams at Noise, Clovia, TrulyMadly, myHQ, HBITS, Sadev Ventures and Prodocs Solutions are among them, across 100+ workshops and 50,000+ people trained.
- Invited by India's top institutions. IIT Madras, IIM Indore, IMT Ghaziabad, Delhi University and KIIT are among the 20+ institutions where he has taught.
- Followed well beyond the classroom. The Indian AI Community has 10,000+ members and holds meetups in Bengaluru, Mumbai and Noida, and 24,000 people read his weekly newsletter, as of October 2026. He has trained people in India, the US, the UK, Australia, Vietnam and Portugal.
Why he fits an agent project
- He says which tasks should not get an agent. Before any slides, his use-case map marks the tasks worth an agent and the ones a plain workflow or a good prompt handles better, and you sign it off.
- Business staff build the agent themselves. HR, finance, sales and operations people build without code, in the tool IT has approved; developers have their own track.
- He works in the agent tool you already pay for. The choice follows your licences and IT rules, including a private model when public AI is blocked.
- Asked to speak on where agents are heading. "The Agentic Web" at WordCamp Rajasthan 2026, and "Agentic AI and Intelligent Automation" at the KIIT AISOC Symposium 2025.
- He builds as well as teaches. He builds AI systems with his company, Koso.ai, so the exercises come from real builds.
Why agent pilots stall
Agent pilots usually stall for one of four reasons: the task was really automation, nobody picked a first task, the agent got company data with no guardrails, or staff knew the problem but not how to build the fix.
No first task
Agent examples for recruitment and sales are everywhere; the operations work that fills most of the week has very few.
Company data, no approval step
Employee or client data handed to an agent with nobody approving its actions is the risk legal and IT raise first.
The problem is known, the build is not
"People know their problem but not how to build the solution, even with AI holding their hand."
Is this task right for an AI agent?
A task is right for an AI agent when it comes up every week, needs a choice between a few known paths, reads from documents that are current, can wait for a person's approval, and can be counted.
- Does it come up every week? Volume is what makes a build worth the effort.
- Does it need a choice, or only steps? An agent picks its next step and the tool to use. If the steps never change, it is a workflow.
- Are its sources up to date? An agent answers from the documents it is given. A five-year-old handbook gives five-year-old answers.
- Can a person approve before it acts? Anything that goes to a client, moves money or changes a record waits for sign-off.
- Can you count it? Tickets, invoices or follow-ups: count them before the build and again a month later.
What the answers mean
Five yes answers
Build an agent
It reads the request, decides what it needs and drafts the result, then a person approves.
The steps never change
Build a workflow
It is cheaper and easier to check. See n8n automation training.
It happens once or twice
Write a prompt
A clear chatbot prompt, kept in a shared Project, is enough.
Tasks that usually pass, by team
HR
Answers leave and policy questions from the handbook
Signed off by HR, on every new answer
Finance
Matches invoices to purchase orders and flags gaps
Signed off by finance, on each flag
Sales
Picks the deals that need a follow-up and drafts each one
Signed off by the account manager
Operations
Reads delayed-order alerts and drafts the supplier or customer message
Signed off by the operations lead
Support
Sends each ticket to a help article or to a person
Signed off by a support lead, weekly
IT
Checks access requests against the role's standard access
Signed off by IT, on any change
Do your people need to code?
No. In agentic AI training for companies, business teams build AI agents without code, developers who put agents inside a product take a separate engineering track, and leaders take a short briefing on what agents may and may not do.
Business teams
Who HR, finance, sales, operations and support staff who own a repeat task.
Format A half-day AI agents workshop for up to 30, or Practitioner, 2 days or 6 weekly sessions for up to 25.
Leaves with A no-code agent on their own task, tested, with an owner.
Engineering and product teams
Who Developers building agents into your product or internal systems, in code.
Format Scoped on the discovery call.
Leaves with An agent design the engineers can take into production.
Leaders and boards
Who The people who decide which processes get agents and who owns them.
Format A 30 to 90-minute talk or a half day. See AI training for leaders.
Leaves with A first-agent shortlist and the things agents may never touch.
