AI · Agentic systems
Banao designs, builds, and operates agentic AI — systems where a model plans, calls your tools, and acts across a workflow — wired to the software you already run, with guardrails and a human checkpoint on every consequential action.
We ship agents that survive contact with production: scoped to one job, traced end to end, and able to hand back to a person the moment they reach the edge of what they can do safely. It is the same discipline we run inside Banao before any of it reaches you.
Banao— Vikaas runs our own demand generation as an agentic pipeline, in production every day.
An agent in production is not one model call. It is a planning loop, a tool layer, grounding, guardrails, and the observability to know what it did — we own all of it.
Single-purpose agents that plan, call tools, and complete a real task — scoped tightly to one job so behaviour stays predictable and testable.
Supervisor and worker agents that divide a workflow, pass state cleanly, and escalate to a human when a step exceeds their remit.
Function calling wired to your CRM, ERP, ticketing, and databases — so the agent acts on real systems instead of describing what it would do.
RAG over your documents and data so answers are sourced from your facts, with citations, not the model's training-time guesswork.
Input and output checks, allow-lists for actions, and hard limits on what an agent may touch — defined with you, enforced in code.
Approval gates on consequential actions, with the reasoning shown so a person reviews the decision rather than rubber-stamping a score.
Task-level eval suites that score the agent on your real cases before and after every change, so a prompt tweak can't silently regress behaviour.
Full traces of every plan, tool call, and output, so when an agent does something odd you can see exactly why and fix the cause.
Working and long-term memory designed for the task — enough context to be useful, scoped so it does not leak data across users or sessions.
The right model for each step, with routing and caching, so quality stays high without a token bill that quietly outgrows the value.
The word "agent" is doing a lot of work in vendor decks. We use it precisely: an agentic system is one where a model decides the next step, calls a tool to act, observes the result, and loops — within limits you set — until the task is done or it hands back to a person.
That is a real capability, and it is also the wrong answer for a large share of the problems it gets pitched at. The first job of a serious build is to separate the cases that need an agent from the cases a plain script or a single grounded prompt would handle more cheaply and more reliably.
A chatbot returns text. An agent takes actions in your systems — books, updates, files, routes — which is exactly why guardrails and a human gate matter more, not less.
If the steps never change, that is automation, and code is more reliable than a model deciding the obvious. We will say so rather than sell a model.
We ship most agents at low autonomy first — suggest, then act-with-approval, then act — and only widen the dial once the evals and the team trust the behaviour.
A demo agent has to work once, on a friendly question, in front of an audience. A production agent has to work on the messy 5% of inputs that never appear in a demo, at 3 a.m., without a human watching every call. Almost all of the engineering goes into that gap.
We build the loop, the tool layer, and the grounding first, then wrap them in the parts that make it safe to deploy: guardrails, evals, tracing, and a clean hand-off to a person. The model is the easy part; the system around it is the work.
One agent, one job, a clear definition of done. Narrow scope is what makes behaviour testable and failures legible — broad "do-anything" agents are where projects drown.
Actions and answers are tied to your data and tools. Where the agent lacks a fact or a permission, it says so and stops, rather than improvising.
Nothing reaches production without passing a task-level eval suite built from your real cases. Every change re-runs it, so quality is measured, not hoped for.
Hard limits, approval gates, and a clean hand-back to a person on the cases the agent can't handle. The failure mode is "ask a human", never "act anyway".
We have been called in to rescue enough stalled agent projects to see the same failures repeat. None of them are about the model being too weak. They are about scope, grounding, evaluation, and trust — the engineering discipline, not the intelligence.
We would rather tell you these on the first call than bill you to discover them on the third. If your last agent pilot died, it almost certainly died of one of the following.
An agent asked to do everything does nothing reliably. The demo that handled one happy path collapses the moment it meets the real range of inputs.
Without task-level evals there is no way to know if a change helped or hurt. Teams tune prompts by vibe, regress silently, and lose confidence in the system.
An agent allowed to act on its own guesses will eventually act wrongly on a real system. Without grounding and guardrails, one bad call ends the pilot.
If the people who own the workflow can't see why the agent did what it did, they won't let it act. Tracing and a human gate are how adoption actually happens.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified. The deployments are live.
Banao — Vikaas
Vikaas plans, drafts, and sequences Banao's own demand generation as an agentic workflow, with a human approving what goes out. We run our revenue engine on it before we offer the pattern to a client.
