Self-serve observability for agentic workflows.
See exactly what your agent did, in production.
Why Acme AI
You deploy AI agents that chain multiple steps and external tools together. You need to see what actually happened, not just whether it succeeded. Traditional observability dashboards show you a pass/fail state — they don't show you the decision path, the tool calls, or where things went wrong. Acme AI gives you trace-level visibility so you can debug with confidence and ship faster.
AI platform engineers and MLOps leads use Acme AI to get immediate insight into agent behavior. No waiting for logs to aggregate. No guessing which tool call failed. You see the exact execution path, the inputs, and the outputs at every step.
How it works
Integrate the Acme AI SDK into your agent runtime. The SDK instruments your agent code and starts emitting structured traces on every execution. There's no sampling at the start — you get full visibility from the first request.
Each agent run becomes a trace. Traces capture every tool call, LLM invocation, and branching decision. You can replay an execution end-to-end in the UI and see exactly what happened in sequence.
When something breaks, the UI highlights the failure point with full context. You don't dig through logs — you see the error, the input that caused it, and the surrounding steps in one view.
Query traces by agent type, status, or custom metadata. Build dashboards for the metrics that matter to your team. Acme AI stores traces with efficient compression so you retain historical data without exploding costs.
Proof
- 12ms median trace overhead
- SOC 2 Type II certified
- Used by 3 of the top-10 AI labs
Get started
Add the SDK to your agent code and start collecting traces in minutes. When you're ready to explore the data, the primary CTA lets you dive in immediately. If you'd rather see a guided walkthrough first, the secondary CTA connects you with the team for a demo.