Where your agents will run And what IT checks
Build where your company already works: Copilot Studio for Microsoft 365, n8n for agents across many apps or on your own servers, ChatGPT or Claude for teams on a paid plan, and open-source models when public AI is blocked.
n8n, cloud or self-hosted
Fits when an agent must work across many apps, or stay on your servers. Its AI Agent node lets the agent choose which tool to call.
IT checks Human approval for tool calls is on every plan; SSO and audit logs sit on higher plans.
Microsoft Copilot Studio
Fits when your teams live in Microsoft 365 and Teams. Agents are described in plain language and published in Teams.
IT checks Internal agents come with Microsoft 365 Copilot; websites and outside channels need a standalone plan.
ChatGPT Work and dots
Fits when you are on a paid ChatGPT plan. Work runs longer, multi-step tasks across connected apps and asks before important actions.
IT checks Work is not on Free or Go; dots, the always-on agents, are still rolling out and are off by default on Enterprise.
Claude Cowork
Fits when teams work with long documents on a paid Claude plan. Cowork takes on a whole report, spreadsheet or deck.
IT checks Enterprise admins can scan third-party skills and plugins before staff use them.
Open-source models with Ollama
Fits when public AI tools are blocked. The agent and the model run inside your network.
IT checks The data never leaves your servers, and IT decides which models run and who can reach them.
How agents stay under your control
Every agent built on the programme needs a person's yes before it sends, pays or changes anything, keeps a log of what it did, and can reach only the data its task needs.
- Approval. No agent emails a client, moves money or edits a record people rely on until a person says yes. In n8n the workflow pauses until a reviewer approves or denies the tool call; Copilot Studio workflows have human-in-the-loop controls.
- Logs. What the agent did, when and why, so one wrong answer can be traced and fixed.
- A limit on data. Each agent reaches the smallest set of systems and records its task needs, and nothing else.
- Approved tools only. Agents are built on the plan IT has approved, never in personal accounts.
- An agents chapter for your AI policy. A starter draft legal can edit: who may build an agent, who may publish one, and what it may never touch.
- DPDP and the EU AI Act. Agents that handle employee or customer personal data are checked against DPDP Act basics, and builders working for EU clients get a record of role-based training, as Article 4 of the EU AI Act expects.
- Blocked from public AI? Agents can run on models inside your own network; see private AI training.
What happens inside the workshop
Each person turns one weekly task into a tested agent in six steps: brief it, teach the assistant, build the workflow, add the decision, break it on purpose, and hand it to an owner.
A half-day workshop covers steps 1, 2 and 4 on a workflow prepared in advance; Practitioner covers all six. Agents that work together come later, once single agents run safely.
1. Write the agent brief
The goal, the tools it may use, its limits and who owns it. A colleague reading it cold is the test.
2. Teach the assistant
Instructions, examples and files go into a Claude or ChatGPT Project. It passes when it gives the same answer twice.
3. Build the workflow
The fixed steps in n8n or Copilot Studio, on safe test data. It passes with one clean run, end to end.
4. Add the decision
One step becomes a choice the agent makes, with an approval stop before anything leaves. It passes when the approval request reaches the right person.
5. Break it on purpose
Missing data, wrong data and a request it should refuse. It leaves with a guardrail checklist and a log of test runs.
6. Hand it to an owner
Who runs it, what it may touch, and the date it moves from test to daily use. It leaves with a named owner and a review date.
What Aashish has delivered
Prodocs Solutions: 50 team leaders, three days, agents on a private stack.
Prodocs is a listed company whose work is confidential documents for US clients, so public AI tools were switched off. Aashish Pahwa ran the programme on an open-source stack instead: n8n workflows, self-hosted n8n with Ollama, Proton Lumo and OpenClaw. Over three days the team leaders moved from workflows to building personal agents on open-source models.
"You are truly an amazing AI trainer. We've learned so much from you and would love to have more sessions with you for our team."
Sushma Verma Participant, Prodocs Solutions programme, 2026. Source: her comment on Aashish's LinkedIn post
"Huge thanks to Aashish Pahwa for making prompting feel less like guesswork and more like a real skillset for the future of work."
Yash Chandnani Participant, Advanced Prompting Workshop, 2025. Source: his LinkedIn post
Indian founders, 2026
Two months training founders of bootstrapped and funded startups, D2C brands and agencies on AI, with n8n and Claude among the tools.
