AI Agent Development
Dzimark builds custom AI agents for businesses in the USA, UK, Canada and Australia. Unlike a chatbot that only answers questions, an AI agent can take the next step: look up an order, update your CRM, book an appointment, draft a quote or move work between systems. We design every agent with clear limits, human approval where it matters and a full log of what it did, so you get the time savings without losing control.
Updated November 5, 2026
AI Agent: what's included.
Customer service agents that resolve requests, not just answer them
Sales and lead agents that qualify, book and update your CRM
Operations agents for orders, invoices, scheduling and data entry
Research and reporting agents over your own documents and data
Guardrails, human approval steps and audit logs
Integrations with the tools you already use
AI Agent Development: in detail.
What an AI agent is, and how it differs from a chatbot
A chatbot answers questions from what it knows. An AI agent works towards a goal: it decides which steps are needed, uses tools to carry them out and checks the result. For example, a chatbot can tell a customer your returns policy, while an agent can find their order, confirm it is eligible, create the return label and email it to them.
That extra power is why agents need more careful design. Every action an agent can take is a tool we build deliberately, with limits on what it can change and when it must ask a person first. If your goal is mainly to answer questions, a well-built chatbot is often the better and cheaper choice, and we'll tell you so.
Types of AI agents we build
Customer service agents handle the requests that fill your inbox: order status, changes, cancellations, returns, appointment changes and account questions, using your real systems rather than canned answers.
Sales and lead agents reply to new enquiries in seconds, ask the qualifying questions your team would ask, book calls into your calendar and log everything in your CRM.
Operations agents take on repetitive back-office work: reading invoices and purchase orders, updating spreadsheets and accounting tools, chasing missing information and preparing daily summaries.
Research and reporting agents search your documents, tickets and databases to answer internal questions or prepare reports, and show where each answer came from.
Tools and integrations
An agent is only as useful as the systems it can reach. We connect agents to the tools you already use through their official APIs: CRMs such as HubSpot, Salesforce or Zoho; helpdesks such as Zendesk or Freshdesk; ecommerce platforms such as Shopify and WooCommerce; calendars, email, Slack and Microsoft Teams; spreadsheets, databases and your own software.
Where a workflow is better handled by an automation tool such as n8n or Make, we combine the two: the agent makes the judgement calls, and the automation does the reliable, repeatable steps.
Guardrails, approvals and evaluation
We decide with you what the agent may do on its own, what needs a person's approval and what it must never do. Refunds above a set amount, messages to all customers or changes to prices, for example, can always wait for a human to approve them.
Before launch we test the agent against a set of real examples from your business, including awkward and unusual cases, and measure how often it chooses the right action. After launch, every action is logged with the reason the agent gave, so you can review what happened, spot mistakes early and improve the agent over time.
How an AI agent project runs
Discovery: we map the process the agent will take over, the systems involved, the decisions a person makes today and what success looks like.
Prototype: we build a working agent on a small slice of the process and test it against real examples, so you can see how it behaves before committing to the full build.
Build and integrate: we connect the tools, add guardrails and approval steps, and set up logging and monitoring.
Launch: the agent starts with a limited scope, often with a person reviewing its actions, then takes on more as it proves reliable.
Improve: we review logs and feedback, fix weak spots and add new tasks.
When an AI agent is the wrong choice
Agents are not the answer to every problem. If a process follows the same fixed steps every time, a standard automation is cheaper and more predictable. If the work involves high-stakes judgement with no room for error, an agent should support a person rather than act alone. And if the data or systems the agent needs are not available through an API, that has to be solved first.
We start every project by checking whether an agent, a chatbot, a simple automation or a mix of them is the best fit, so you don't pay for complexity you don't need.
Models, data and security
We choose the AI model for each job, from leading hosted models to open-weight models that can run on your own infrastructure when data must stay in-house. The agent only gets the access it needs, credentials are stored securely, and sensitive data can be masked before it reaches the model.
We design around the privacy laws that apply to you, such as UK GDPR, Canada's PIPEDA, Australia's Privacy Act and US state laws, and document where data is stored and processed so your team or adviser can review it.
Related services
Many projects start with an AI chatbot and add agent abilities later. See our AI chatbot development service for customer-facing chat, AI voice agents for phone calls and appointment booking, and AI automation for n8n, Make and Zapier workflows. Our guide to AI agent development cost explains what drives the budget for an agent project.
AI Agent: where it makes a difference.
Order and returns agent for online stores
Finds the customer's order, checks the returns policy, creates the return and sends the label, with refunds above a set amount waiting for staff approval.
Lead qualification and booking agent
Replies to new web or WhatsApp enquiries, asks qualifying questions, books a call in your calendar and creates the deal in your CRM.
Invoice and document processing agent
Reads invoices and purchase orders from email, extracts the details, matches them against your records and flags anything that doesn't add up.
Internal knowledge and reporting agent
Answers staff questions from your policies, tickets and databases, and prepares weekly summaries with links to the sources.
AI Agent: questions, answered.
What is the difference between an AI chatbot and an AI agent?
A chatbot answers questions. An AI agent can also take actions in your systems, such as updating an order, booking an appointment or creating a CRM record, within limits you set. Many businesses start with a chatbot and add agent abilities once it is working well.
Can an AI agent make mistakes?
Yes, like any system it can. That is why we limit what each agent can do, require human approval for sensitive actions, test it against real examples before launch and log every action so mistakes are caught and fixed quickly.
Which systems can your AI agents connect to?
Any system with an API, including CRMs such as HubSpot, Salesforce and Zoho, helpdesks, Shopify and WooCommerce, calendars, email, Slack, Microsoft Teams, spreadsheets, databases and custom software.
Which AI models do you use?
We choose per project: leading hosted models for most tasks, and open-weight models that can run on your own servers when data has to stay in-house. The agent is built so the model can be changed later.
How long does an AI agent project take?
It depends on the number of tasks and systems involved. We usually start with a working prototype on one part of the process, then expand. You get a written plan with milestones in our proposal before any work starts.
What does an AI agent cost?
The main cost drivers are the number of tools and integrations, how complex the decisions are, the level of testing and approval needed, and ongoing model usage. Our guide to AI agent development cost explains these in detail, and we quote each project in writing.
Is our data safe?
Agents only get the access they need, credentials are stored securely, sensitive data can be masked before it reaches the AI model, and we document where data is stored and processed. We design around the privacy laws that apply to your business.
When should we not use an AI agent?
When a process always follows the same fixed steps, a standard automation is usually cheaper and more reliable. When a decision is high-stakes with no room for error, the agent should assist a person rather than act alone. We'll recommend the simplest option that does the job.
Who owns the agent when the project is finished?
You do. When all invoices are paid, you own the custom code, prompts and configuration we built for you, and you can host and run the agent on your own accounts.
How do we get started?
Book a free call and tell us which process you'd like to hand to an agent. We'll review it, suggest the best approach and send a written proposal with scope, timeline and cost.
Let's put AI to work in your business.
Tell us what you want to automate or build. We reply within one working day with ideas and a free estimate.
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