Agentic AI · AI agent development
Your prototype agent works once, then falls over on the second real request
Banao develops production AI agents one job at a time: an agent that plans, calls your tools, and finishes a real task — grounded in your data, scoped tightly, and evaluated against your hardest cases before it ever acts on a live system.
We build the loop, the integrations, the guardrails, and the eval suite as one deliverable, then ship it behind a human gate and widen autonomy only as the numbers earn it.
Banao— InterviewGod is an agent we developed and run on our own hiring, every week.
What developing a Banao agent includes
A developed agent is more than a prompt. It is the loop, the tools, the grounding, the guardrails, and the tests that prove it works — we build all of them.
Task scoping and agent design
We pin the agent to one job with a clear definition of done, the tools it may call, and the limits it must not cross — before a line of the loop is written.
The planning and reasoning loop
The decide-act-observe loop that lets the agent break a task into steps, call a tool, read the result, and continue — bounded so it can't spin forever.
Tool and function-call integration
We wire the agent to your CRM, ERP, ticketing, and databases through their APIs, so it acts on real systems instead of describing what it would do.
Grounding and retrieval
Retrieval over your documents and data so the agent works from your facts, with citations, and stops when it lacks the information to act safely.
Guardrails and approval gates
Allow-lists, output checks, and a human sign-off on consequential actions — defined with you and enforced in code, not left to the model's discretion.
Evaluation and regression testing
A task-level eval suite built from your real cases, run before launch and on every change, so a prompt edit can't silently break behaviour.
Tracing and staged rollout
Full traces of every plan and tool call, and a rollout that starts at suggest-only, moves to act-with-approval, and widens to act as the evals allow.
What separates a prototype agent from a production one
A prototype agent has to impress once, on a question someone chose because it works. A production agent has to be right on the inputs nobody anticipated, act on systems that bite back, and do it without a human watching every call. Closing that gap is the whole job of development.
We develop for the gap, not the demo. The loop and the model are the quick part; the work is the grounding, the guardrails, the evals, and the trace that lets your team see what the agent did and why.
Built for the messy 5%
We develop against the awkward, ambiguous, and adversarial inputs a demo never shows — because those are the ones that decide whether the agent survives a week in production.
Measured, not vibe-tuned
Every change re-runs the eval suite. We can tell you whether a tweak helped or hurt, instead of shipping on a hunch and discovering the regression in production.
Yours to run and maintain
We hand over readable code, the eval harness, and the traces — so your team can extend the agent without re-hiring us for every change.
The first agent we trust is our own
InterviewGod is an agent Banao developed and runs on its own hiring: it reads applications, grounds itself in the role, and ranks candidates with the reasoning attached, before a recruiter opens the pile. We depend on it across a ~300-person engineering operation.
Developing an agent we stake our own hiring on is a different standard from shipping one and walking away. The discipline that keeps InterviewGod honest is the discipline we bring to yours.
- InterviewGodAn agent we built that screens Banao's own applicants every week.
- VikaasAn agent we built that runs Banao's own demand generation.
Where we develop agents
India
Bangalore and Chandigarh hold the delivery bench, so development starts in weeks and runs close to the engineers who ship it, under the DPDP Act.
UAE
From Dubai we develop for GCC enterprises and keep agent data inside UAE boundaries where the PDPL and client policy require it.
US & UK
For US and UK clients we develop to SOC 2 and UK GDPR expectations, with the audit logging and evals their risk teams ask of any agent that can act.
When you don't need a custom agent
Developing an agent is the right call less often than the market implies. We will tell you before you commit a budget to one:
- An off-the-shelf tool already does it: if a product covers your workflow, configuring it beats developing an agent from scratch.
- The workflow is fixed: if the steps never change, a script is cheaper to build and more reliable than an agent deciding the obvious.
- Too little volume to evaluate: if the task is rare, you can't build a meaningful eval set, and an unevaluated agent is one you can't trust to act.
How we start — prove the agent before we build it
We don't quote an agent build off a brief. We test the hardest part of your workflow first.
- AI Discovery Sprint2 weeks · fixed price
We scope the agent, test feasibility on your hardest case, and hand back an agent design, an eval plan, and ROI maths — yours to keep. If you proceed, the Sprint is credited against the build.
- Build
We develop the loop, tool integrations, grounding, guardrails, and the eval suite together — integration and evaluation are deliverables, not afterthoughts.
- Production & continuous learning
We ship behind approval gates with full tracing, widen autonomy as the evals allow, and keep improving the agent on live cases.
Frequently asked questions
What does "AI agent development" actually include?
The full system: task scoping, the planning loop, tool and API integration, grounding in your data, guardrails, an evaluation suite, tracing, and a staged rollout. The model is one part; the engineering around it is what makes the agent usable.
Can you build on our existing stack and tools?
Yes. We integrate the agent with the CRM, ERP, ticketing, and databases you already run, through their APIs — including older systems via retrofit. We do not require you to replace working software to add an agent.
How do you keep a developed agent from going off the rails?
Allow-lists on what it can touch, output checks, approval gates on consequential actions, and a hand-back to a person on anything outside its scope. The default failure mode is to ask a human, and every action is traced so you can audit it.
How do you test an agent before it goes live?
We build a task-level evaluation suite from your real cases and score the agent against it before launch and after every change. That eval gate is what lets us widen autonomy without guessing whether the latest change helped or hurt.
How long does it take to develop an agent?
A common path is a 2-week Discovery Sprint, then a 6–10 week build, then a staged rollout starting with approval gates. Banao's ~300-engineer bench means development begins in weeks, not the months a fresh hire would take.
Do we get the code, and can our team maintain it?
Yes. We hand over readable code, the eval harness, and the traces, and we document the design so your engineers can extend the agent. You are not locked into us for every future change.
What does it cost to develop an agent?
The Discovery Sprint is a fixed price and produces the scoped design and ROI maths you need to size the build. Build cost depends on the number of tools, the grounding required, and the eval coverage — all of which the Sprint pins down before you commit.
Put your hardest workflow in front of us
Bring the task you wish an agent could own. In 45 minutes we'll tell you whether it's worth developing one — and what a production-grade build would take.
Book a 45-min scoping call