# Keep people in charge of the agents you already run

> Your AI agents are already working in production, and the question now is what they did and who allowed it. Our engineers add a view of what's running, an approval step before the actions that need a person, and one record of every run. It sits over the agents you already have, on any framework, so your team can pause one agent without switching everything off.

Canonical: https://www.techfabric.com/solutions/govern-your-agents

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## One agent is a feature. Six is an operation.

The questions change at about the third agent, and nothing built for the first one answers them.

### Nobody knows what is running

Somebody asks what the agents are doing now, and the answer is assembled from three log searches and an impression.

### Approval happened somewhere

An action went out. Which person allowed it, on what basis, and whether they saw what you are looking at now, is not recorded in one place.

### The only lever is off

Something is going wrong and the choice is between watching it and stopping the service, because nothing between those was built.

Agent frameworks ship a runtime and assume somebody else builds the console, so the operational half arrives when somebody is already worried.

## What we install

### Missions

Work framed as something with a goal and a boundary, rather than a stream of individual runs nobody can group.

### Approval tickets

An action needing a person becomes a ticket with the agent's context attached, so the decision is made on what it saw.

### A runs ledger

Every run, what it was given, what it did, who allowed it. One queryable record rather than four log searches.

### Levers short of off

Pause a mission, redirect one agent, revoke a grant. The space between doing nothing and stopping everything.

### Delegated authority

Who may approve what, scoped, so oversight is not one person becoming the bottleneck for every agent in the company.

### Policy the agent cannot argue with

The gates are evaluated by the platform rather than requested in a prompt, which is the difference between a rule and a suggestion.

## Built when our own count passed the threshold

Tower exists because reading output stopped being supervision once we had several agents working at once. It is free and open source and it runs in your environment.

## Questions

### Do our agents have to be built on your framework?

No. Tower supervises agents rather than replacing the runtime under them. The tighter integration is with Harness because we built both, and it is not a condition.

### Is this a compliance product?

It is an operations product that happens to produce what compliance asks for. Building it the other way around gets you a report nobody uses and no levers when something goes wrong.

### How much can it actually stop?

As much as you give it authority over. The point of the delegation model is that the answer is a decision you make rather than one the tool makes for you.