Traccia 是供应商中立的 AI 智能体控制平面，帮助团队在生产环境中观察、评估和治理自主智能体，确保合规与可审计性。
热门评论
PH 用户
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
PH 用户
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.
What ships todayOpen-source SDK (Python + Node), OpenTelemetry-nativeFull-fidelity traces across models/agent frameworksEval path: prompts → datasets → scorers → experiments before you promoteRuntime governance: policies + evidence so “observe” isn’t the end of the storyWho 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 calldeciding promote vs rollbackblocking an action at runtimeEven “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.
https://traccia.ai
- Aditya (and the Traccia team)
PH 用户
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 emergeWas 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
PH 用户
Congrats on the launch. Open sourcing the SDK is a lot to give away for a control plane, good to see.
PH 用户
Did there occur any specific cases with AI agents that you could label as anomaly within their process?
PH 用户
能在一个地方管理不同平台上的 AI 智能体，这太重要了，尤其是现在大家都在混用各种框架。做得真扎实！
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
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.
What ships todayOpen-source SDK (Python + Node), OpenTelemetry-nativeFull-fidelity traces across models/agent frameworksEval path: prompts → datasets → scorers → experiments before you promoteRuntime governance: policies + evidence so “observe” isn’t the end of the storyWho 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 calldeciding promote vs rollbackblocking an action at runtimeEven “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.
https://traccia.ai
- Aditya (and the Traccia team)
Trying Traccia today? Platform is open - use coupon TRACCIAPH for 3 months free