智能代理编排
Software that decides the next step for itself, instead of following a fixed script.
Most software waits to be told. You open it, you click, it does the thing you clicked. An agent is different: you give it a goal and the tools to reach it, and it works out the steps itself — reading what came in, deciding what to do, doing it, checking whether that worked, and trying something else if it did not.
Orchestration is the layer above that. One agent handling one job is useful; five agents sharing your systems without tripping over each other needs someone deciding who does what, in what order, and what happens when one of them fails. That coordination is where most homemade attempts fall apart.
The part that matters commercially is not the cleverness. It is the control: which decisions the agent is allowed to make alone, which ones stop and wait for a person, and how you see what it did. Built without those, an agent is a liability. Built with them, it is a colleague who never forgets a step.
Someone opens the orders inbox at 8am. Forty messages, three formats, two in Malay. They read each one, look up the customer, check stock, and type it into the system. Two and a half hours, every morning, and mistakes happen around order thirty.
The agent has already read all forty overnight. Thirty-six are drafted and waiting for a one-click approval. Four are flagged: an unknown customer, an item out of stock, an ambiguous quantity, a price that does not match the list. Your person spends twenty minutes on the four that need a brain.
How we actually build it
-
01
We write down the goal
Not "use AI on orders" — "read every order that arrives by email or WhatsApp and draft it into the system for approval". If we cannot write it plainly, it is not ready to build.
-
02
We give it exactly the tools it needs
Access to the inbox, the price list, the order system. Nothing more. Narrow permissions are the difference between a useful agent and an incident.
-
03
We decide where a human stands
Usually: it drafts, you approve. Once you have watched it be right a few hundred times, you can widen what it does alone.
-
04
We make it observable
Every decision logged in plain language, so when something looks wrong you can see what it thought and why.
- The work needs judgement — "which supplier, given this stock level and that lead time?"
- Inputs arrive in messy, inconsistent formats
- The steps change depending on what the previous step found
- The process is identical every single time — a plain automation is cheaper and more predictable
- A mistake would be expensive and nobody will be reviewing the output
- You cannot describe the goal clearly. If you cannot, neither can the agent
- Access to the systems it will touch
- Someone who can say what "correct" looks like
- A couple of hundred past examples to test against
2–3 weeks to a working prototype; 4–8 weeks to something running unattended.
What people ask about this
What happens when it gets something wrong?
It flags rather than guesses, and everything it does is logged and reversible. In the first weeks nothing is submitted without your approval — you widen its authority as you build confidence, not before.
Does it need to connect to my accounting system?
Only if the job requires it. Where there is an API we use it; where there is not, we drive the screen instead — see RPA & Selenium.
Often paired with
Would this work for you?
The first call is free, and we will say plainly if this is the wrong tool for your problem.
预约免费通话