AI Agent Development for tasks that need judgement and tools
Agents that read, decide and act inside your systems, with logs and human approval before anything risky happens.
An AI agent is software that uses a language model to choose its next step, then calls tools to carry it out: search a database, read an email, create a ticket, send a quote. It differs from a chatbot because it does work, not just talk. That also means mistakes have consequences.
We build agents for narrow jobs where rules alone are too rigid and a human is wasting time. Examples include sorting inbound enquiries, drafting replies from your records, reconciling documents, or preparing a first-pass quote. Every agent we ship has limits, logs and an approval step for anything that cannot be undone.
Why agent projects fail and how we avoid it
Too broad a job
'An agent to run sales' never works. Agents do well on one clear task with a measurable result, such as qualifying a lead or matching an invoice to a purchase order.
Unchecked actions
An agent that can send emails or edit records without review will eventually send the wrong one. Actions need permission levels, previews and an undo path where possible.
No way to see what happened
When an agent gets something wrong, you need to know which tool it called and why. Without traces, you cannot fix it or trust it.
Costs and loops
Agents can call a model many times for one task, or get stuck repeating a step. Step limits and budget caps are needed from the start.
What we build in AI agent development
Task agents on your tools
An agent connected to your CRM, helpdesk, spreadsheets, email or internal APIs, with only the permissions its job needs. Read access comes first, write access is added step by step.
Human-in-the-loop approvals
Risky steps pause for a person to approve on WhatsApp, email or a simple web screen. The reviewer sees what the agent plans to do and why, then clicks accept or edit.
Document and email processing
Agents that read invoices, enquiries or forms, pull out the key fields, check them against your records and flag mismatches. Clean items pass through, doubtful ones are queued for review.
Tracing and audit logs
Each run records inputs, tool calls, outputs and cost. You can replay a failed run, see where it went wrong, and prove what was done.
Evaluation before launch
We build a test set from your real cases and score the agent on it. A change in model or prompt is checked against the same set so quality does not slip silently.
Cost and safety limits
Caps on steps, spend per run and daily spend, allow-lists of tools and domains, and protection against instructions hidden inside emails or documents the agent reads.
How an agent project runs
Discover
We shadow the current task, collect twenty to fifty real examples including awkward ones, and measure how long it takes a person today.
Define
We set the agent's scope, tools, permissions, approval points and success measure, for example 'correct field extraction on ninety out of a hundred invoices'.
Design
We design the review screen and notifications first, since that is where your staff will spend time with the agent.
Build
We build in read-only mode, compare its suggestions to what staff actually did, then enable actions gradually with approvals.
Ship & extend
We go live on a slice of real work, watch the logs, and tighten. Support covers one month, and further tasks reuse the same tools and logging.
What it costs
Agent scope varies a lot. The nearest anchor is ₹1,00,000 for AI chatbot and automation work, and a single-task agent with approvals often sits close to it. We give a fixed written quote after discovery.
- Number of tools and systems connected
- Approval and review screens
- Volume and variety of input documents
- Evaluation set and testing effort
- Monthly model usage cost
All prices exclude 18% GST. See also website costs and app costs.
Questions we get asked
What is the difference between an AI agent and a chatbot?
A chatbot answers questions. An agent also takes actions through tools, such as updating a record or drafting an email, and decides the order of steps itself. Because it acts, it needs permissions, logging and approval limits.
Can an agent run without any human checking?
For low-risk, reversible steps, yes. For anything that sends money, contacts customers or changes important records, we recommend approval until the agent has a proven record on your data. You can relax this gradually. Approval rules can be relaxed one task type at a time, as the agent proves reliable on your own cases.
How do you test an agent?
We collect real cases with the correct outcome, run the agent on them and score the results. We repeat after every change. Accuracy numbers are shared with you before go-live, along with the types of cases it still gets wrong.
Which model do you use?
We choose per task, usually among Claude, GPT and Gemini models, based on accuracy, speed and cost on your test cases. The design keeps the model replaceable, so you are not locked in to one provider.
Is it safe to connect an agent to our email?
It can be, with limits. We use least-privilege access, treat email content as untrusted so hidden instructions are not obeyed, and require approval before sending. We explain these controls in the written scope. A written list of permissions and an audit log show exactly what the agent can touch.
What happens if the model provider changes pricing or behaviour?
Because we keep an evaluation set, we can re-run it on a new model version and compare results before switching. Costs are capped per run, so a price change does not turn into a surprise bill.
Ready to scope it?
Tell us what you want to build. You get a fixed written quote after a free discovery call.