Use Case: AI Agent Runtime

Problem

Modern AI agents are long-lived loops: a planner, some tools, some memory, occasional human-in-the-loop. Operators need to know:

• is the agent alive,
• is it stuck (no progress in N minutes),
• which tools are wired up,
• can I pause it,
• can I send it a one-off instruction,
• what's in its audit trail.

Bare scripts don't answer those questions. Heavy platforms are overkill.

How Capsule helps

Wrap the runtime as a Capsule Service:

• Health: alive + last-progress timestamp.
• Configs: which model, which toolset, current goal.
• Actions: pause, resume, cancelTask, injectInstruction, dumpMemory.
• Audit: every operator intervention is recorded.

Typical Architecture

[ AI Agent Runtime ]
   ├─ Planner / loop
   ├─ Tools
   ├─ Memory
   └─ Agent (outbound to Opstage)

What Opstage can show

• Online status and last progress timestamp.
• Current goal / task summary (whatever the runtime chooses to report).
• Recent actions and their results.

What Opstage can do

• Pause and resume the loop with confirmation.
• Inject a one-off instruction (via an action with a freeform string field).
• Cancel a stuck task; the agent reports the cancellation result back.

CE scope

• Online status and last-progress timestamp.
• Current goal / task summary, surfaced from whatever the runtime chooses to report.
• Operator actions: pause, resume, cancelTask, injectInstruction, dumpMemory.
• Audit of every operator intervention.

Future EE / Cloud enhancements

• Approval workflows for high-impact actions (e.g. injectInstruction requires a second approver).
• Cross-runtime rollups for fleets of agents.
• Hosted Cloud Opstage so runtimes deployed across customer environments share one console.

Next steps

• Action Model
• Health Reporting