II · Planning & Control FlowMature★★

Disambiguation

also known as Clarifying Questions, Confirmation Loop, Ask About Ambiguity

Have the agent ask a clarifying question before acting on an ambiguous request.

This pattern helps complete certain larger patterns —

  • specialisesHuman-in-the-Loop★★— Require explicit human approval at defined points before the agent performs an action.

Context

A team is building an agent that takes free-form user requests and acts on them — moving a calendar event, editing a file, sending a message. Real user requests are often underspecified or refer to entities the agent cannot uniquely resolve from context. The deployment is interactive enough that the agent can ask a follow-up question before doing anything irreversible.

Problem

An agent that always acts will silently pick one interpretation when several are plausible, and confidently do the wrong thing — moving the wrong meeting, editing the wrong file, replying to the wrong thread. Rolling back the wrong action is usually more expensive than asking a single clarifying question would have been. But asking on every request quickly becomes annoying and trains the user to ignore prompts, so the agent has to detect when it is actually uncertain instead of asking by default.

Forces

  • Asking too often is annoying.
  • Asking too rarely produces wrong work.
  • The model must detect ambiguity, which is itself hard.

Example

A scheduling assistant gets the message 'move my meeting with Sam to Tuesday'. There are three Sams and two Tuesdays in scope. An always-act agent picks one and silently moves the wrong meeting. The team adds Disambiguation: when the resolver returns multiple candidates with similar likelihood, the agent asks 'which Sam — Sam Patel from Finance or Sam Chen from Design?' before touching the calendar. One short question prevents an embarrassing rollback.

Diagram

Solution

Therefore:

Detect ambiguity via low-confidence intent classification or explicit ambiguity rubric. When detected, ask one focused question and wait for the answer before acting. Phrase the question with the most-likely interpretation as a default.

What this pattern forbids. Below the confidence threshold the agent must ask; it is forbidden to guess.

The smaller patterns that complete this one —

  • usesRouting★★— Classify an incoming request and dispatch it to the specialist (lane / agent / model) best suited to handle it.
  • generalisesCommunicative Dehallucination★— When an instructed agent would have to invent missing context to comply, have it reverse roles and ask the instructor for the missing detail before answering.

And the patterns that stand alongside it, or against it —

  • complementsConfidence Reporting★— Surface the agent's uncertainty about its answer alongside the answer itself.
  • complementsEcho Recognition·— Recognize human message repetition as emphasis or a re-ask rather than as an independent input, so the agent does not produce a near-duplicate reply when the human repeats themselves.
  • complementsPassive Goal Creator★— Analyse the user's articulated prompts and accompanying context to derive a precise, actionable goal before any planning or tool use begins.
  • complementsSocratic Questioning Agent★— Drive the agent toward its goal by asking the user a sequence of strategic, open-ended questions that surface the user's own latent knowledge, goal, or context — rather than producing an answer directly.

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References

Provenance