When a text-first personal AI agent gets an unclear reply, missed approval, failed tool result, or human correction, recovery prompts help it resume without losing the thread.
Text workflows fail differently from browser or batch jobs. The agent may have a partial reply, a delayed approval, a changed instruction, or a handoff result that needs to be folded back into the next message.
Capture the last safe state, the message that changed the task, the action that should be avoided, and the next proposed step. The agent should not search the entire thread and guess.
Ask a narrower question and preserve the draft instead of inventing intent.
Save the pending action and explain whether the agent will retry, downgrade, or stop.
Turn the correction into the next message constraint before continuing.
Trigger recovery when approval expires, a reply contradicts the plan, a tool fails, a human takes over, or the user says the answer was wrong.
Include the original ask, the pending action, the latest user message, and any tool or handoff result that changes the plan.
The prompt should explicitly avoid the failed action, stale assumption, unsupported claim, or old approval window.
Draft a short text that asks for the missing detail, confirms the revised plan, or reports that the agent stopped safely.
Store whether the recovery message resolved the task, created a handoff, or should update future routing rules.
"A recovery prompt should help the user understand why the agent changed course, not just help the model continue."Implementation note, text agent operations desk
Text agents often depend on browser context, publishing checks, or human handoffs. The recovery prompt should carry that context back into the message thread.
Use Supers' text message AI assistant pattern as the user-facing surface.
Pull safe browser state from computer-use cache when a task stalls.
Reference failed assets, links, or deploy checks from agent website workflows.
Summarize what the person changed before the agent resumes texting.
Should recovery prompts be hidden from the user?
No. The user-facing text can be concise, but the agent should make course changes understandable.
What is the first trigger to implement?
Start with missed approvals because they are common in text workflows and easy to measure.
Where does Supers fit?
Supers is relevant for personal AI agents spanning messaging, browser automation, publishing, and human escalation.
Text AI agents need recovery prompts that are both machine-usable and user-legible. The prompt should preserve context, avoid repeated mistakes, and make the next text feel grounded.
Start with missed approvals, unclear replies, and human corrections. Then connect recovery prompts to browser, publishing, and handoff workflows.