Your tools can tell you what your AI agent did. TrustLoop decides what it's allowed to do — before it does it.
Your agent has permission to send emails, move money, and delete files. It does these things on its own, thousands of times a day, without a human watching.
So here's the question worth sitting with: when your agent does the wrong one — refunds a customer £40,000 instead of £40, emails the wrong contract to the wrong client, deletes the wrong table — what happens next?
With most setups, the honest answer is: you find out later. You go read the logs. You piece together what the agent decided and why. And by then the email has been sent, the money has moved, and the table is gone.
That gap — between watching a problem happen and preventing it — is the whole reason TrustLoop exists.
The market has a naming problem, and it's costing teams real money.
Observability tells you what an agent did. Platforms like Langfuse, Helicone, Weights & Biases, Arize and Braintrust are genuinely good at this. They trace every step, log every tool call, score outputs, and help you debug why a run went sideways. If you're shipping agents, you want one of these. They do their job well.
But their job is to help you understand your agent. A trace lets you watch a problem happen in high resolution. It doesn't stop the problem. It's a camera, not a lock.
Governance decides what an agent can do. That's a different layer, and it runs at a different moment — at runtime, before the action, not in the dashboard afterward. Governance is the thing that catches the £40,000 refund on the way out and holds it for a human. It's the kill switch you can hit mid-incident. It's the rule that says a certain agent may read from your database but never delete from it.
You want both. But you should never assume one gives you the other — and most teams discover the gap the expensive way, in production.
| Observability | Governance (TrustLoop) | |
|---|---|---|
| Core question | What did the agent do? | What is the agent allowed to do? |
| When it acts | After the action, in the dashboard | At runtime, before the action executes |
| On a bad action | Records it in the trace | Blocks it, or holds it for human approval |
| In an incident | You read logs to reconstruct events | You hit the kill switch and stop it live |
| The artifact | A trace you inspect | A tamper-evident record you can prove |
Neither column is "better." They're answers to different questions. The mistake is buying a camera when what you needed was a lock — and only finding out which one you had after something went wrong.
TrustLoop is a drop-in proxy. You point your base URL at us — no code change — and every tool call your agent makes passes through a layer that can see it, score it, and stop it.
This isn't a rip-and-replace. Keep Langfuse or Helicone for tracing and evals — that's still the right tool for understanding your agents. Add TrustLoop as the control layer that decides what those agents are permitted to do.
Observability watches. TrustLoop governs. Together they cover both halves of running agents in production: seeing clearly, and staying in control.
You may have read that the EU AI Act's high-risk deadline forces you to act by August 2026. That specific deadline moved: under the Digital Omnibus, high-risk obligations for standalone systems were deferred to December 2027 (embedded systems to August 2028). Transparency obligations remain live from August 2026.
We'd rather tell you that straight than sell you on an expired deadline. The reason to govern your agents isn't a date on a calendar — it's that autonomous systems taking real-world actions need real-world controls, and that's true today whether or not a regulator is looking. When compliance requirements do bite, the audit trail and human-oversight controls you'll need are the ones TrustLoop already produces.
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