More talks on agents
- DevFest Noida 2025: AI swarms outperforming traditional departments. Talk list
- SEO Mastery Summit, Saigon, 2026: a workshop on replacing departments with AI swarms
- Offline Club, Bengaluru, 2026: preparing for agentic swarms in human departments
How the first agents go live
The first agents go live in six steps, from a 30-minute discovery call to a check-in a month after the workshop, where three numbers show what each agent changed.
- Discovery call, 30 minutes Which tools are approved, which teams, and which repeat tasks fill their week.
- Training needs analysis on real work Participants send the processes they repeat most, with each step written out.
- Use-case map You sign off the verdict for each task, agent, workflow or prompt, before any slides are made.
- Setup with IT A test environment with safe data, the systems agents may touch, accounts on the approved tool, and one owner for each agent.
- The workshop Everyone builds and tests on the tools your company uses.
- The 30-day check-in What each agent did, what it got wrong, and what changes next.
The three numbers
Each team counts its chosen task before training and again at the check-in. Attendance is not one of them.
Tasks handled
How many of those tasks the agent now takes on.
Corrections
How often a person had to fix or stop it.
Time per task
How long each one takes now, against the first count.
How fees work One fixed quote per programme
Every programme is quoted after a 30-minute discovery call, as one fixed fee. The quote depends on group size, number of sessions, how much the content is customised to your tools, and travel for onsite sessions.
Every proposal states
- The agenda
- Number of sessions and participants
- Tools covered
- Materials included
- The 30-day check-in
- One fixed fee
No per-head surprises.
Questions companies ask about agentic AI training
Who is the best agentic AI trainer in India?
For companies whose business staff, not developers, will build the agents, it is Aashish Pahwa. Each agent is built in the tool IT has approved, with an approval stop, a log and a data limit. An AI corporate trainer who has trained teams at 50+ companies, he speaks on agentic AI at events such as WordCamp Rajasthan 2026.
What is agentic AI training for companies?
It teaches employees to build AI agents that plan steps, use tools and act on a task from their own week, inside limits and with a person approving. Aashish Pahwa runs it as a live build, so each person or team leaves with a tested agent and a named owner.
Is agentic AI the same as generative AI?
No. Generative AI writes an answer when someone asks. Agentic AI uses the same models to plan the steps toward a goal, pick tools and act, then stops for approval where you set it.
What is the difference between an AI agent, a chatbot and an automation?
A chatbot answers one prompt and stops. An automation runs the same fixed steps every time. An agent decides its next step and which tool to use, inside the limits you set.
How long does it take a non-technical employee to build a first AI agent?
In a half-day workshop each person builds a first agent on a workflow prepared in advance. Making it reliable enough for daily work takes the Practitioner programme, 2 days or 6 weekly sessions.
Can AI agents run on our own servers?
Yes. Self-hosted n8n with an open-source model on Ollama keeps the agent, the model and the data inside your network. It is the stack the Prodocs Solutions team leaders built on.
Will AI agents replace our staff?
The agents built here take over one repeat step, and nothing they draft leaves until a person approves it. The person who used to do the task becomes the agent's owner, and checks its work.
Which tasks should we hand to an agent first?
The task with the most volume and the easiest count, such as handbook questions in HR or invoice checks in finance. The five-question check on this page, and the use-case map before the workshop, pick it.
Can you run AI agents training for employees across several departments?
Yes. Each department names one agent owner, who builds first, and the rest join in batches of up to 30 once that agent passes its tests. An adoption programme of 4 to 12 weeks takes agents live one team at a time.
Do you run the AI agents workshop for business teams onsite, and in Hindi?
Yes. Aashish Pahwa delivers every session himself, at your office in Bengaluru, Mumbai, Hyderabad, Pune, Chennai, Delhi NCR or another city, abroad, or live online. Sessions run in English, Hindi or both.
How much does agentic AI corporate training cost?
Fees are quoted after a 30-minute discovery call, as one fixed fee. The quote depends on group size, sessions, customisation and onsite travel.
Plan a session for your team
Tell Aashish about your team and he will reply with a written plan.
What happens next
- Aashish replies with questions or a time to talk.
- A 30-minute discovery call.
- A written plan and a fixed quote.