Context

Many AI features start as a single prompt box. That is useful for demos, but it is not enough for workflows that need multiple steps, tool calls, and human review. This work captures a product pattern for an AI workflow console: a place where operators can see what the system is doing, what context it is using, which tools were called, and where a task can be retried or rolled back.

Product Goals

• Make long-running AI tasks visible instead of opaque.
• Separate user intent, retrieved context, tool output, and final response.
• Give humans clear review points before important actions.
• Track latency, cost, and error states per workflow step.
• Keep failed tasks inspectable instead of turning them into generic error messages.

Engineering Shape

The system is organized around workflow runs. Each run has a durable goal, step records, tool-call records, model metadata, and a compact audit trail. The frontend focuses on status, timeline, and review actions. The backend owns orchestration, retries, permissions, and persistence. The AI layer receives only the context required for the next step.

Decisions

• Use structured step state instead of free-form logs.
• Keep tool contracts strict so failures can be attributed to schema, permission, upstream, or model behavior.
• Store enough trace data for debugging while avoiding sensitive prompt leakage.
• Prefer explicit review actions for irreversible operations.

What This Shows

This project represents the kind of AI engineering I care about: not just calling a model, but building the surrounding system that makes the model useful, inspectable, and safe to operate.