How to capture tasks from an LLM without losing control.

An AI conversation often produces a real commitment: follow up with someone, review a document, make a decision, or move a project. The fragile part is the handoff. Copy only the task and you lose its context; let the model create active work freely and you lose control.

Capture the commitment with its reason

A useful task record carries more than a sentence. It can include the person, project, due date, source, and the brief reason the work matters. That is the context already present in the LLM conversation and most likely to disappear when you switch tools.

Propose first; activate only after review

An agent should not quietly fill your day because it inferred that a suggestion sounded actionable. The safer contract is explicit: capture only when the user asks, create a proposal, and let the user accept or dismiss it before it becomes an active commitment.

Automation should shorten the handoff, not remove the owner's judgement.

Make retries harmless

LLMs, automations, and networks retry. A reliable capture endpoint deduplicates repeated requests, scopes every item to one account, rate-limits abuse, and provides a credential that can submit work without reading the rest of the account.

Keep tasks and documents distinct

A task belongs in a review flow. A long research note or Markdown document belongs in a private library. Treating both as the same object produces noisy task lists and misleading knowledge records.

Frey follows this review-first pattern. An MCP-compatible LLM can call its task capture tool, and another agent can use a personal capture key over HTTP. The commitment arrives proposed, duplicates are suppressed, and the owner decides whether it enters the day.