The control plane for AI systems
AI agents are becoming operators.
They don't just generate outputs.
They access systems, use tools, make decisions, and take actions.
Traccia is the control plane that makes those actions observable, evaluable, governable, and enforceable.
They call tools. Read and write data. Invoke models. Delegate work to other agents. Make decisions on behalf of users and businesses.
That changes what organizations need from AI infrastructure.
Observability can tell you what an agent did.
Evaluation can tell you how well it performed.
Security can tell you who has access.
But none of those, on their own, answers the most important question:
Should this agent be allowed to do this, right now?
Traccia is building the control plane for AI systems.
We give organizations the infrastructure to discover, identify, observe, evaluate, govern, and control AI agents in production—regardless of which framework they use or where they run.
From observation to control
The first generation of AI infrastructure focused on visibility.
Teams needed traces, logs, token usage, latency, and debugging tools. Traccia provides those foundations through an OpenTelemetry-native SDK that works across modern agent and LLM frameworks.
But visibility is only the beginning.
As agents gain access to tools, sensitive data, APIs, and business systems, organizations need to understand not only what happened, but what the agent was allowed to do—and whether it should have been allowed to do it at all.
That is where Traccia's control plane begins.
A control plane built around the agent
Traccia brings together the capabilities required to operate AI agents responsibly:
Know which agent is acting, for whom, in which environment, and with what authority.
Understand the complete trajectory of an agent's work—from model calls and tool use to multi-step workflows.
Capture the context needed to reconstruct what happened, including traces, tool calls, state, outputs, and governance evidence.
Measure quality, correctness, risk, regressions, and outcomes before and after deployment.
Define what agents are permitted to do using centrally managed, versioned policies.
Enforce those decisions where actions actually happen—at the execution boundary.
The important distinction is simple:
Policies make decisions. Enforcement makes those decisions real.
Built for the reality of agentic systems
AI systems aren't static services.
An agent may choose a different tool tomorrow.
Delegate to another agent.
Retrieve different context.
Use a different model.
Take a different path toward the same goal.
Traccia is designed around that reality.
Our control plane is framework-agnostic and built to work across first-party agents, multi-agent systems, and third-party agent runtimes. The platform architecture explicitly accounts for agent registries, dynamic identity, policy engines, tool/MCP gateways, evaluation, cost governance, evidence, and multiple enforcement boundaries.
Open by design
Traccia starts with an OpenTelemetry-native SDK.
Instrument your agents once. Keep using the observability stack you already have. Send telemetry to Traccia or to your own OTLP-compatible infrastructure.
Then add the control-plane capabilities when you need them.
This lets Traccia complement—and make agent-aware—the systems enterprises already depend on rather than forcing a rip-and-replace.
Our belief
We believe autonomous AI will become part of the operational fabric of every modern enterprise.
But autonomy without accountability does not scale.
The infrastructure around AI agents must answer, continuously:
Who is acting?
What are they doing?
What evidence exists?
How should the behavior be evaluated?
What is allowed?
And what happens when it isn't?
That is the infrastructure we are building.
Traccia is the control plane for AI systems.