Traccia is a vendor-neutral AI Agent Control Plane built for teams running autonomous agents in production. Observe agent behavior, evaluate performance, govern actions with policies and runtime controls, and maintain an auditable trail of what happened. Built with an open, developer-first SDK and OpenTelemetry, Traccia works across models, frameworks, and existing observability stacks—so teams can control their agents without being locked into a single AI vendor.
We’ve been building Traccia because we kept seeing the same gap with AI agents: once an agent can call tools, make decisions and take actions, a traditional trace can tell you what happened — but not whether that action was acceptable.
With traditional software, an execution usually follows a path defined by the developer. Agents are different. They can reason, choose tools, change their path and take actions we didn’t explicitly define.
That creates a new infrastructure problem for teams deploying agents in production:
What did the agent do? Why did it do it? Was it allowed to? Which policy and permissions applied? And can we prove what happened afterwards?
Traccia is our AI Agent Control Plane — a vendor-neutral layer to observe what agents do, evaluate how they behave, govern what they’re allowed to do, and audit what happened.
We’ve open-sourced the Traccia SDK and built it developer-first, with OpenTelemetry at the foundation. It works across models and agent frameworks, so teams can add observability and governance without being locked into a single AI vendor. We’re still early, and we’re building this alongside developers and teams actually deploying agents.
I’d especially love feedback from people running agents in production:
What are you using today to debug agent behaviour? How are you evaluating agents? And more importantly — how do you control what an agent is allowed to do?
We’re also making it easier to try Traccia during the launch.
Congrats on the launch. Open sourcing the SDK is a lot to give away for a control plane, good to see.
The Tsunami of AI agents will overhelm the industry. Every organization and team wants to build AI agents. Yet, once the initial excitement fades and these agents are operating at scale, difficult questions will emerge
Was my agent actually allowed to do that?
Why did it make that decision?
Could I have prevented that action?
How do I stop a rogue execution before it causes harm?
Traccia as an AI agent control plane ensures you have the right visibility and control over what you agents can do, what they can access, terminate calls on policy violation and many more.
Trying Traccia today? Platform is open - use coupon TRACCIAPH for 3 months free
Congratulations on the launch! It sounds like a really useful project based on the description. I’ll show it to our team - we’re currently using a similar solution...
Did there occur any specific cases with AI agents that you could label as anomaly within their process?
Having one place to manage AI agents across different platforms is huge, especially now that everyone's using a mix of frameworks. Really solid build!
Hey Product Hunt 👋 We’re live.
I’m one of the makers of Traccia. If you’ve ever watched an agent take a tool call you didn’t expect and then scrolled a 2,000-span trace trying to answer “was that even allowed?” - that’s the pain that started this.
Full-fidelity traces across models/agent frameworks
Eval path: prompts → datasets → scorers → experiments before you promote
Runtime governance: policies + evidence so “observe” isn’t the end of the story
Who this is for Teams putting agents in production - not demos. If your stack already tells you what happened, but not whether it should have happened, you’re our ICP.
One ask If you run agents in prod, comment with your current stack for:
debugging a bad tool call
deciding promote vs rollback
blocking an action at runtime
Even “we use X and it’s fine/it sucks because Y” helps more than a silent upvote.
Trying Traccia today? Platform is open - use coupon TRACCIAPH for 3 months free. I’ll be in the comments all day and will answer everything personally.
“Finally, a vendor-neutral AI Agent Control Plane.”
Traccia launched on Product Hunt on August 27th, 2026 and earned 185 upvotes and 12 comments, placing #6 on the daily leaderboard. Traccia is a vendor-neutral AI Agent Control Plane built for teams running autonomous agents in production. Observe agent behavior, evaluate performance, govern actions with policies and runtime controls, and maintain an auditable trail of what happened. Built with an open, developer-first SDK and OpenTelemetry, Traccia works across models, frameworks, and existing observability stacks—so teams can control their agents without being locked into a single AI vendor.
Traccia was featured in Open Source (68.8k followers), SaaS (43.9k followers), Developer Tools (518.3k followers), Artificial Intelligence (477.1k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 294.5k products, making this a competitive space to launch in.
Who hunted Traccia?
Traccia was hunted by Vijay Poudel. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Traccia stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Vijay Poudel
We’ve been building Traccia because we kept seeing the same gap with AI agents: once an agent can call tools, make decisions and take actions, a traditional trace can tell you what happened — but not whether that action was acceptable.
With traditional software, an execution usually follows a path defined by the developer. Agents are different. They can reason, choose tools, change their path and take actions we didn’t explicitly define.
That creates a new infrastructure problem for teams deploying agents in production:
What did the agent do? Why did it do it? Was it allowed to? Which policy and permissions applied? And can we prove what happened afterwards?
Traccia is our AI Agent Control Plane — a vendor-neutral layer to observe what agents do, evaluate how they behave, govern what they’re allowed to do, and audit what happened.
We’ve open-sourced the Traccia SDK and built it developer-first, with OpenTelemetry at the foundation. It works across models and agent frameworks, so teams can add observability and governance without being locked into a single AI vendor. We’re still early, and we’re building this alongside developers and teams actually deploying agents.
I’d especially love feedback from people running agents in production:
What are you using today to debug agent behaviour?
How are you evaluating agents?
And more importantly — how do you control what an agent is allowed to do?
We’re also making it easier to try Traccia during the launch.
3 months free with coupon code: TRACCIAPH