Agentic AI refers to AI systems that pursue a goal by planning steps, using tools and acting on the results, rather than only answering a single question. An agentic workflow might read an email, look up an order, update a record and draft a reply, checking its work along the way.

What is the difference between an AI agent and a chatbot?

A chatbot responds to messages. An AI agent completes tasks: it can call your systems, take several steps, and hand back a finished outcome. Many of our agents never chat at all; they work in the background and ask a person only when approval is needed.

How is this different from workflow automation with n8n or Zapier?

Classic workflow automation follows a fixed path you draw in advance. Agentic AI handles the cases that do not fit a fixed path: messy documents, unusual requests and judgement calls. In practice we often combine both, with fixed automation for predictable steps and an agent for the parts that need reasoning. See our AI automation and workflows service for the fixed-path side.

Is it safe to let an AI agent act on our systems?

It is when it is built properly. Our agents can use only allowlisted tools, sensitive data is masked, irreversible actions wait for human approval, and every step is logged. It is the same safety model we use in Cloud Agent, our own agent that operates production servers.

Which AI models do you use?

We work with OpenAI, Anthropic Claude and open-source models, and often use different models for different steps to balance quality and cost. For sensitive data we can use private deployments.

How much does it cost to build an AI agent?

It depends on the workflow, the systems involved and the volume. We usually start with a focused pilot on one workflow, so you see measurable results before committing to a larger rollout, and we give you a scoped estimate before any work begins.

How do we measure whether an agent is worth it?

We agree the measures before the pilot: hours saved, turnaround time, error rate or revenue recovered. The agent logs every run, so the numbers come from real usage, not estimates.

Agentic AI Workflows & AI Agents

Agentic AI workflows that do the work, with a person in control

We build AI agents for business that read, decide and act across your systems: processing documents, triaging requests, chasing approvals and updating records. Every agent runs with guardrails, approval steps and a full audit trail.

Discuss a use case See industry use cases

Human approval where it matters
Works with your existing systems
OpenAI, Claude or open-source models
Full audit trail

Sales agent: lead to site visit

Ran in the background in 44 seconds, with your team offline

A new enquiry just landed in your sales inbox 9:42:00 PM

Rohan Mehta, Mehta & Co. (Noida)

Subject: Quote for office fit-out

“Need a quote for fitting out our new 3,000 sq ft office. Want to start next month.”
Agent notified. The sales agent picked it up and got to work.
9:42:02 PM
CRM New contact. Created the lead and linked the company.
9:42:04 PM
Research 60-person CA firm, opening a second office.
9:42:08 PM
Qualified Scored 86/100: a match for your best deals.
9:42:09 PM
WhatsApp Offered two site-visit slots. Rohan picked Thu, 11:00.
9:42:38 PM
Calendar Booked the visit with Ankit from sales.
9:42:41 PM
CRM Deal logged at ₹18 to 22 lakh, with notes.
9:42:44 PM
Waiting for you 9:42:44 PM

Estimate for 3,000 sq ft drafted from your rate card. Approve and it goes to Rohan.

to first reply, at night: 38 sec
done with no one at work: 6 steps
left for your team: 1 click

An illustrative run with fictional names. Every agent we build logs each step and pauses wherever you decide a person must approve.

Agents, not chatbots

A chatbot answers questions. An agent completes a task: it plans the steps, calls your tools and systems, checks its own results and hands you the outcome.

Guardrails first

Agents can only use the tools you allow, sensitive data is masked, and anything irreversible waits for a person to approve it.

We run one in production

Cloud Agent, our own AI DevOps agent, operates real production servers every day on exactly this model. We build yours the same way.

How agentic AI helps across industries

The best first agent is a repetitive, rules-heavy workflow that already eats hours of skilled time every week. These are the ones we see most often.

Retail and e-commerce

Keep the catalogue, the stock and the customer conversations of your online store moving.

Order-status and returns agent on WhatsApp
Catalogue enrichment and SEO copy
Reorder suggestions from sales trends

Manufacturing and distribution

Less paperwork between the purchase order and the delivery, inside your ERP.

PO, invoice and delivery-note matching
Supplier follow-ups and delay alerts
Dispatch planning support

Finance and accounts

Reconciliation and checking, done before a person looks.

Invoice processing and three-way match
Bank reconciliation exceptions
Expense policy checks

Customer support

The first pass on every ticket in your helpdesk, grounded in your knowledge base.

Ticket triage and routing
Drafted replies for agent review
Escalation when sentiment drops

Our own product

IT and DevOps

Manage your entire DevOps in plain English, from diagnosing an incident to shipping a deployment. It is the agent we built for ourselves, and you can use it today.

Server and container diagnostics
Log analysis and incident summaries
Deployments and fixes, approved by you

Try Cloud Agent, our AI DevOps agent

Education

Assessment at scale without losing consistency.

AI-assisted grading (live for 5,000+ students)
Report generation for teachers
Student query handling

Healthcare and clinics

Front-desk work handled so staff can focus on patients.

