How to infer values from conversation context

Back to how-to guides Use this pattern when the customer already said a value earlier in the conversation, and a later step must reuse it instead of asking again.

Concept

Inference reads what the customer already said. When a FLOW step starts to collect a field, the runtime checks the recent customer messages. If a message holds the value with enough confidence, the runtime fills the field. The step then skips the question. You opt in per field with infer: true.

Setting	Default	What it does
`infer`	`false`	Turns on inference for the field.
`infer_confidence`	`0.8`	Lowest confidence the runtime accepts.
`infer_confirm`	`true`	Asks the customer to confirm the value before it is stored. See Confirm inferred values before using them.

Inference is narrow. Know these limits before you rely on it:

It runs only in a FLOW step, once, when the step first starts to collect.
It reads the last few customer messages. It does not read assistant messages, tool results, or memory.
On a reasoning agent, infer: true only changes which fields the extractor considers. infer_confidence and infer_confirm have no effect there.

To capture a value the customer states during the current step, use silent extraction.

Minimal working example

A trip assistant asks what the customer needs. The customer often names the destination and party size in that first answer. The next step reuses them and writes them without a confirmation question. agents/trip-intake.agent.abl

AGENT: Trip_Intake

GOAL: "Collect trip details and reuse what the customer already said."

PERSONA: "You are a concise travel assistant."

EXECUTION:
  conversation_history_window: 4

FLOW:
  entry_point: ask_purpose
  steps:
    - ask_purpose
    - collect_trip
    - done

  ask_purpose:
    REASONING: false
    GATHER:
      - purpose:
          type: string
          required: true
          prompt: "What can I help you with today?"
    THEN: collect_trip

  collect_trip:
    REASONING: false
    GATHER:
      - destination:
          type: string
          required: true
          infer: true
          infer_confirm: false
          infer_confidence: 0.85
          prompt: "Where are you travelling to?"
      - party_size:
          type: number
          required: true
          infer: true
          infer_confirm: false
          prompt: "How many people are travelling?"
    THEN: done

  done:
    REASONING: false
    RESPOND: "Trip to {{destination}} for {{party_size}} is ready for {{purpose}}."
    THEN: COMPLETE

Variable sources:

purpose is asked in the first step.
destination and party_size are inferred from earlier customer messages. If the runtime cannot infer one, the step asks its prompt.

How it works

The first step collects purpose. The customer answers “A trip to Lisbon for 3 people”.
The next step starts. The runtime scans the recent customer messages, newest first. It skips messages with no letter or digit.
It runs the normal extraction on those messages for the active fields that set infer: true and have no value yet. It handles at most 3 fields in one pass, in declaration order.
It accepts a value when the value is not empty and its confidence meets infer_confidence. When the extractor gives no score, the runtime accepts the value only if it comes from a span of the customer’s text.
It stops scanning older messages once every opted-in field has a value.
It validates the accepted values like any other extracted value.
The step asks its normal prompt for every field that is still empty.

Settings that change the behavior:

EXECUTION conversation_history_window sets how many recent messages the runtime scans. The default is 2 and the maximum is 10. A value of 0 turns inference off.
A project-level inference setting can change the default confidence and confirm values, when your deployment sets one. Set the properties on the field so the behavior does not depend on a default.

Common variations

Confirm before storing

Leave infer_confirm at its default, true. The runtime then asks “I’ll assume … Does that work?” before it stores the value. See Confirm inferred values before using them.

Infer on a reasoning agent

A top-level GATHER field can set infer: true. This makes an optional field eligible for extraction and lets a model-sourced value satisfy completion. The agent does not apply a confidence limit or ask for confirmation. Pair it with prompt_mode: extract_only for a value the agent should never ask for. agents/support-router-intake.agent.abl

AGENT: Support_Router_Intake

GOAL: "Capture the customer's order number and urgency from what they say."

PERSONA: "You are a concise support assistant."

GATHER:
  order_number:
    prompt: "What is your order number?"
    type: string
    required: true
    satisfied_by: [user_input]

  urgency:
    prompt: "How urgent is this?"
    type: string
    required: false
    infer: true
    infer_confidence: 0.9
    prompt_mode: extract_only
    options: [low, normal, high]

COMPLETE:
  - WHEN: order_number IS SET
    RESPOND: "Order {{order_number}} logged with {{urgency}} urgency."

Turn inference off for one agent

Set conversation_history_window: 0 in EXECUTION. Fields with infer: true are then asked normally.

Verification

Run the validator. Expect no errors and no warnings.
In the first turn, say “I need a trip to Lisbon for 3 people”. Expect the agent not to ask for the destination or the party size.
Repeat and say only “I need a trip”. Expect the agent to ask for both.
Say a vague first message, such as “somewhere warm”. Expect the agent to ask for the destination, because the confidence is below the limit.
In the trace, open the dsl_collect event with the mode prior_turn_inference. appliedFields lists the inferred fields. pendingFields lists fields that wait for confirmation.

Production readiness checklist

Each inferred field has a prompt, because the step asks it when inference fails.
Each field sets infer_confirm on purpose.
Each infer_confidence is a number from 0 to 1.
The window covers the turns where customers state the value.
Tests cover a clear statement, a vague statement, and no statement.
Inferred values that can cause a financial or legal effect use infer_confirm: true.

Common mistakes

Mistake	Why it happens	How to avoid it
Writing `infer: True` or `infer: yes`	Other languages accept these.	Write lowercase `true`. Anything else reads as `false` without an error.
Writing `infer_confidence: high` or a value above 1	The compiler does not check the range.	Use a number from 0 to 1. A value above 1 never accepts an inference.
Setting `infer_confirm` without `infer: true`	The properties look independent.	Add `infer: true`. Without it the setting is ignored.
Expecting inference from tool results or memory	The name suggests any context.	Inference reads customer messages only. Use `SET` or a tool result for other sources.
Expecting inference on a later turn	The runtime runs it once at step entry.	Use silent extraction for values stated during the step.
Expecting a confidence limit on a reasoning agent	The properties compile there.	Use a `FLOW` step when you need the limit and the confirmation.

Troubleshooting

Symptom	Likely cause	What to check
The step asks for a value the customer already gave	The confidence is below the limit, the window is 0, or the message is older than the window.	`infer_confidence`, `conversation_history_window`, and the `dsl_collect` event.
The runtime accepts a wrong value	The limit is too low, or the earlier message was ambiguous.	Raise `infer_confidence` or use `infer_confirm: true`.
Nothing is inferred	The field is not active, already has a value, or the agent has no model client.	The field activation, the value, and the agent model setup.
Only some opted-in fields are inferred	The pass handles 3 fields at most.	The number of `infer: true` fields in the step.
A reasoning agent ignores the confidence limit	The limit applies in `FLOW` steps only.	The agent type.

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References

How to infer values from conversation context

Concept

Minimal working example

How it works

Common variations

Confirm before storing

Infer on a reasoning agent

Turn inference off for one agent

Verification

Production readiness checklist

Common mistakes

Troubleshooting

​Concept

​Minimal working example

​How it works

​Common variations

​Confirm before storing

​Infer on a reasoning agent

​Turn inference off for one agent

​Verification

​Production readiness checklist

​Common mistakes

​Troubleshooting

Concept

Minimal working example

How it works

Common variations

Confirm before storing

Infer on a reasoning agent

Turn inference off for one agent

Verification

Production readiness checklist

Common mistakes

Troubleshooting