B2B SaaS platform (anonymized)
An agent reads each incoming ticket, grounds itself in the product docs and account history, routes to the right queue, and drafts a reply for an agent to approve. Every consequential send stays behind a human gate.
Banao operates a ~300-person engineering company on its own agentic AI before any client sees it. InterviewGod screens our own hires; Vikaas runs our own demand generation. Both are agents acting on real systems, every working day, with our own team in the loop.
That is the difference between a vendor who has read about agents and one who depends on them to run a business. When an agent has to survive our own operation, the version that reaches your workflow is already hardened.
We deliver from offices in India, the UAE, the UK, and the US, and we build to the data-residency and governance rules each market expects.
From Dubai we serve enterprise digitization across the free zones and the wider GCC, including long-standing work with RAK Ceramics. Agents are built to keep data inside UAE boundaries where the PDPL and client policy require it.
Vision 2030 programmes are moving from pilots to operated systems. We build Arabic-capable agents and keep data in-Kingdom to meet PDPL and SDAIA expectations for regulated workloads.
For California and New York enterprises we build to SOC 2 controls, with the evaluation, audit-logging, and governance that US procurement and risk teams now ask of any agent that can act.
Our Cambridge UK presence supports fintech and public-sector work under UK GDPR and ICO guidance, where explainability and a clear human-accountability trail are non-negotiable.
Bangalore and Chandigarh hold our delivery bench, so a build starts in weeks. We design to the DPDP Act and run cost-efficient delivery close to the engineering that ships it.
Most vendors will sell you an agent regardless. We would rather tell you when not to build one — it is why technical teams take our second call.
You have likely been pitched agents by several vendors already. We start by proving which of your workflows an agent should touch, not by quoting a build.
We map your candidate workflows, test feasibility on the hardest one, and hand back a scoped agent design, an eval plan, and ROI maths — yours to keep either way. If you proceed, the Sprint cost is credited against the build.
We build the agent loop, tool integrations, grounding, guardrails, and the eval suite together — integration and evaluation are deliverables, not afterthoughts.
We deploy behind approval gates with full tracing and monitoring, widen autonomy only as the evals and your team allow, and keep improving the agent on live cases.
What is agentic AI, in plain terms?
An agentic system is one where a model decides the next step, calls a tool to act, checks the result, and repeats within limits you set — until the task is done or it hands back to a person. It acts in your systems rather than only answering questions.
How is an AI agent different from a chatbot?
A chatbot returns text. An agent takes actions — updating a record, routing a ticket, booking a slot — through tools wired to your software. Because it acts, it needs guardrails, evaluation, and a human gate on consequential steps that a chatbot does not.
Which models and frameworks do you build on?
We are model-agnostic and choose per task, defaulting to the most capable Claude models for planning and reasoning. We route simpler steps to cheaper models and build the orchestration ourselves rather than locking you into one framework.
How do you stop an agent from doing something harmful?
Three layers: hard limits and allow-lists on what it may touch, approval gates on consequential actions with the reasoning shown, and a clean hand-back to a person on anything outside its scope. The default failure mode is to ask a human, never to act anyway.
Do we need clean data or our own model to start?
No. We ground agents in the data and tools you already have and handle the data engineering as part of the build. You do not need a custom model — for most workflows a strong general model with good grounding outperforms a bespoke one.
How do you know the agent actually works?
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 safely instead of tuning by guesswork.
How long until an agent is in production?
A common path is a 2-week Discovery Sprint, a 6–10 week build, and a staged rollout that starts with approval gates. Banao's ~300-engineer bench means delivery begins in weeks, not the months a fresh hire would take.
How do we prove ROI before committing budget?
That is what the AI Discovery Sprint produces — fixed price, two weeks, a scoped design and ROI model you keep whether or not you continue. Worst case you have a free assessment; best case you have your board business case.
Can the agent run in our own cloud or region?
Yes. We deploy to your cloud and keep data inside the region your policy or regulation requires — UAE, Saudi Arabia, UK, US, or India — and build the audit logging your risk team needs to sign off.
Will it integrate with the systems we already run?
That is the point of an agent. We wire it to your CRM, ERP, ticketing, and databases through their APIs, including older systems via retrofit. Integration is part of the build deliverable, not a separate project.
Bring the workflow that eats the most hours or the most errors. In 45 minutes we'll tell you whether an agent is the right tool — and what it would take to put one in production.
Book a 45-min scoping call
