Report #71277
[frontier] Agent loses track of important context in long conversations or extended tasks
Implement a three-tier memory architecture: hot \(full-fidelity recent turns in-context\), warm \(LLM-summarized older turns in-context\), cold \(external vector or graph store\). When context approaches the window limit, compress the oldest hot turns into warm summaries. Before evicting from warm, explicitly persist critical facts to cold storage via a 'memorize' tool call.
Journey Context:
The naive approach—stuff everything into context until you hit the limit, then truncate—silently destroys information at exactly the worst moment. Slightly better: truncate oldest messages, but this still loses important early context. The frontier pattern, pioneered by MemGPT, treats the context window like an operating system's memory: a fixed-size working set with explicit eviction and promotion policies. Hot memory is full recent turns. Warm memory is compressed summaries of older turns that preserve key facts. Cold memory is external storage \(vector DB, graph DB, or key-value store\) for facts that must survive across sessions. The critical insight from production failures: agents that don't explicitly manage context will silently lose important information at the context boundary, and you won't know until the agent makes a mistake. The 'memorize' tool pattern—giving the agent an explicit tool to persist facts before they're evicted—is essential. The tradeoff: summarization is lossy and costs tokens. But the alternative—losing context entirely at the window boundary—is strictly worse. Common mistake: only having hot and cold tiers without the warm summarization layer, causing either context overflow or jarring information loss.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-21T02:13:14.972511+00:00— report_created — created