II · Planning & Control FlowEmerging★

Passive Goal Creator

also known as Dialogue Goal Extractor, Goal Refinement from Prompts

Analyse the user's articulated prompts and accompanying context to derive a precise, actionable goal before any planning or tool use begins.

This pattern helps complete certain larger patterns —

  • used-byPrompt/Response Optimiser★★— At runtime, transform user inputs and model outputs into standardised, template-aligned prompts and responses against predefined constraints, so the agent and its downstream consumers see consistent shapes.

Context

A team runs an agent behind a dialogue interface — a chatbot, a coding assistant, a personal-assistant surface — where users type short, conversational prompts. Those prompts are often under-specified relative to what the agent has to do: the user says "book me a flight Thursday" and leaves the destination, the time of day, and the preferences implicit. Other relevant context (recent conversation, stored preferences, prior tasks) lives in memory but does not arrive automatically with the prompt.

Problem

If the planner reads the raw user prompt directly it inherits all of that under-specification. It then either guesses (producing confidently wrong work the user has to correct) or fails on a missing field. Pushing the clarification work into every downstream component spreads the same problem across many places. The team needs one early step that turns a thin dialogue prompt plus retrieved memory into a precise, structured goal that the planner can act on.

Forces

  • Underspecification: users rarely articulate complete context or precise constraints.
  • Efficiency: users expect quick responses, so the goal-clarification step must be cheap.
  • Reasoning uncertainty: ambiguous goal information propagates into the plan.

Example

A user types: "book me a flight Thursday". A passive goal creator pulls recent conversation (the user mentioned Tokyo last week), checks memory (the user prefers morning departures), and emits a refined goal: "book a morning flight from the user's home airport to Tokyo on the next Thursday". The planner now has something concrete to plan against, instead of the original eight-word prompt.

Diagram

Solution

Therefore:

A dedicated component receives the user's prompt via the dialogue interface, retrieves related context from memory (recent tasks, conversation history, positive/negative examples), and produces a refined goal handed to the planner. In multi-agent setups, the same component can receive goals via API from a coordinator instead of directly from a user.

What this pattern forbids. Downstream planning components must consume the refined goal, not the raw user prompt.

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

  • alternative-toProactive Goal Creator★— Anticipate the user's goal by capturing surrounding multimodal context (gestures, screen state, environment) in addition to what the user types or says.
  • complementsDisambiguation★★— Have the agent ask a clarifying question before acting on an ambiguous request.
  • complementsPlan-and-Execute★★— Plan all the steps once with a strong model, then execute each step with a cheaper model under the plan.
  • alternative-toSocratic 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