AI Agents
AI agents that handle real work — with a human in the loop
We design and build AI agents that answer customers, find answers in your company's documents and sort incoming requests. They work inside the tools you already use, and they hand over to a person when they shouldn't decide alone.
What is an AI agent?
An AI agent is software that uses a large language model to complete a task in several steps: it reads a request, decides what to do, looks things up or acts in your tools — a helpdesk, a CRM, a database — and returns an answer or a finished action. Unlike a chatbot, it can do things, not just reply. Unlike a fixed automation, it can handle requests that don't follow a script. Good agents work inside clear rules and pass the conversation to a person when they're unsure.
Chatbot, automation or agent?
The three get mixed up constantly, and the difference decides what you should build:
| Chatbot | Automation | AI agent | |
|---|---|---|---|
| What it does | Replies to messages from a script or a model | Runs the same fixed steps every time | Decides which steps to take for each request |
| Handles unexpected requests | Poorly | No — it follows the script | Yes, within the rules you set |
| Takes actions in your tools | Rarely | Yes | Yes |
| Best for | Simple FAQs | Predictable, high-volume tasks | Varied, text-heavy requests |
| Example | "What are your opening hours?" | New invoice → copied to accounting | "Where is my order, and can I change the address?" |
Often the right answer is a mix: an agent that understands the request, plus a plain automation that does the predictable part. If a fixed automation is enough, we'll tell you — it's cheaper and easier to maintain. See AI workflow automation with n8n.
Which AI agents do we build?
Customer support agent
Answers customers on chat, WhatsApp and email from your own help docs and order data, and hands over to your team with full context.
See the service →Internal knowledge assistant
Lets your team ask questions in plain language and get answers from company documents, with a link to the source.
See the service →Voice agent for calls & bookings
Answers routine phone calls, takes booking requests and passes anything unusual to a person, with a written summary of the call.
See the service →Inbox agent
Sorts a shared inbox, labels and routes each email, and drafts replies for a person to check and send.
See the service →Lead qualification agent
Asks new leads the questions your sales team would ask, scores them against your criteria and writes the result to your CRM.
See the service →When does an AI agent make sense?
An agent earns its keep when the task is repetitive, text-heavy, governed by clear rules, and easy for a person to check. Typical situations:
- Online stores: "where is my order", returns and delivery questions that follow your policy, answered at any hour.
- SaaS companies: how-to questions that are already answered somewhere in your help centre.
- Agencies and professional services: staff searching contracts, proposals and procedures spread across several drives.
- Service businesses that take bookings: routine calls and messages about availability, prices and changes.
- Sales teams: inbound leads that need the same five qualifying questions before anyone gets on a call.
- Operations teams: a shared inbox where most emails need sorting and a standard first reply.
How does an AI agent work?
- Request. A message arrives through a channel you choose: website chat, WhatsApp, email, a form or a phone call.
- Understand. The model works out what the person wants and checks it against your rules — what the agent may handle and what it must pass on.
- Look up. The agent searches your knowledge base and, where allowed, reads data from your systems, such as an order status or an account plan.
- Draft. It writes the answer or prepares the action, based only on what it found.
- Check. Depending on the risk, the answer goes out directly, waits for a person to approve it, or the conversation is handed over with a summary.
What do we build AI agents with?
We pick tools for each project rather than forcing one stack. Typical building blocks:
Workflows usually run on n8n, which can be self-hosted so your data stays on servers you control. For the model itself we choose between commercial models and open-source ones that can run privately, based on the quality, cost and privacy each project needs. When an agent needs its own interface — an admin panel, a review queue, a client portal — we build it in Bubble.io or Webflow. We aren't partners or resellers of any of these vendors, so the choice is about fit, not commission.
How do we work on an AI agent project?
- Discovery call. We talk through the task, the volume and the tools involved, and tell you honestly whether an agent is the right answer.
- Audit and fixed-scope proposal. We review your knowledge sources and systems, then send a written proposal with scope, timeline and price.
- Build and test with human review. We build the agent, test it on real past requests, and keep a person approving answers until the results are good enough to loosen that.
- Launch and monthly care. We go live on one channel first, watch the conversations, and keep improving the agent as your products and policies change.
Payments are milestone-based: you pay after you approve each stage. Every launch includes one month of free bug fixing.
How much does an AI agent cost?
Setup for a typical AI agent starts at $2,500, and monthly care starts at $300 per month. AI model usage is billed by the model provider and grows with the number of conversations; we estimate it with you before the build. The price mostly depends on:
- How many channels and systems the agent connects to — one website chat is simpler than chat, WhatsApp, email and a CRM.
- The state of your knowledge. Clear, up-to-date help docs make for a faster build than information scattered across emails and people's heads.
- Answers versus actions. An agent that only answers is simpler than one that changes orders, books appointments or updates records.
- How much review you need. Approval queues, audit logs and strict permissions add work, and are worth it in sensitive areas.
Is an AI agent right for your business?
Good fit when…
- The same kinds of requests arrive every day
- The answers already exist in documents, policies or data
- A person can quickly check whether an answer is right
- You're ready to keep a human in the loop at the start
Not a fit when…
- Volume is low enough that a person handles it comfortably
- Each case needs judgement that can't be written down
- Mistakes carry legal, medical or financial risk with no review step
- The information the agent would need isn't written anywhere
Frequently asked questions
What is the difference between a chatbot and an AI agent?
A chatbot replies to messages, usually from a script or a fixed set of answers. An AI agent decides which steps to take for each request: it can look up data in your systems, take actions such as updating an order, and hand the conversation to a person when it isn't sure. Agents suit varied, text-heavy requests; simple chatbots suit short FAQ lists.
How much does it cost to build an AI agent?
At Webziper, setup for a typical AI agent starts at $2,500, with monthly care from $300 per month. AI model usage is paid to the model provider and depends on the number of conversations. The final price depends on the channels, the systems the agent connects to, and whether it only answers or also takes actions.
Will an AI agent replace my team?
No. A well-built agent takes over the repetitive part of the work, such as routine questions and first replies, and passes everything unusual to a person with a summary. Your team spends less time on copy-paste answers and more on the cases that need judgement.
Is my company data safe with an AI agent?
It can be, if the agent is designed for it. We give the agent access only to the data it needs, keep API keys on the server, log what it does, and can run workflows on self-hosted n8n or use open-source models running privately when data must not leave your infrastructure.
How long does it take to build an AI agent?
A typical first version takes 3–6 weeks, including testing on real past requests. Agents with many integrations or strict review steps take longer. We usually launch on one channel first and add others once the results are reliable.
Have a task that eats hours every week?
Tell us what it is. We'll tell you honestly whether an AI agent fits — and what it would take to build.
Book a Free Call →