AI agents
AI agents that do the work, not just the chatting
Custom agents that carry out multi-step work against your real systems — qualifying leads, answering support, chasing follow-ups — with approval gates and a human they hand off to.
We will tell you honestly if a fixed automation would do the same job for less.
Quick answer
When is an AI agent the right choice, and what stops it doing something it shouldn't?
Mana Studio builds custom AI agents that carry out multi-step work against real business systems — qualifying leads, answering support, chasing follow-ups. An agent differs from a chatbot in that it acts rather than answers: it can look something up, take a step, read the result and decide what to do next. It is the wrong choice whenever the path is fixed — if a process is always step one, then two, then three, an automation with an AI step inside it is cheaper, more predictable and easier to debug, and Mana Studio turns down agent projects on those grounds regularly. Control comes from four things: scoped tool access, so an agent can only touch systems it has explicitly been granted; approval gates on anything irreversible or customer-facing; confidence thresholds that trigger a handover to a person; and a complete log of every action for auditing. Quality is measured before launch against the client's own historical cases — an evaluation set built from work already done, reporting how often the agent reaches the same outcome. Projects are quoted to scope; the code, prompts, configuration, keys and accounts are all in the client's name. Mana Studio is based in Warangal, Telangana, India, works with clients in India, the United States, the United Kingdom and the UAE in English, Telugu and Hindi, and replies to enquiries within one business day.
Last reviewed
- Pricing
- Fixed price against a written scope, after a call
- Right when
- The next step genuinely varies — otherwise use automation
- Controls
- Scoped tool access, approval gates, confidence handover, full audit log
- Measured on
- Your own historical cases, reported before launch
- You own
- Code, servers, domain and every key, in your name
- Based in
- Warangal, Telangana, India
An agent is worth building when the next step genuinely depends on what the last step found. A lead that needs a different question depending on the answer. A support request whose resolution depends on what the order history shows. A follow-up that should escalate, pause or close depending on the reply.
When the path is fixed, an agent is the expensive way to build an automation, and we will say so. Most of the enquiries we get about agents are better served by an automation with an AI step in it — cheaper, more predictable, easier to debug. The call at the start exists to work out which one you actually need.
Where an agent is right, the engineering that matters is the boundary: which systems it can read, which it can write to, what it must never do without a human, and what happens when it is unsure. We design that first, because an agent with tools and no boundary is not a product, it is an incident waiting to happen.
Agents businesses actually run
Lead qualification agents
Ask the follow-up questions a salesperson would, score against your real criteria, write the result to the CRM, and book the call — or mark it not worth one, with the reasoning attached.
Customer support agents
Answer from your knowledge base, look up the actual order or account, resolve what can be resolved, and hand the rest to a person with the context already gathered.
Sales follow-up agents
Work a quote through its follow-up sequence, read the replies, adjust, and escalate to a human at the point where a human adds something.
Internal operations agents
Multi-step back-office work — reconciling records across systems, preparing a document set, chasing a missing approval — with an audit trail of every action taken.
Voice agents
Phone agents that answer, qualify and book, already published as packaged products. Useful where calls are being missed, not where they are being handled well.
Guardrails and handover
Scoped tool access, approval gates on anything irreversible, full logging of what the agent did, and a clean handover to a person the moment it is out of its depth.
How agents are built
Mainstream, well-supported technology — chosen so your next developer can pick the project up, not so we are the only ones who can maintain it.
Reasoning
- Commercial LLM APIs
- Tool / function calling
- Retrieval over your data
Tools it uses
- Your CRM
- Your database
- Email & WhatsApp Business API
- Internal REST APIs
Control
- Scoped permissions
- Approval gates
- Confidence thresholds
- Full action logging
Application
- Node.js
- Next.js
- MongoDB
- Webhooks
How we work
How an agent project runs
Decide whether it should be an agent at all
We map the decision points. If the path is actually fixed, you get an automation instead, at a fraction of the cost — that recommendation is free.
Define the boundary
Which systems it can read, which it can write to, what always needs human sign-off, and what it must never attempt. Written down before anything is built.
Build and evaluate on real cases
Tested against your own historical cases so there is a measured success rate, not an impression from a demo.
Launch behind a human
Shadow mode first where it makes sense — the agent proposes, a person approves — then progressively more autonomy as the log shows it earning it.
What it costs
Agent packages are published in USD for the US market; India projects are quoted to scope after the first call, because agent scope varies far more than a website's does.
Systems we have built and run
An agent is only as good as the systems it can act on. This is that engineering.
Algo Trading
AlgoEdge
Automated PNL reporting platform for Tradetron Creators — real-time MTM tracking, drawdown alerts, and Telegram summaries
Tradetron API · Telegram Bot
Job Portal
Sarkari Job Samachar
Comprehensive job portal for government job seekers with real-time updates and notifications
Next.js · Appwrite · SEO · Content Management
AI Agent Development by industry
The sectors we have shipped work in, and what changes about the build in each one.
Related services
AI Agent Development — frequently asked questions
What is the difference between an AI agent and a chatbot?
A chatbot answers. An agent acts — it can look things up in your systems, take a step, read the result and decide what to do next. A chatbot that can only talk is much cheaper and is the right answer whenever answering is the whole job.
When is an AI agent the wrong choice?
Whenever the path is fixed. If the process is always step one, then two, then three, an automation with an AI step in it is cheaper, more predictable and easier to debug. We turn down agent projects on these grounds regularly.
What stops an agent doing something it shouldn't?
Scoped tool access — it can only touch systems you explicitly grant — plus approval gates on anything irreversible or customer-facing, confidence thresholds that trigger a handover, and a complete log of every action so you can audit what happened.
Can an agent work with our existing CRM and tools?
Yes, provided they have an API or a supported integration. That is generally where the build effort goes: the reasoning is the straightforward part, and connecting reliably to real business systems is the work.
How do you measure whether the agent is any good?
Against your own historical cases. We build an evaluation set from work that has already been done, measure how often the agent reaches the same outcome, and report that number before launch rather than after.
Who owns the agent?
You do — the code, the prompts, the configuration, the keys and the accounts, all in your name. There is no platform fee to us and nothing stops working if you stop working with us.
Scope an AI agent
We will tell you honestly if a fixed automation would do the same job for less.