AI agent development company for agentic AI that works inside your systems
Webify.AI builds AI agents that act: they look up orders, update records, book slots and hand over to a person when they should. Have us build a custom agent as a project, start from a pre-trained Bot Store agent adapted to your systems, or hire our AI agent developers by the hour.
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What does an AI agent development company build?
Software in which a language model works out the next step and carries it out through tools: it looks up an order, updates a record, books a slot or drafts a reply. We build agents for customer support, sales and lead qualification, back-office operations and internal knowledge, connected to the systems you already run.
A chatbot answers. An agent acts. Each action is a separate tool with its own permissions, so the agent can only do what you have allowed, and a person approves anything with consequences, such as a refund or an outbound message. A project can start as a question-answering assistant and add one tool at a time.
- Customer support: answers with live order or account data, resolves routine requests and hands the rest to your team with context. See AI customer support agents.
- Sales: replies to new leads in seconds, asks qualifying questions and books calls into your calendar. See AI lead qualification.
- Marketing: follows up abandoned quotes and bookings, and drafts segments, campaigns and reports for your team to approve. See AI marketing agents.
- Retail: writes and updates product listings in bulk across storefronts and marketplaces. See AI teams for retail automation.
- Operations: approvals, data entry and status updates across your back-office systems. See workflow automation.
- Internal knowledge: answers staff questions from your policies and documents, showing the passage behind each answer. See internal knowledge assistants.
What is agentic AI, and how is it different from generative AI?
Agentic AI is software that works towards a goal: it plans the steps, uses tools to carry them out and checks the result. Generative AI produces content when asked and stops there. An agent uses the same language models, but adds tools, limits and a record of what it did, so it can finish a task.
Swipe the table sideways to see every column.
| Tool | What it does | Where it stops | Good for |
|---|---|---|---|
| Generative AI assistant | Writes or summarises when someone asks | Hands the text back to a person | Drafts, summaries and research |
| Chatbot | Answers questions from a script or your documents | Cannot change anything in your systems | FAQs and first-line information |
| RPA or workflow automation | Follows fixed rules between systems | Stops or fails when the input varies | High-volume steps that never change |
| AI agent | Reads the request, chooses a tool, acts and checks the result | At the limits you set, or by handing over to a person | Tasks with varied input and clear right and wrong outcomes |
Not every job needs an agent. Fixed steps with one AI step are cheaper to build and easier to test, and we will tell you when that is the better answer. Our guide to AI agents vs chatbots vs RPA goes through how to decide.
An agentic AI solution is the whole system around the model: the tools, the permissions, the test set, the approval steps, the logs and the monitoring. That wider system decides whether a project lasts. Gartner predicted in August 2025 that up to 40% of enterprise applications would include task-specific agents by 2026 (Gartner). In June 2025 it also predicted that over 40% of agentic AI projects will be cancelled by the end of 2027, because of cost, unclear value or weak risk controls (Gartner).
What kinds of AI agents can you build?
Agents for jobs with varied requests and clear outcomes: customer support, sales, marketing, retail catalogues, back-office operations, internal knowledge, HR and IT requests, and voice. Each one connects to the systems that hold the data and has written rules for what it may do alone and what a person approves.
Swipe the table sideways to see every column.
| Agent | Typical job | Connects to | What a person approves |
|---|---|---|---|
| Customer support | Answers with live order or account data, resolves routine requests and hands over the rest | Help desk, CRM, order system | Refunds and exceptions outside your policy |
| Sales and lead qualification | Replies to new enquiries, asks qualifying questions and books calls | CRM, calendar, web forms, WhatsApp | Outbound messages, if you choose |
| Marketing | Follows up abandoned quotes and bookings; drafts segments, campaigns and reports | CRM, email platform, ad accounts, analytics | Anything sent to customers, and any budget change |
| Retail and catalogue | Writes and updates listings in bulk across storefronts; handles orders and returns | Store platform, ERP, PIM, marketplaces | Listings before publishing; refunds outside policy |
| Back-office operations | Routes approvals, enters data and updates statuses across systems | ERP, shared inboxes, spreadsheets | Payments and changes to master data |
| Internal knowledge | Answers staff questions from your policies and shows the passage behind each answer | SharePoint, Confluence, Google Drive | Nothing routine; sensitive questions go to a person |
| HR and IT requests | Handles leave, policy and ticket requests inside Slack or Teams | HR system, service desk | Grievances and anything outside its scope |
| Voice | Answers your existing number, books and updates, and transfers with a summary | Contact-centre platform, CRM | Calls transferred to your team on request |
These are typical set-ups. The rules for each agent are agreed in writing for your business before a build.
