Keep context across long-running work
Duale AI keeps long-running work coherent by separating stored task history from the active context sent to a model.
Duale AI keeps long-running work coherent by separating stored task history from active model context, supporting targeted recall and delegation or conversation continuation.
- Child executors maintain separate working contexts and return outcomes as tool results to the parent.
- Stored history, active context, and targeted recall serve distinct roles during long-running tasks.
- Targeted recall retrieves selected original detail after older active context has been reduced.
- Delegation handles focused work during a task; response.next() adds a turn after success.
- Deadlines bound task duration but do not preserve context.
Summaries were generated by AI. Generative AI is experimental.
Duale AI keeps long-running work coherent by separating stored task history from the active context sent to a model.
Each delegated branch starts focused
A child receives a focused instruction and maintains its own working context. The parent keeps its context and records the child’s returned outcome as a tool result. Their full transcripts remain separate.
This separation keeps each executor focused while the parent receives the result it needs.
Stored history and active context are different
Duale AI uses three related views during long work.
- Context view
- Stored history
- Purpose
- Keeps original task messages and tool results available within the task history
- Context view
- Active context
- Purpose
- Gives the current model call the bounded information it needs
- Context view
- Targeted recall
- Purpose
- Lets the model retrieve selected original detail after older active context has been reduced
The platform stores task messages and returned child outcomes before it continues the task. Later model calls can use a reduced active context and recall selected detail from stored history.
Long-running work explains reduction and targeted recall.
Stored history provides continuity. The deadline bounds the task’s duration; it does not preserve context.
Choose delegation or conversation continuation
Delegation handles focused work during a running task. response.next() adds a new turn after a successful task.
- Decision point
- When it happens
- Delegation inside a task
- While a task is running
- SDK conversation continuation
- After a task finishes successfully
- Decision point
- What it adds
- Delegation inside a task
- A child executor for focused work
- SDK conversation continuation
- A follow-up task on the existing root executor
- Decision point
- Who receives the outcome
- Delegation inside a task
- The executor that delegated the work
- SDK conversation continuation
- Your application
- Decision point
- Time boundary
- Delegation inside a task
- No later than the caller’s remaining deadline
- SDK conversation continuation
- No later than the inherited conversation deadline
Multi-turn continuation defines the SDK contract for response.next().
Executors and deadlines explains the work tree that delegation creates.
Use delegation when a running task needs focused child work. Use conversation continuation when your application needs a new turn after a successful outcome.