AI agents that finish the work, not just talk about it
We build AI agents that answer customers, qualify leads, read documents and run the back-office steps your team repeats every day. They work inside the tools you already use. A person stays in charge of anything that matters until the agent has earned trust.
The short answer
AI agent development is building software that takes a goal, decides the steps and carries them out in your systems. A single-task agent can be live in 3 to 6 weeks. A workflow agent across several systems takes 8 to 14 weeks. The best first agent is one job your team repeats every day, with a person approving anything that cannot be undone.
Agent, chatbot or plain automation? Know which you need
Many jobs sold as AI agents only need simple automation, which is cheaper and never makes things up. This is how we tell them apart.
| Question | Rule-based automation | Chatbot | AI agent |
|---|---|---|---|
| What it does | Follows fixed if-this-then-that steps | Answers questions in a chat window | Works toward a goal, using your tools along the way |
| Handles messy input | No. It breaks on anything new | Partly. It understands the question | Yes. It reads, decides the next step and checks its work |
| Takes action in your systems | Yes, but only the exact steps you wired | Rarely | Yes, inside limits you set |
| Best for | Simple, stable tasks | Common questions | Multi-step work that used to need a person |
AI agents we build
Six kinds cover most of what businesses ask us for. Each one starts with a single job and grows from there.
Want an agent that answers the phone? That is a voice agent, with its own speed and call-handling problems. See our AI voice agent development page. For more ideas by department, read our AI agent examples.
Types of agent and how pricing works
There are two parts. The build, which we fix in writing before we start. The running cost, mostly model usage and hosting, which grows with how much the agent does. We size both against your real volume so you can compare them with what the work costs you today.
| Type of agent | Typical time | Good fit for |
|---|---|---|
| Single-task agent | 3 to 6 weeks | One job and one or two integrations. A person approves anything that cannot be undone. |
| Workflow agent | 8 to 14 weeks | A multi-step process across three to six systems, with memory and an admin screen. Most business projects land here. |
| Autonomous or multi-agent system | 4 to 9 months, in phases | High volume, regulated data and several agents dividing the work. See our agentic AI page for this tier. |
Want to see what drives the price before you talk to anyone? Our AI agent development cost guide walks through it with a worked example.
How we keep an agent in its lane
An agent that can act can also act wrongly. These rules go in before the first real task, not after the first mistake.
- A person approves refunds, payments, deletions and anything sent to a customer, until the logs show the agent can be trusted
- Every step the agent takes is logged, so you can see what it did and why
- Hard limits on what it can touch, how much it can spend and how many times it can retry
- A test set built from your real cases, run before every change goes live
- A clear handover to a person when the agent is unsure, with the full history attached
When an agent is the wrong answer
- Tasks that happen a few times a month
- Work where one mistake costs more than a year of savings
- Processes nobody has written down or agreed on yet
In those cases we will suggest a plain automation, a better form, or leaving it with your team. Adding AI to tools you already run is covered on our AI integration services page.
How the build runs
- 01
Pick one job worth automating
We look at where your team spends its hours and pick the job with the clearest payback. If the numbers do not work, we tell you before you spend anything.
- 02
Build it with a person in the loop
The first version drafts, suggests and asks. Your team approves. We read every log to see where it gets things wrong.
- 03
Hand over more as trust grows
Steps the agent gets right every time move to automatic. The risky ones stay with a person. You decide, based on the numbers.
How to tell if your agent is working
A demo that looks clever proves nothing. These five numbers show whether an agent earns its keep. We report all five to you every week.
| What we measure | Why it matters |
|---|---|
| Tasks finished without help | The share of jobs the agent completes with no person stepping in. This is the number that pays for it. |
| Accuracy on a sample | Each week a person checks a random sample. If accuracy drops, the agent goes back to asking before it acts. |
| Time saved per task | Minutes a person used to spend, times the number of tasks. Compare it with the running cost. |
| Handovers and why | Every time it passes work to a person, the reason is logged. The top reasons tell us what to fix next. |
| Cost per task | Model and hosting cost divided by tasks done. It should fall as we tune the agent. |
Zygobit in brief
- Zygobit was founded in 2021.
- Our engineering team works from Mohali, India.
- We have offices in East Windsor, New Jersey and Putney, New South Wales.
- We build AI agents, voice agents, chatbots and the software around them.
- You own the code, the prompts and the accounts your agent runs on.
Need more than one agent?
When several agents need to plan, split up work and check each other, that is a bigger build with its own risks. Our agentic AI services page covers those systems.
Prefer to understand the moving parts first? Our plain-English guide to building a custom AI agent explains them without the jargon.
AI We've Actually Shipped
Conversational, agentic and machine learning work running in production. Each card opens the full case study.
AI agent questions
What teams ask before they let an AI agent into their systems.
An AI agent is software that is given a goal and works toward it on its own. It reads what comes in, decides the next step, uses your tools to do it and checks the result. A chatbot only answers questions. An agent can also take action, like updating a record or booking a meeting.
A single-task agent usually starts around $10,000. Agents that work across several systems cost more. Running costs come on top, mostly model usage and hosting, and grow with volume. After a free 30-minute call we give you a fixed build price in writing.
A single-task agent takes about 3 to 6 weeks. A workflow agent takes 8 to 14 weeks. Larger multi-agent systems are built in phases over 4 to 9 months, with the first useful part live early.
Mostly model usage and hosting, plus upkeep. Model usage grows with volume and with how long each conversation is. We size the running cost against your real volume before you commit, and we use smaller models for routine steps to keep it down.
It is safe when the limits are set before it goes live. We start with a person approving every important action. Every step is logged. We cap what the agent can touch and spend, and test it on your real cases before each change. Steps move to automatic only when the logs show they are reliable.
We pick the model per step, not per project. Routine steps often run on smaller, cheaper models, and hard reasoning steps on the strongest ones. We can also use models your company has already approved. The agent is built so the model can be swapped later without a rebuild.
Yes. Agents are only useful when they can read and write to the tools you already use. We connect them through each tool's API, and build a connector where there is none.
Usually not all of it. The agent only needs clean access to the data its one job touches. Part of the first weeks is checking that data and fixing the gaps that would trip it up. A full data clean-up is rarely worth doing before you know what the agent needs.
Yes. We start by reading its logs and testing it on your real cases, so we can see where it fails. Then we tell you whether to fix it or rebuild it. Fixing is cheaper more often than people expect.
An AI agent is one piece of software doing one job or one workflow. Agentic AI is the wider approach, where several agents plan, split up work and check each other. Most businesses should start with one agent on one job and grow from there.
Bring the job your team dreads
Tell us the task, how often it happens and which tools it touches. In thirty minutes we can tell you if an agent pays for itself, what it would cost, and where to start.