Can I require human approval before an AI agent acts?

Yes. Add an approval gate to any workflow step. The run waits until a reviewer approves or rejects it, and the decision is recorded in the audit trail.

What happens when an agent hits its budget limit?

Work stops before more money is spent. A run or project that reaches its budget cap pauses instead of continuing, so a loop cannot quietly drain your account.

Can I see what my agents did and who approved it?

Yes. Every action, approval and cost is written to an audit trail you can review in the app or query through the FleetQ MCP server.

Human approval and budget limits for AI agents in production

AI agents are easy to demo and hard to trust in production. The model is rarely the problem. Agents spend money with no ceiling, take actions nobody reviewed, and leave no record of why. FleetQ puts budgets, human approval and an audit trail around your agents in one place.

The three failure modes we see

Runaway costs. A retry loop or an oversized context keeps calling the model overnight, and the first sign is the invoice.
Unreviewed actions. An agent sends the email, merges the change or updates the record before anyone has looked at it.
No audit trail. When something goes wrong, nobody can say which agent did what, what it cost, or who signed it off.

How FleetQ handles it

Budget caps that pause work. Set a global budget and a cap per run. When a run or project reaches its limit, it pauses instead of spending more.
Approval gates per workflow step. Mark any step as needing a human decision. The run waits in the approvals inbox until someone approves or rejects it.
A full audit trail. Every action, approval and cost is recorded with the reasoning behind it, so you can answer "what happened?" in minutes.
Access from your own tools. Approvals and budgets are also available through the FleetQ MCP server, so you can review and approve from the client you already use.

Compared with building it yourself

You can build these controls on top of any agent framework: a cost counter around each model call, a queue that holds risky actions for review, and a log table for everything else. Each piece is small. Keeping them consistent across every agent, workflow and integration is the hard part, and it is work that does not ship features.

FleetQ gives you those controls out of the box, and you keep your own model keys. If you are weighing other tools, see how FleetQ compares to LangGraph, CrewAI and n8n.

Try it

Self-host the AGPL-3.0 Community Edition for free, or start on FleetQ Cloud.

Start free on Cloud Self-host from GitHub

FAQ

Can I require human approval before an AI agent acts?: Yes. Add an approval gate to any workflow step. The run waits until a reviewer approves or rejects it, and the decision is recorded in the audit trail.
What happens when an agent hits its budget limit?: Work stops before more money is spent. A run or project that reaches its budget cap pauses instead of continuing, so a loop cannot quietly drain your account.
Can I see what my agents did and who approved it?: Yes. Every action, approval and cost is written to an audit trail you can review in the app or query through the FleetQ MCP server.
Do I need my own LLM API keys?: Yes, you bring your own keys.