Agent operations
AgentOS
Most companies bolt one chatbot onto one workflow. This is what it looks like when the whole operation runs on agents.
The problem
A single assistant is easy. The hard part is a lot of them: keeping them from contradicting each other, giving them a shared view of what is true about the business, stopping them running up a bill unsupervised, and making sure the useful thing one of them learned on Tuesday is known to all of them on Wednesday.
Without that, every agent is a fresh intern who has never seen the company before.
What we built
One shared brain
Agents read from a common knowledge base rather than each carrying its own half-remembered context. Update a fact once and every agent has it.
It learns overnight
A nightly job reads the day's logs, extracts what worked and what did not, and writes the lessons back into the context every agent loads. The system that runs on Friday is not the one that ran on Monday.
Work as a queue, not a chat
Tasks are filed, prioritised and executed against a queue with explicit caps, so work continues when nobody has a browser tab open — and stops when it should.
Reachable from chat
The agents are wired into the messaging your team already uses, so filing a task or getting an answer does not require opening yet another dashboard.
Spend has a ceiling
Every automated path is metered and capped. Budget guardrails are treated as a first-class feature, because the failure mode of autonomous work is a surprise invoice.
How it works
- Work arrives from a person, a schedule or another agent, and lands on the queue.
- The right specialist agent leases the task and executes it with its own tools.
- Results and reasoning are written back to the shared knowledge base.
- Overnight, the learning pass turns the day's logs into better instructions for tomorrow.
At a glance
Want something like this?
We build production software for other people too — this is just what we build for ourselves.
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