Mfinite KloudGoverned Agents
Agents that do real work in your tools, like sorting tickets, updating records or preparing reports, inside limits you set and with a log you can audit.
The problem
An agent is a model that can act: call an API, change a record, send a message. That is useful, and it is also a new kind of access to your systems.
Most agent failures aren’t dramatic. A token with too much access updates the wrong record, or a loop runs up a bill overnight. The fix is the same as for any new staff account: narrow permissions, a second pair of eyes on risky steps, and records.
What we build
- An agent for one defined job, with a written list of what it may and may not do.
- Tool access through scoped service accounts and per-tool allow-lists, often exposed as MCP servers.
- Approval steps for risky actions, in the tool your team already uses.
- A full action log: every tool call, its inputs and its result, tied to the request that started it.
- Limits on steps, time and spend, with a clean stop when one is hit.
- Evals that replay real tasks and check the agent picks the right tools and stops when it should.
Reference architecture
What you own at the end
- Agent code and prompts, in your repository
- A permission map: which tool, which account, which actions
- Approval and escalation rules, written down
- Test scenarios and a runbook for pausing or rolling back an agent
When it’s not the right fit
If the job is the same fixed steps every time, a plain script or workflow is cheaper and more predictable. We’ll build that instead.
Talk about Governed Agents
Tell us roughly what you’re trying to build. A few sentences is plenty. We’ll reply by email.
Start a conversation