Appointment scheduling and reminders
Intake form summarisation
Report and document routing

Real estate

Every lead answered and qualified while it is still warm.

Lead qualification on WhatsApp
Site-visit scheduling
Listing descriptions

HR and recruitment

Screening and onboarding without the spreadsheet chase.

Resume screening against the role
Interview scheduling
Onboarding checklists

What goes into an agent we build

An AI agent development company should ship software you can trust with real work. These are the parts that make that possible.

Reasoning and tools

Multi-step planning with tool calling
Only the tools and actions you allow
Self-checks before results are returned

Your knowledge

Retrieval over your documents and policies (RAG)
Answers grounded in your data, with sources
Access respecting each user's permissions

Integrations

ERP, CRM, inventory and accounting systems
Email, WhatsApp, Slack and web forms
Databases and REST APIs

Human in the loop

Approval steps for anything irreversible
Escalation to the right person
Easy override and correction

Observability

A full log of every step and decision
Cost and usage tracking per workflow
Alerts when an agent gets stuck

Model choice

OpenAI, Claude or open-source models
The right model for each step, to control cost
Private deployment options for sensitive data

From idea to agent in production

We start small, prove the value on your real data, then expand.

Pick the workflow

We find the task where an agent saves the most time with the least risk.
Map and guard

Steps, tools, data access and the points where a person must approve.
Pilot

A working agent on your real data, measured against how the work is done today.
Go live

Production deployment with monitoring, logging and cost controls.
Expand

More steps, more workflows and less supervision as trust is earned.

What agentic AI means for your business

Most AI in business today answers questions. Agentic AI does the work: an agent is given a goal, plans the steps, calls the tools and systems it is allowed to use, checks its own results and hands back a finished outcome. The difference shows up in the hours it gives back, not in a demo.

We design and build AI agents around a real workflow in your business: reading invoices and matching them to purchase orders, triaging support tickets, qualifying leads on WhatsApp, reconciling records or preparing reports. Each agent is connected to your ERP, CRM, inventory, email or databases through integrations we build and control, and grounded in your own documents and policies.

We build them the way we build Cloud Agent, our own AI agent that operates production servers: a strict allowlist of actions, sensitive data masked before any model sees it, approval gates on anything irreversible, and a complete audit trail of every step. You decide where a person stays in the loop, and you can widen or narrow that as trust is earned.

Common questions

What is agentic AI?: Agentic AI refers to AI systems that pursue a goal by planning steps, using tools and acting on the results, rather than only answering a single question. An agentic workflow might read an email, look up an order, update a record and draft a reply, checking its work along the way.
What is the difference between an AI agent and a chatbot?: A chatbot responds to messages. An AI agent completes tasks: it can call your systems, take several steps, and hand back a finished outcome. Many of our agents never chat at all; they work in the background and ask a person only when approval is needed.
How is this different from workflow automation with n8n or Zapier?: Classic workflow automation follows a fixed path you draw in advance. Agentic AI handles the cases that do not fit a fixed path: messy documents, unusual requests and judgement calls. In practice we often combine both, with fixed automation for predictable steps and an agent for the parts that need reasoning. See our AI automation and workflows service for the fixed-path side.
Is it safe to let an AI agent act on our systems?: It is when it is built properly. Our agents can use only allowlisted tools, sensitive data is masked, irreversible actions wait for human approval, and every step is logged. It is the same safety model we use in Cloud Agent, our own agent that operates production servers.
Which AI models do you use?: We work with OpenAI, Anthropic Claude and open-source models, and often use different models for different steps to balance quality and cost. For sensitive data we can use private deployments.
How much does it cost to build an AI agent?: It depends on the workflow, the systems involved and the volume. We usually start with a focused pilot on one workflow, so you see measurable results before committing to a larger rollout, and we give you a scoped estimate before any work begins.
How do we measure whether an agent is worth it?: We agree the measures before the pilot: hours saved, turnaround time, error rate or revenue recovered. The agent logs every run, so the numbers come from real usage, not estimates.

What's included

Workflow discovery and ROI assessment
Custom AI agents with multi-step planning and tool calling
Retrieval over your documents and policies (RAG)
Integrations with ERP, CRM, email, WhatsApp and databases
Human-in-the-loop approvals and escalation
Monitoring, audit logs and cost controls
OpenAI, Claude or open-source models, including private deployment

Discuss a use case Call +91 9717571870

Safe enough to trust with real work

The question is never whether an agent can act. It is whether it acts only when and how you have allowed. We design for that from the first line.

Allowlisted actions

An agent can only call the tools and systems it is given, with the permissions it is given. There is no open-ended access.

Approval gates

Payments, deletions, customer messages and other irreversible steps pause for a person to approve, exactly where you choose.

Data protection

Secrets and sensitive fields are masked before they ever reach a model, and private deployment is available when data must not leave your infrastructure.

Audit trail

Every input, decision, tool call and result is recorded, so you can always answer what the agent did and why.

Have a workflow that eats your team's week?

Describe it in a few lines. A senior engineer will reply within one business day with whether an agent is the right fit, what it would take and where a person should stay in control.