AI agent control plane

Agent experiments become hard to track when prompts, runs and outputs live in separate places.

Agent experiments become hard to operate when prompts, runs, outputs and review notes live in separate conversations. This project frames those pieces as an internal operations surface with traceable jobs.

We created a control layer where tasks, agent context and run status sit in one real-time interface.

The control plane gives the team a Vue and Convex workspace for defining agent jobs, storing task context, watching run status and reviewing outputs. It is designed around operational visibility rather than chat alone.

Technical foundation: Vue 3, Convex, Agent runners. This matters not as a logo list, but as the set of choices that keeps data, state, user actions and future maintenance manageable.

Vue 3 provides the operations surface and Convex stores live state. Agent runners remain outside the control plane, behind a clear job and status model.

In real use, “AI agent control plane” works as a clear sequence: it starts from the original problem, then the user takes the primary action, follows a clear data path and reaches the result. The experience stays logical instead of being a random set of screens.

The practical value shows where manual work used to be needed: part of the process is automated, responsibilities are clearly separated, and each module does one understandable job. That is what keeps the solution easy to maintain and extend.

The team can see what is running, what needs review and which agent workflows are reusable.

The team can see what agents are doing, which results need review and which workflows are worth keeping. Agent work becomes easier to supervise and repeat.