When an agent starts failing on long-running or larger tasks, the fix is usually not "make it reason better" — it's rearranging what's in its context. Reaching for a summarizer alone treats one stage of a five-stage lifecycle problem and leaves the other four unmanaged, which is why "just summarize the history" so often trades reliability for a lower token count instead of fixing the root cause.
"},{"heading":"Short answer","html":"Production agent failures mostly come from unmanaged context, not weak reasoning. Treat context as something you architect, ingest, scope, anticipate, and compact — not a single log you truncate when it gets too big. Naive accumulation grows token cost quadratically with turns; only a compaction step that's validated against what must survive achieves linear cost without paying for it in accuracy.
"},{"heading":"Builder model","html":"Think of an agent's context like a working set, not a transcript. You don't just delete things when it gets full — you decide upfront what data structure holds it (a flat log vs. a structured store), what's allowed to enter and in what form, what's scoped as relevant to the current step versus the whole session, what's proactively prefetched before it's needed, and finally how it gets compacted without losing the provenance of the facts you kept. Those are the five primitives: architecting, ingesting, scoping, anticipating, and compacting/consolidating. Skipping straight to the last one is the common mistake.
"},{"heading":"Mechanism","html":"Each new agent turn re-sends (or attends over) the accumulated history, so a session that naively accumulates context grows its total token cost quadratically with turn count — cost per turn keeps rising as the history keeps growing. Periodic summarization collapses that history into something shorter, flattening the curve back to linear, but every summarization pass is lossy: specific numbers, exact facts, and the provenance of where a fact came from can silently disappear. Performance holds while the loss stays below some threshold, then drops sharply once too much detail is gone — an "accuracy cliff" rather than a graceful degradation. The paper's claim is that only compaction validated against what needs to be preserved before it's committed gets you linear cost without the cliff, and that validation only works if the other four stages (deciding structure, admission, and scope ahead of time) already constrain what the compactor is allowed to lose.
"},{"heading":"Evidence","html":"The reference implementation built around this five-stage lifecycle (Maximem Synap) scores 92% on LongMemEval and 93.2% on LoCoMo, offered as evidence that lifecycle-managed context beats flat-history-plus-summarization baselines on long-memory recall. The authors are explicit that this recall win doesn't by itself certify production readiness: existing memory benchmarks, including the ones just cited, don't measure latency efficiency, token efficiency, or context-rot resistance, so a high recall score can still hide an expensive or slow pipeline.
"},{"heading":"How to apply","html":"context-compaction-safety — the sibling case where compaction breaks a long-running agent's safety constraints rather than its factual recall.