How would you manage conversation state in an AI application?

Separates durable application state from the model's bounded request context while preserving ownership, concurrency, and deletion.

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TL;DR

The application owns the conversation, participants, messages, tool outcomes, and business state in a durable store. For each turn it builds a bounded model context from the relevant history, current policy, and trusted state. The provider's conversation feature can simplify transport, but it does not replace application authorization, retention, or an auditable record.

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State ownership, context reconstruction, concurrency, privacy, and lifecycle management.

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Core answer
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Overview
A conversation identifier is not permission to send an entire history back to a model forever.
The application owns the conversation, participants, messages, tool outcomes, and business state in a durable store. For each turn it builds a bounded model context from the relevant history, current policy, and trusted state. The provider's conversation feature can simplify transport, but it does not replace application authorization, retention, or an auditable record.
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Stronger answer
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Long histories are trimmed, summarized, or compacted with source links so important facts are not silently rewritten. I separate user messages, generated messages, verified facts, pending actions, and summaries because they have different authority. Optimistic concurrency or turn sequencing prevents two simultaneous messages from producing conflicting state updates.

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Details

Sending the full message history on every turn.
Treating generated summaries as authoritative facts.
Ignoring concurrent turns in the same conversation.

How would you manage conversation state in an AI application?

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