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.
What people are actually describing
Most enquiries here describe a job a person does, not a technology. The useful question on the first call is whether it needs judgement, because that is the line between automation and an agent.
“Somebody has to read every enquiry and decide where it goes.”
Reading intent from unstructured text and routing it. A rules engine cannot do this well and a person does not need to. The clearest agent case there is.
“Our team chases the same follow-ups every week.”
An agent that reads the thread, decides whether a reply is needed and drafts it, with a human approving before anything goes out until you trust it.
“Support answers the same tier of question forever.”
Handle the repeatable tier, recognise its own limits and hand over with the history attached. The handover rule is the design decision that matters.
“We want it to take actions, not just reply.”
Booking, updating a record, raising a ticket. Every action gets a permission boundary and an audit trail, and anything irreversible gets an approval gate.
“We tried one and it did something we did not expect.”
Usually no constraint on what it was allowed to do. An agent needs a defined set of tools and a hard boundary, not just a good prompt.
“Our process is fixed and never varies.”
Then you do not want an agent. You want automation, which is cheaper to build, cheaper to run and far more predictable.
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.
Agent, automation, or chatbot?
These three get used interchangeably in sales conversations and they are different products with different costs and different failure modes. This is the split we work to.
| If this is true | What you want | Why |
|---|---|---|
| The steps are the same every time | Automation | Rules you wrote, running the same way every time. Cheaper, faster and predictable. Do not pay for judgement you do not need. |
| Something has to be read and interpreted before deciding | AI agent | Intent, priority and routing from unstructured input. This is what an agent is actually for. |
| A customer asks questions and wants answers | Chatbot | Conversation grounded in your content. Answering is not the same problem as deciding and acting. |
| It answers the phone | Voice agent | Telephony, latency and interruption handling make this its own build. |
| It has to act inside your systems | Agent with tool permissions | The tools it may use, and the approval gate before anything irreversible, are the build. Not the model choice. |
| You need it to be right every single time | Rules, with AI assisting | An honest limit. Where a wrong answer is unacceptable, the decision stays deterministic and the model only drafts or suggests. |
When not to build an agent: if you cannot describe what it is allowed to do and what it must never do, it is too early. That boundary is the specification, and writing it is the first session.
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.
- AI agent packages in USD — from $899Single agent, multi-agent system, and a custom agent platform.
- AI voice agent packages — from $699Phone agents that answer, qualify and book.
- AI chatbot packages — from $299When answering questions is the whole job.
- Every published package price on one pageBoth markets, all services.
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.
Sources & further reading
The primary documentation behind the claims on this page.
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.
Reference
Terms and guides worth reading first
Plain definitions and walkthroughs from our own reference library. No sign-up, no gate.
- EvalsEvals are a fixed set of test cases with known-good answers, run against an AI system to measure whether a change made it better or worse
- IdempotencyAn operation is idempotent if running it twice has the same effect as running it once
- WebhookA webhook is an HTTP request one system sends to another the moment something happens
- API IntegrationAn API integration is a connection between two software systems that lets them exchange data automatically
- AI Voice AgentAn AI voice agent answers phone calls, understands what the caller wants and takes action
- AI AgentAn AI agent is a program that uses a language model to decide what steps to take toward a goal, then carries them out by calling real tools
Best AI Agent Development Company in India To Handle Work That Needs a Decision, Not Just an Answer
An agent earns its cost when the next step genuinely depends on what the last step found. A lead that needs a different follow-up question depending on the answer given. A support request whose resolution depends on what the order history actually shows. That branching is what separates an agent from an automation, and it is also what makes agents more expensive and considerably riskier to get wrong.
When the path is actually fixed, an agent is the expensive way to build something an automation would do for a fraction of the price, and we say so on the first call. Most enquiries about agents turn out to be better served by an automation with an AI step inside it, and working out which one you actually have is most of what that first call is for.
The boundary comes before the build
An agent with tools and no boundary is not a product, it is an incident waiting to happen. Before anything is built, we write down which systems it can read, which it can write to, what always needs a human's sign-off, and what it must never attempt under any circumstance. That document exists before a single prompt is written.
Agents worth building
Not every business process needs a decision-maker sitting inside it. These are the ones where it consistently does.
- Lead qualification agents that ask follow-up questions, score against your real criteria, and book the call or mark it not worth one, with the reasoning attached
- Customer support agents that look up the actual order or account and resolve what can be resolved, handing the rest to a person with context already gathered
- Sales follow-up agents that work a quote through its sequence, read the replies, and escalate to a human at the point a human adds something
- Internal operations agents doing multi-step back-office work such as reconciling records across systems, with a full audit trail of every action taken
What stops it from doing something it shouldn't
Scoped tool access means the agent can only touch systems it has been explicitly granted, nothing else. Approval gates sit on anything irreversible or customer-facing. Confidence thresholds trigger a handover rather than a guess, and a complete log of every action taken means an audit of what happened is always possible after the fact, not a matter of trusting the system's word for it.
What it costs
Agent packages are published in USD for the US market, starting with the single-agent tier, alongside published packages for voice agents and chatbots where those are the actual fit. India projects are quoted to scope after the first call, because agent scope varies far more from client to client than a website's does, and a single published number would be dishonest about that range.
Measured against your own history, not a demo
We build an evaluation set from work your business has already done, then measure how often the agent reaches the same outcome a person would have. That number is reported before launch, not offered as an impression after watching it work once in a demo. Launch itself usually runs in shadow mode first, where the agent proposes and a person approves, with autonomy increasing only as the log shows it earning it.
When you don't need an agent
If the process is always step one, then two, then three, in the same order every time, an automation with an AI step in it is cheaper, more predictable and considerably easier to debug when something goes wrong. We turn down agent projects on exactly this basis on a regular basis, because building the more expensive thing when the cheaper thing does the job is not a service worth selling.
If answering a question is genuinely the whole job with no action required afterward, a chatbot grounded in your knowledge base is a simpler and cheaper build than an agent, and it is the honest recommendation whenever that is true.
Ownership after launch
The code, the prompts, the configuration, the keys and the provider accounts are all in your name, not licensed from us. There is no platform fee to us, and nothing about the agent stops working if you stop working with us. That is the same principle every build on this site follows, and it matters more with an agent than almost anywhere else, because an agent touching your real systems is not something you want tied to a vendor relationship you cannot walk away from.
Talk to us about ai agent development
Tell us what you are trying to build and where you are based. We call back within 2 hours during business hours.