AI Agent Development for Businesses
An AI agent carries multi-step work through on its own instead of just replying in chat. It reads the request, looks things up in your systems, uses the tools it is allowed to, and hands anything with real consequences to a person to approve.
Common Pain Points
Work an AI agent should take over
Your team spends the day hopping between systems: read the email, check the ERP, retype it into the CRM
Your chatbot can answer questions but can't do the follow-up, so customers still wait for a person
Rule-based automations break every time the input doesn't match the expected format
You want AI doing real work, but worry it will overstep without anyone noticing
Pick the right tool
How is an AI agent different from a chatbot or automation?
Chatbot
- What it does
- Answers questions from the data you give it
- Messy input
- Fine, if it's a question
- Uses other systems
- Mostly not
- API cost per task
- Low
- Best for
- Answering customers, repeat questions
Automation (n8n / Make)
- What it does
- Runs steps you wrote in advance
- Messy input
- Breaks, needs another rule
- Uses other systems
- Yes, the ones you wired up
- API cost per task
- Lowest
- Best for
- Repeat work whose steps never change
AI Agent
- What it does
- Decides the next step itself, then uses tools to do it
- Messy input
- Reads it and works it out
- Uses other systems
- Yes, within the permissions you grant
- API cost per task
- Higher, it reasons several times per task
- Best for
- Work that needs reading, a decision, then steps across systems
💡 Our take: the steps are the same every time → n8n automation is cheaper and harder to break · you only need answers → an AI chatbot is enough · it has to read, decide and act across several systems → that's where an agent pays off. Most real systems mix them: automation as the frame, with an agent only on the steps that need judgement.
Examples
AI agents a business can put to work
Customer request agent
Reads messages from LINE OA, email or your website, checks order status in your system, answers what it can and hands the rest to your team with a summary.
Sales agent
Drafts quotes from ERP prices and stock, updates lead status in the CRM, and summarises a customer's history before a meeting.
Document agent
Reads invoices, POs and PDFs, enters the data into your system, and flags only the lines that don't match for a person to check.
Internal knowledge agent (RAG)
Answers your team's questions from SOPs, contracts and manuals, and says which document and page each answer came from.
Connected through MCP and APIs
Lets the agent use Google Workspace, your CRM, a database or your own API, with only the permissions that task needs.
A person approves real actions
Sending email, changing records or passing work on needs a person to confirm first, and every tool call is logged so you can see exactly what the agent did.
Process
Start with one task, then expand
Pick one task first
Find a task that happens often, eats staff time and can be measured. Start with one, not the whole company
Prototype on real data
Build the first agent against your real systems and data, then test it on past cases where you already know the right answer
Set limits and approvals
Decide what the agent may do alone and what waits for a person, cap the API spend, and log every step
Go live, then expand
Put it in front of the team, watch where it goes wrong, fix that, then move on to the next task
How we build
An agent doing real work needs limits
Only the access the task needs
The agent reads only the data its task needs, and writes only where you allow it to
A person confirms anything irreversible
Emailing a customer, changing a price or placing an order waits for a person. When information is missing, the agent stops and hands over a summary instead of guessing
Every tool call is logged
You can see what the agent saw, what it decided and what it did. Every mistake becomes a test case for the next round
A spending cap
A limit on reasoning steps per task and on API spend per day, so the bill never surprises you
Technology
Tech Stack
Got a task you'd hand to an agent?
Tell us about it and we'll say plainly whether it needs an AI agent, an automation or a chatbot. Free to talk.
Cost
An AI agent costs you in two ways
First, the build: a one-off cost set by the scope, meaning how many systems it connects to and how many steps it takes. We quote it after we've talked through the actual task. Second, the model's API usage, billed monthly by what it actually uses. An agent reasons several times per task, so it uses more tokens than a typical chatbot. Before we start, we estimate that in baht per month from your volume and set a cap so it stays in budget.
FAQ
Questions before building an AI agent
We start with work where a mistake can be undone, and anything irreversible waits for a person to approve. Every tool call is logged, so you can see where it went wrong, and that case joins the test set so it doesn't happen again.
We settle this before starting. We choose a model provider whose data terms you accept, or run an open-source model on your own server, and the agent only sees the data its task needs, in line with PDPA.
It depends on the task. For multi-step work with many tools we usually start with a flagship Claude or GPT model, and route simple high-volume steps to a smaller, cheaper one to keep API costs down.
Yes. The agent receives LINE OA messages through the Messaging API and can reply or hand over to your team, the same as email or website chat.
No. We design, build and deploy it. If you do have an IT team, we hand over the code and documentation so they can run it.
Further reading