AI practice · Gurugram
AI agent development for workflows that need action — not just answers
We design agents that read context, call the tools you allow, and stop for a human when the next step is sensitive. Useful automation with an audit trail — not a black box that emails your customers unsupervised.
An AI agent is the right shape when the job is a sequence: look something up, draft a reply, update a record, open a ticket, or prepare a quote — with rules about when a person must approve. Chat alone is not enough if the work still happens in CRMs, ERPs, or admin panels.
We start with the workflow, the systems of record, and the failure modes. Then we wire tools with least privilege, logging, and a clear handoff. The same studio can build the APIs and screens around the agent so operations can trust what ran overnight.
Who this is for
Ops and revenue teams drowning in repetitive multi-step work; product companies that want in-product automation with guardrails; and buyers who need agents that call their APIs — not a generic autopilot that cannot be audited.
Problems we solve
The same workflow is copy-pasted every day
Agents execute defined steps across tools you already use, with status you can inspect.
Chatbots answer but nothing gets done
We add tool calls and write-backs where you allow them — draft first, then act with approval when needed.
Automation scripts break when the process changes
We keep the process explicit: prompts, tools, and policies you can revise without rewriting the whole product.
Fear of unsupervised outbound actions
Sensitive steps require confirmation. Everything that ran can be logged for review.
Suitable first workflows
Ticket triage and draft replies
Classify, summarise, and draft — with a human sending the customer-facing message until trust is earned.
CRM hygiene and follow-ups
Fill missing fields, propose next steps, and queue tasks; write-backs only on fields and stages you approve.
Internal ops checklists
Gather data from APIs, prepare a pack for finance or ops, and stop when a signature or payment step is required.
Scoping an initial implementation
We pick one workflow with a clear success measure (time saved, fewer handoffs, fewer missed steps). We list systems of record, allowed tools, and which steps need approval. That keeps the first agent shippable instead of a vague “automate everything” programme.
Monitoring covers run logs, failure rates, and human override volume. Evaluation uses real cases from your team — not only demo scripts — so you know when to widen permissions.
What you get
A scoped agent for one high-value workflow, with tools, permissions, and monitoring defined before scale.
- Workflow mapping and success criteria
- Tool and API integration plan
- Permission and approval rules
- Agent orchestration and retries
- Human-in-the-loop checkpoints
- Audit logging and alerts
- Admin controls for operators
- Handoff docs for your team
Security, testing, and maintenance
Agents only receive the credentials and scopes you approve. We prefer short-lived tokens, environment secrets outside the model context, and no silent writes to production without an explicit policy. NDAs and data-handling terms are agreed before integration work starts.
We test happy paths and failure paths: missing data, API errors, and steps that must refuse. Product QA covers the UI for approvals and the records left behind. We do not claim zero-error automation — we design for recovery.
Questions about ai agents
How is an AI agent different from a chatbot?
A chatbot mainly answers. An agent can take steps in tools you connect — create records, draft messages, trigger jobs — under rules you define.
Will the agent email customers on its own?
Only if you explicitly allow that path. Default designs draft for review or act only inside your internal systems until you expand permissions.
Which systems can you connect?
CRMs, ticketing, databases, and internal APIs are common. We connect what you can authenticate and support — we will not invent integrations you do not have.
Can you start with one workflow?
Yes. That is the preferred start. Prove value on one process, then add the next.
Related development services
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Knowledge answers
RAG chatbot development
When the first job is grounded answers from your documents.
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New products
SaaS MVP development
Ship the product the agent lives inside.
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APIs and admin
Web development
Backends and admin UIs agents and humans both use.
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Broader AI
AI development
Chatbots, NLP, and ML when the job is wider than one agent.