What are the ways to get an AI agent from Webify.AI?
Three. Start from a pre-trained agent in our Bot Store and adapt it to your systems, have us build a custom agent as a defined project, or add AI agent developers to your own team by the hour. We will say plainly which fits, including when an existing agent is close enough.
A pre-trained Bot Store agent
91 role-based agents across 26 industries, from hospital front desks to credit card servicing. Adapting one is usually faster than starting from nothing.
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A custom agent, built as a project
You describe the task and how you will judge success. We scope it, agree the success criteria in writing and deliver it.
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AI agent developers in your team
Named engineers you interview, from $45 an hour (£36 in the UK), who join your stand-ups and commit to your repositories.
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A private agent on your own servers
For data that must not reach an outside AI service: an open-weight model on your hardware, air-gapped or in an in-country cloud.
→ Read more
What makes a good first AI agent project?
A task that happens often, follows rules you can write down, has answers you can check, and costs little if the agent hands over instead of finishing. One workflow and one or two systems is enough. Avoid starting with anything where a single wrong action is expensive.
- Volume: it happens often enough that the saved time adds up.
- Clear outcomes: you can tell a right answer from a wrong one, so the agent can be tested.
- Available data: the documents and records it needs are current, and you can give access to them.
- Safe to fail: a wrong draft or a handover to a person costs little.
- An owner: someone on your side who will read samples and decide when to widen it.
If a task fails these tests, a simpler workflow or a person may be the better answer. Discovery exists to find that out before you pay for a build.
Should you adapt a Bot Store agent, commission a custom agent or hire engineers?
Adapt a Bot Store agent when one is close to what you need. Commission a custom agent when you want a defined result at a quoted price. Hire engineers by the hour when you have someone to lead them and want to set the priorities yourself.
Swipe the table sideways to see every column.
| Route | Best when | Who steers the work | How it is priced |
|---|---|---|---|
| Bot Store agent, adapted | An existing role fits your workflow | We do, with your sign-off | Agreed after we check the fit with your systems |
| Custom agent, as a project | The task is specific to your business | We do, against written success criteria | Written quote after discovery, before any build |
| Engineers in your team | You have a technical lead and a changing backlog | You do | $45 to $60 an hour, or monthly plans |
| Private agent on your servers | Data must not reach an outside AI service | We do, then hand over | Fixed-price pilot from $1,000 |
There is a fifth option: buy a ready-made product and build nothing. Our guide to custom agents vs off-the-shelf chatbots covers when that is the right call.
Where do your AI agents run?
Where your data rules allow. Most agents use a hosted model through an API under your own account. As an IBM Partner we also deploy on IBM watsonx. When data must stay inside your network, the agent runs on an open-weight model on your own servers, and we hold none of your data.
| Option | Suits | What to weigh |
|---|---|---|
| Hosted model API, under your account | Most customer-facing and internal agents | Check the provider's data terms and your account settings before sending real data. |
| IBM watsonx | Teams that want a vendor-supported enterprise platform | Platform costs are separate from our rates; we deploy and integrate it. |
| Open-weight model on your servers | Regulated data, client confidentiality, air-gapped sites | You buy or rent the GPU; see private AI for hardware and pilot prices. |
Which systems can your AI agents connect to?
Any system with an API or another way for software to read and write records. Each action is a separate tool with its own permissions, tested on test data before it touches live records. A first agent usually connects to one or two systems, and more are added once those run well.
- CRM: Salesforce, HubSpot, Zoho and Microsoft Dynamics.
- Service desks: Zendesk and ServiceNow.
- ERP: SAP, NetSuite and Oracle.
- HR: Workday.
- Documents: SharePoint, Confluence and Google Drive.
- Channels: web chat, WhatsApp, email, voice, Microsoft Teams and Slack.
A system without an API can often be reached through scheduled file exchange instead. Omnichannel and integrations sets out what each channel needs on your side, and security covers how access to your systems is controlled.
