We keep talking about AI agent costs as: model price tokens requests

But agentic systems add another cost: uncontrolled execution.

• An agent retries a tool 5 times.

• Calls an expensive model when a cheaper one would work.

• Loops through unnecessary steps. Hits APIs it didn't need.

• Delegates work to another agent.

In July 2025, Replit s AI agent deleted a production database containing data on 1,200+ executives and 1,190+ companies despite an explicit code freeze. Replit later acknowledged the incident and shipped additional safeguards.

It s a good example of why we think observing an agent after something goes wrong isn t enough.

With Traccia, you can define runtime policies around agent execution for example, which tools an agent is allowed to use, how many retries/tool calls it can make, or how long it can run.

Imagine an AI agent handling customer refunds.

It checks the order.
It calls the payment system.
It decides the refund amount.
It executes the refund.

Everything looks fine.

We re giving agents access to APIs, databases, CRMs, payments and internal systems.

But something feels missing.

We can observe what an agent did.
We can evaluate whether it was right.
We can detect a policy violation.

Everyone's shipping agents. Few teams have a clean answer for what happens after the demo works.

Tool calls go live. Budgets get fuzzy. Compliance asks for evidence you can't reconstruct from logs. Evals live in one tool, traces in another, and "governance" is still a Notion doc someone updates after an incident.

We're building Traccia around a simple loop for production agents:

We launched Traccia on Product Hunt yesterday and finished #6 Product of the Day.

But honestly, the ranking isn't what stayed with me.

It was the support from the community.

A handful of people. A lot of late nights. More doubts than we d like to admit.

And today, Traccia is live on Product Hunt.

What started as conversations and rough ideas slowly became something real through broken builds, discarded ideas, tiny wins, and a small team that just kept showing up.

We re not a big company. There s no huge team behind this.

We ve been thinking about this a lot while building Traccia.

One behavior we ve seen with MCP workflows is an agent getting into repeated tool calls without making meaningful progress. The workflow hasn t necessarily failed it can just keep going.

That got us thinking about a different kind of agent monitoring: not just detecting errors, but defining boundaries on execution.

One thing we ve been thinking about while building Traccia: an agent can get stuck making repeated tool calls, or keep switching to expensive models, without actually making meaningful progress.

And the scary part is that the run may still technically succeed.