AI agents in your systems
Agents that read your data, use the tools you approve, and draft the next step. Anything that changes a record waits for a person.
- Acts throughTools you approve, one by one
- Changes dataOnly after a person approves
- Every stepLogged, so you can audit it
What agents are good at
Work that follows a pattern but needs judgement at each step.
- Triage
Reading incoming tickets, emails or orders, and routing each one with a reason.
- Drafting
Replies, refunds and updates prepared with the facts looked up, ready for a person to send.
- Reconciling
Comparing records across two systems and flagging what does not match.
- Form filling
Turning documents into the fields another system needs, with the uncertain ones marked.
How an agent run works
An agent is only as safe as the tools and limits around it. This is the shape of every run.
Scoped tools
Each tool does one thing, with the narrowest access that works.
Run in parallel
Independent steps happen at once, so a run takes seconds, not minutes.
Approval steps
A person approves anything that sends, pays, deletes or changes a record.
Limits per run
A cap on steps, time and spend, so a loop cannot run up a bill.
Audit log
Every tool call and decision recorded, so you can see why it acted.
When an agent is the wrong choice
Often a simpler system does the job better.
- The steps never changePlain code, no model needed
- It only answers questionsRAG on your documents
- Mistakes are costly and rareA person, with better tools
- No clear success testDefine one before building
Questions about agents
What usually comes up before an agent build.
Can an agent act without approval?
Only for actions you decide are safe to automate, and only after the eval set shows it gets them right.
Which systems can it work with?
Anything with an API or a database we can read: your CRM, help desk, ERP or internal tools.
What happens when it gets stuck?
It stops at its limit and hands the task to a person, with what it found so far.
How do we know it is working?
Task evals before launch, and a sample of real runs reviewed every week after.
See it built
A client project of this kind, anonymised under NDA: the architecture, the trade-offs and the results measured at launch.
A support assistant that knows when to say "I don't know"
A grounded support copilot with three-way routing, a feedback loop and a human in the loop.
- 42%conversations resolved without a human
- 0.6%replies with an unsupported claim
- 4.4/5customer rating on AI-handled replies