AI agents
AI Agents Still Need Human Review
A useful AI agent needs a narrow job, allowed inputs, allowed tools, approval rules, logs, fallback paths, and a clear definition of success.
Fast takeaways
- Give the agent a job before giving it tools.
- Separate drafting, routing, updating, and deciding.
- Human review is a design feature, not a weakness.
An agent without boundaries is not a system.
Most AI agent ideas start too broadly: answer customers, update tools, handle leads, write reports, and make decisions. That sounds powerful, but it is hard to test and risky to trust.
A better agent starts with one job. For example: summarize an intake, draft a follow-up, classify a support message, find a missing document, or prepare a daily operator view.
Scope the inputs, actions, and approval points.
A scoped AI agent plan should define what the agent can read, what tools it can call, what it can draft, what it can update directly, what requires approval, and what happens when confidence is low.
That keeps sensitive actions behind human review and makes the system easier to debug when something goes wrong.
Logs make improvement possible.
Logs are not just compliance furniture. They show what the agent saw, what it produced, who approved it, what changed, and where the process got stuck.
That history is how a business improves the workflow without pretending the agent is perfect on day one.