How to build decision replay for text AI agents

Text-native personal AI agents need a lightweight way to explain their actions after they draft, send, defer, or escalate a message. Decision replay gives the user a short record of evidence, choice, skipped paths, approval state, and memory updates.

Build the replay layer See text assistant workflows

A replay card should fit inside the same channel as the agent.

For text agents, the replay must be short enough to read on a phone and structured enough for the agent to reuse on the next run.

Start with the action that needs explaining.

A text AI agent usually acts in small but consequential ways: drafting a reply, sending a reminder, asking for approval, summarizing a decision, or pausing a workflow. The replay should attach to that action, not to the entire conversation. That keeps the record precise and easy to scan.

Super's text message AI assistant pattern is a useful reference because the same channel can carry the request, approval, and replay. The replay should feel like a compact receipt, not a separate dashboard the user must remember to open.

Keep the replay human-scale.

The default card should show five fields and let detailed logs stay behind the scenes.

Evidence

  • Relevant prior messages
  • Source links
  • Calendar or task context
  • Tool results

Choice

  • Drafted reply
  • Sent message
  • Deferred task
  • Escalated approval

Memory

  • One-time decision
  • Reusable preference
  • Blocked assumption
  • Next safe checkpoint

Use logs as the source, not the product.

The audit log should store full events, but the user-facing replay should only show the decisive evidence. This avoids burying a phone user in raw tool calls.

Use cached context for recovery.

When a text agent resumes browser or computer-use work, replay should link back to the cached task context. Super's computer-use cache use case points to the kind of resumable state that makes replay useful.

Build the replay in five steps.

The implementation can stay simple as long as the replay object has a consistent schema and every field is tied to an action.

Define replay triggers

Create a replay when the agent sends a message, asks for approval, changes a task state, resumes after a pause, or chooses not to act. Avoid generating replay cards for every internal thought or minor classification.

Capture decisive evidence

Store the few messages, sources, or tool results that mattered. If the agent is building or editing a website, link the replay to the relevant brief and output, similar to Super's AI agent website-building workflow.

Record skipped paths

Show what the agent did not do: no reply sent, approval requested, unsafe option rejected, or incomplete source ignored. This is often the trust-building part of the replay.

Attach approval state

Make clear whether the user approved, edited, rejected, or ignored the action. If the approval should become future policy, mark the scope explicitly.

Write the memory update

End the replay with what the agent will remember. A one-off instruction should not silently become a permanent preference.

Why text agents need this first: messaging is intimate, fast, and easy to misunderstand. If an AI agent drafts or sends a message, the user needs to know which context shaped the reply. A replay card provides that without forcing the user into a full audit trail.

The most important design choice is restraint. A replay should answer: what did the agent use, what did it decide, what did it avoid, and what changes next time? Anything else can live in expandable details or logs.

What to avoid: do not label replay as model reasoning, do not expose raw hidden traces, and do not create a wall of timestamps. The value is not more text. The value is a structured artifact that makes delegated work inspectable.

Over time, replay records become a training surface for agent policy. Repeated approvals can become automation, repeated rejections can become guardrails, and repeated follow-up questions can improve evidence capture.

Implementation checklist

  • Attach replay to actions. Generate replay for meaningful user-visible decisions, not every internal step.
  • Limit the default fields. Evidence, choice, skipped paths, approval state, and memory update are enough for the first view.
  • Link back to source data. Every replay should trace to the underlying messages, sources, or logs.
  • Make memory scope explicit. Separate one-time approvals from reusable preferences.
  • Review replay patterns weekly. Use them to reduce unnecessary approvals and improve agent policies.

Sources and reference points

FAQ

Should replay appear after every text?

No. Show replay after consequential actions, approvals, escalations, or resumed work. Too many replay cards become noise.

Does replay expose chain-of-thought?

No. It should show user-facing evidence, selected action, skipped paths, approval state, and memory scope without exposing private model reasoning.

Where should detailed logs live?

Detailed logs should remain available behind the replay for debugging, compliance, and security review.

How does replay improve automation?

Replay records reveal which decisions users repeatedly approve, reject, or edit, making it easier to tune future agent policy.

Give text agents a replayable memory.

A good replay layer turns each important text-agent action into a short, inspectable record the user can trust and the agent can learn from.

Explore SuperOpen text assistant use cases