How do you deliver an AI agent project?
In stages, with a decision at each one. We agree in writing what success looks like, prove it on one workflow with your own data, and only then build for production. At each stage you can change the scope or stop, and what we have built so far is yours.
- 1
Discovery
Map candidate workflows, check your data, systems and access, and pick one with clear right and wrong answers.
- 2
Written success criteria
The accuracy to reach on a test set of your real requests, the response time, and who reviews the outputs.
- 3
Pilot on one workflow
Built in your accounts and repositories, on your own data, with only the tools and permissions the agent needs.
- 4
Evaluate and decide
We run the test set and report the scores, including the failures. You choose production, a change of scope or a stop.
- 5
Production and handover
Integrations, approvals, logging and monitoring, then documentation and a runbook for whoever runs it next.
How we build covers each step in detail, and our guide to how long an AI agent takes to build breaks down the timeline.
How do you keep an AI agent from doing the wrong thing?
By limiting what it can do, checking what it tries to do and recording what it did. Every agent gets narrow tools, accounts with the least access that works, validated inputs, human approval for risky actions, spending and rate limits, and full logs. We test it against hostile inputs such as prompt injection before real users reach it.
- Tested before launch against a set of real requests, and re-tested after every change to a prompt or model.
- Tells users they are talking to an AI and gives them a way to reach a person.
- Hands over with the conversation, what it tried and what it found, so nobody starts again.
- Code, prompts and test sets live in your repositories, and the IP is assigned to you. Our security page sets out the contract terms.
What can go wrong with an AI agent, and how is it controlled?
An agent can give a wrong answer, take an action nobody intended, be tricked by a hostile input, run up costs, or change behaviour when a model changes. Each risk has its own control, and all of them are tested before launch and again after every change.
| Risk | What it looks like | Control |
|---|---|---|
| Wrong answers | A confident answer that is not in your documents | Answers drawn from your sources, scored against a test set, with handover when unsure |
| Too much access | The agent does something nobody intended | Narrow tools, least-privilege accounts and human approval for consequential actions |
| Prompt injection | A message or document tries to give the agent new instructions | Validated inputs and limited tools, tested with hostile examples from the OWASP Top 10 for LLM applications |
| Runaway cost | A loop or a script runs up model usage | Spending and rate limits per user and per task |
| Model changes | A new model version changes how the agent behaves | The same test set re-run after every change to a prompt, a model or a tool |
| Hard to change later | Only the vendor can maintain it | Code, prompts and test sets in your repositories, with documentation and a runbook |
What does AI agent development cost, and how long does it take?
A first agent scoped to one task with one or two integrations typically takes six to twelve weeks from discovery to production. Engineering time is billed at our published rates: $45 an hour for an AI / LLM engineer and $60 for a senior AI engineer. Model usage is billed separately by your provider.
Swipe the table sideways to see every column.
| Type | What it can do | Hours | Engineering cost |
|---|---|---|---|
| Knowledge-base assistant | Answers from your documents | 240 to 480 | $8,400 to $16,800 |
| Connected assistant | Reads orders, accounts or cases for a signed-in user | 480 to 960 | $22,800 to $45,600 |
| Agent that takes actions | Creates, changes or books things within set limits | 800 to 1,600 | $42,000 to $84,000 |
Planning estimates, not quotes. Our AI agent cost guide shows how they compare at other market rates and what moves them up or down.
Running costs come on top: model usage billed by your provider, hosting in your own account and, by our estimates, 20 to 40 hours a month of maintenance in the first months.
A defined project is quoted in writing after discovery, before any build starts. Private agents that run offline start with a fixed-price pilot from $1,000, described on the private AI page. Every rate, in US dollars and pounds, is on our pricing page.
These are typical ranges, not promises. Access to your systems and data usually decides the pace more than the model does.
Who will work on our agent, and how do we work together?
Named engineers you can interview, from our Ahmedabad delivery centre. A senior AI engineer leads the design, AI / LLM engineers build the agent and its tools, and developers handle the integrations. They join your stand-ups, work in your tickets and commit to your repositories.
- UK teams: 09:00 to 13:00 UK time every working day, inside a normal Indian working day, at no extra cost.
- US teams: 09:00 to 12:00 US Eastern time through an evening shift, at 15% extra.
