... 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. Developers trying Traccia

... with the cheapest model. Maybe it's the one you can actually control. How are you controlling agent spend today? Comment from Vijay Poudel(@vijaypoudel1): @mateo_rossi  exactly the problem that we are solving with Traccia. You can enforce policies around the maximum number of retries, tool calls and many more. The agent run gets killed in real time on violation Comment from Aadil Ghani(@aadilghani): The case I'd test is two agents sharing one budget. Each ... ... pass a 'remaining spend' check before either charge is recorded, then together exceed the cap. I'd reserve budget before dispatch and reconcile it after the call. Does Traccia

... refund that should have been $100. Would you know why it happened? Would you be able to stop it? Would you have the evidence to understand exactly what the agent did? That s the problem we re building Traccia for. Not just observing AI agents after something goes wrong. Controlling them while they run. Trace the execution. Evaluate decisions. Enforce policies. Control risky actions. We re building Traccia

... agent after it does something wrong isn't always enough. You need to be able to define what an agent is allowed to do and enforce those boundaries while it is running. That s why I m building Traccia , a vendor-neutral AI Agent Control Plane. Our mission is simple: Help enterprises deploy agents at scale without losing sleep over what those agents might do in production. I m here to learn from people building these systems ...

... 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. Just a few people who believed enough to keep building. Seeing Traccia

... 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: Observe what ran Evaluate before you promote Control behavior at runtime Govern with audit-ready evidence Same OpenTelemetry spans. Not four disconnected products. Genuinely curious where this hurts you most today: Visibility ... ... real gate before promote) Control (observe after the fact, can't block in time) Governance (auditors want proof you don't have) Drop your stack and your pain point. LangSmith, Langfuse, homegrown, nothing yet. All useful. We launched Traccia

... 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. In Traccia

... 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. If a policy says this agent cannot invoke that ... ... tool , the goal is to block the action before it happens , not discover it later in a trace. Agents are becoming increasingly autonomous. Shouldn t their permissions be equally enforceable? Comment from Aditya Kumar Saroj(@aditya_kumar_saroj): At Traccia

... 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. That s why we think agent policies need to include cost and execution - runtime policy enforcement : Limit tool calls per run Restrict which models can be called Set execution/time limits Stop the run when it crosses a defined boundary ...