Your fantasy, executed.
We build AI agents for the work you can’t skip but never set out to do. They run it end to end; your people keep the calls that matter.
I wish Friday’s books closed themselves.
- Pull this week’s transactionsbank.export
- Match receiptsdrive.search
- Flag what doesn’t add uprules.check
- Review the 3 flagged itemsYour callA $1,240 vendor charge has no receipt.
- Send the close reportgmail.send
Sketch mode: scripted for this page. The real thing runs on your systems, with your approvals.
Agents we build
Necessary, repetitive, and not your core business — that’s where an agent pays off first.
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Operations
Month-end closes, reporting, and the data that moves between the tools you already pay for. The agent does the routine; you sign off on the exceptions.
A first agent: the Friday close, from bank export to report.
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Engineering workflows
Coding agents like Claude Code, set up inside your repository with your conventions, guardrails and review gates — so what they write is code your team merges.
A first agent: ready issues turned into reviewed pull requests overnight.
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Customer-facing agents
Support and conversation agents grounded in your own docs, with a clean handoff to a person whenever money, risk or a frustrated customer is involved.
A first agent: overnight replies in every language your customers write in.
How we work
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Sketch
One working session to map the fantasy: what you would hand off, what must never go wrong, and what “done” looks like.
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Harness
We build the agent and everything around it — tools, permissions, tests, and the checkpoints where a person decides.
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Prove
Every task leaves evidence you can check: a log, a diff, a receipt. Nothing is marked done on the agent’s word alone.
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Hand over
Runbooks, monitoring and hands-on training, so your team can run and extend the agent. You own the harness; we stay on call if you want us.
Agents do the work.People make the calls.
Questions
What does Agentasy build?
AI agents for the work a company can’t skip but never set out to do: operations such as month-end closes, reporting and approvals; engineering workflows built on coding agents like Claude Code; and customer-facing support agents grounded in your own docs.
Do the agents make decisions on their own?
No. Agents run the routine end to end and stop at checkpoints where a person decides — approving an exception, issuing a refund, merging a change. Nothing irreversible happens on an agent’s word alone.
How does a project work?
In four steps. Sketch: one working session to map the work, what must never go wrong and what done looks like. Harness: we build the agent with its tools, permissions, tests and checkpoints. Prove: every task leaves evidence you can check, such as a log, a diff or a receipt. Hand over: runbooks, monitoring and training so your team runs it.
Do the agents run on our own systems?
Yes. Agents run inside the tools you already use, with your permissions and your approvals. The demo on this page is a scripted illustration of that flow.
Who is behind Agentasy?
Agentasy is led by Arthur Lai, who has spent nearly two decades shipping production systems — telecom, certified payment platforms, POS and vision AI — and leading cross-functional teams. Agentasy is based in Taipei and works with teams worldwide.
How do we start?
Describe the work you would hand off in the contact form on this page. We reply within one working day with how we would build it.
What would you hand off tomorrow?
Write it down in a sentence or two. We reply within one working day with how we would build it.
Prefer email? Write to hello@agentasy.ai.