- A short written update at the end of each Indian working day, and a demo every week.
- We sign your NDA before the first call, and developers are named in the contract.
- Free replacement within five working days, with a handover overlap, for as long as the contract runs.
Our guide to working across time zones with an Indian team covers the daily routine. If you are an agency delivering for clients, the white-label program works the same way under your brand.
How do you choose an AI agent development company?
Ask for evidence of how they work, not a list of logos. Find out who will write the code, how the agent is tested, who owns the result and what happens when you leave. A good company will also tell you when you do not need an agent at all.
- Do they build and run agents of their own, and can you see them?
- Who exactly will write the code, and can you interview them first?
- What is the test set, who scores it, and will they show you the failures?
- Will the code, prompts and test sets live in your repositories, with the IP assigned to you?
- How do they limit what the agent can do, and how do they test against prompt injection?
- What will it cost to run each month, and who pays for model usage?
- What happens when the underlying model changes?
- Which security certifications do they hold, and can you check them yourself?
Two warnings from Gartner are worth keeping in mind. In June 2025 it estimated that of the thousands of vendors claiming agentic AI, only about 130 are genuine, a practice it calls agent washing (Gartner). In September 2026 it predicted that 70% of enterprises will abandon agentic AI built through vendor forward-deployed engineering by 2028, as costs climb and customers cannot change the system without outside help (Gartner). Ownership and knowledge transfer are the answer to the second, so ask for both in writing.
Our own answers: our agents are listed in the Bot Store, engineers are named and you interview them, and your code, prompts and test sets stay in your repositories. We hold no ISO 27001 or SOC 2 certification, and our security page says what we do instead. Our guide to vetting an offshore AI company has a fuller checklist.
Frequently asked questions
How much does an AI agent cost?
By our planning estimates, an assistant connected to your systems takes 480 to 960 hours, or $22,800 to $45,600 at our rates, and an agent that takes actions takes 800 to 1,600 hours, or $42,000 to $84,000. Running costs come on top. See our AI agent cost guide.
How long does it take to build an AI agent?
A first agent scoped to one task with one or two integrations typically takes six to twelve weeks from discovery to production. These are typical ranges, not promises. Our timeline guide explains each stage.
What is the difference between an AI agent and agentic AI?
An AI agent is one piece of software that acts through tools. Agentic AI is the wider approach: giving software a goal, tools and limits, whether that means one agent or several working together. Most first projects need only one.
Is agentic AI safe to use with customer data?
It can be, with the right limits. Each agent gets the least access that works, logs every action and asks a person to approve risky steps. Check your model provider's data terms before sending real data. If data must not leave your network, the agent can run on your own servers: see private AI.
Do we keep the code if we stop working with you?
Yes. The code, prompts, test sets and configuration live in your repositories, the IP is assigned to you in the contract, and model and cloud accounts are in your company's name. Any engineer can carry the work on.
Do we need our own AI model to build an agent?
No. Most agents call a hosted model through an API under your company's account. If your data must not leave your network, the agent can run on an open-weight model on your own servers instead.
Can an AI agent work inside our CRM or help desk?
Yes, if the system has an API or another way for software to read and write records. Each action becomes a separate tool with its own permissions and is tested on test data first. A first agent usually starts with one or two systems.
Which channels can the agent use?
The agent's logic is separate from the channel, so the same agent can sit behind web chat, WhatsApp, email, voice or Microsoft Teams. Each channel needs its own connection and testing, so most teams launch on one channel first. See omnichannel.
What happens when the agent cannot answer?
It says so and hands the case to a person, with the conversation, what it tried and what it found. Designing that path is part of every build.
Can we start from a Bot Store agent instead of a custom build?
Yes. If an agent in the Bot Store is close to what you need, we will say whether adapting it makes more sense than building a new one.
Who maintains the agent after launch?
Whoever you choose. Keep an engineer on an hourly plan for upkeep, or hand the agent to your own team with documentation and a runbook. The code is in your repositories either way.
Do you work with US and UK companies?
Yes. Engineers work from our Ahmedabad delivery centre with daily overlap: 09:00 to 13:00 UK time at no extra cost, or 09:00 to 12:00 US Eastern on an evening shift. US clients pay in dollars and UK clients in pounds, by local bank transfer.