How to handle token limits in long AI support conversations

Long AI support conversations can exceed an LLM’s fixed context window, causing requests to fail or older information—often the customer’s original problem—to be removed, while continually resending lengthy histories also increases cost and response latency. Common strategies include retaining only recent turns through a sliding window, compressing earlier exchanges into rolling summaries, retrieving relevant facts from external memory, and maintaining structured fields for known details such as account IDs and prior troubleshooting steps. The recommended support approach combines structured state for essential facts, a sliding window for the active exchange, and relevance-based retrieval for previous interactions. Twilio separates live history management, which developers control in their own agent code, from Conversation Memory, which stores summaries and customer observations and retrieves relevant, conflict-resolved context through its Recall endpoint. For voice agents, prompt size also affects speaking latency, so retrieval frequency may be adjusted to balance context freshness against speed; when cases are handed to humans, AI-generated summaries can replace reviewing full transcripts.

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