AI agents for operations: built in your stack, approved by a named person
Agents are worth building when a task needs judgment across several steps and several systems, and not before. I build agents that work inside your Jira, Monday.com or internal tools, propose each change as a plan, and wait for a named person to approve before anything is written.
Key takeaways
- An agent is the right tool for multi-step work with exceptions. A rule is the right tool for everything simpler.
- Every agent I ship has an approval gate, an evaluation set and a log.
- The agent runs with the narrowest access its task needs, through accounts you control.
What an agent build includes
- The agent, with its tools scoped to the task.
- A plan format your approver can read in under a minute.
- Evaluation cases drawn from your real requests, with known right answers.
- A log of each plan, approval and executed step.
- A runbook and training for whoever will own it.
An agent I built and run: JAMES
JAMES is an AI Jira administrator, in beta. Asked for a configuration change, it reads the site, writes a plan with the risks and open questions, and executes only after approval. I built it because the same loop kept recurring in my Jira work, and it is the clearest example of how I think an agent should behave.
- Request
- Discovery
- Plan
- Approval
- Execution
- Log
Where do-it-yourself agents stop
Agent builders make the first version easy: connect a model, give it tools, watch it work on an example. The hard parts come after. Somebody has to decide what it may not do, prove it still answers correctly after a model update, and explain a wrong action to the person it affected. Those are engineering tasks, and the builders leave them to you.
When an agent is the wrong call
If the task is the same every time, write an automation rule. If one model call with a good prompt does it, add an AI step to a rule you already have. An agent adds cost, latency and things to supervise, and it should earn them. I say so in the audit when the simpler option wins.
This is often part of an FDE engagement. Before an agent can route tickets or answer from your knowledge base, the configuration underneath has to be right. The AI Readiness Audit finds out what needs fixing first, in two weeks, for a fixed $3,500.
Questions
Which model do the agents use?
Can the agent act without approval for low-risk steps?
How do I know it will not go wrong at scale?
Have a multi-step task in mind? The audit checks whether an agent is the right answer and scopes it.
6–12 weeks
Fixed per scoped build