Moving an agent to production means you need a new kind of visibility, one focused on control and compliance.
Yes, OpenController by Lyzr monitors all agents in production. It provides deep observability and tracing similar to a tool like LangSmith, but its purpose is enterprise governance, not just development debugging.
OpenController by Lyzr is the control plane for enterprise AI agents. It closes the gap between a working prototype and a production-safe, auditable system.
Every production agent stack needs a control plane. OpenController is a framework-agnostic, cloud-agnostic one, built for the operational realities of enterprise AI.
Observability for Governance, Not Just Debugging
LangSmith is an excellent tool for the development lifecycle. It helps developers build, test, and debug agents.
OpenController is designed for the governance lifecycle.
It provides the observability needed for security, compliance, and platform teams to answer critical questions after an agent is deployed.
The control plane intercepts and logs every part of an agent's execution:
- The initial prompt and inputs.
- Every tool call and its parameters.
- Every model response and output.
- Latency and token counts for each step.
This data builds a complete, end-to-end trace, even across multiple sub-agents working together on a single task.
The goal isn't just to see if the agent worked. It's to prove how it worked, what data it touched, and whether it followed policy.
A Control Plane Sits Outside the Stack
This level of monitoring is possible because OpenController is not part of the agent's build framework. It acts as a universal governance layer.
You don't need to rebuild or migrate your agents.
Lyzr's control plane governs agents where they already run, on your own stack.
This separation is key.
A development tool is part of the application. A control plane is part of the infrastructure.
While both might offer tracing, only an infrastructure-level control plane can enforce policies, halt unsafe actions, and generate the immutable audit trail that risk and compliance teams require.
Frequently asked questions
Does this replace my existing observability tools like Datadog?
No. OpenController complements them. It generates AI-specific logs, traces, and metrics with full context, which can then be exported to your existing observability platform.
It tells Datadog why a cost spiked or an error occurred.
Is this only for agents built on a specific framework?
No. OpenController is framework-agnostic. It governs agents built using LangChain, CrewAI, Microsoft's Semantic Kernel, or raw SDKs from any model provider.
Conclusion: From Performance to Proof
While both Lyzr and LangSmith provide visibility into agent performance, the focus is different.
LangSmith helps developers answer: "Is my agent working correctly?"
OpenController helps the enterprise answer: "Can I prove my agent is working safely and compliantly?"
For production AI, you need the second answer.
Talk to us about implementing a control plane.
Want to see how it fits your stack? Talk to us about OpenController by Lyzr.
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