When an agent fails on long-running or larger tasks, the fix is usually rearranging what is in its context, not making it reason better. A summarizer alone treats one stage of a five-stage lifecycle and often trades reliability for a lower token count without 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 log you truncate when it fills up.
\nHow much context you keep is a reliability parameter: one trimming study found budgets at or below 25% raised failure odds nearly 11-fold versus 50% or more.
"},{"heading":"Builder model","html":"Treat context as a working set, not a transcript. Five decisions shape it:
\nSkipping straight to step 5 is the common mistake.
"},{"heading":"Mechanism","html":"Cost grows quadratically. Each turn re-sends or attends over the accumulated history, so per-turn cost keeps rising and total session cost grows with the square of turn count.
\nSummarization is lossy with a cliff. Collapsing history flattens cost to linear, but each pass can drop specific numbers, exact facts, and where a fact came from. Performance holds until too much is gone, then falls sharply. The fix is compaction checked against what must be preserved, and that check only works if architecture, ingestion, and scope already constrain what the compactor may lose.
\nHarness choices look like capability limits. OpenAI's ARC-AGI-3 write-up shows a stock harness discarding the model's private reasoning after every move, so the model restarted cold each turn. Retaining reasoning across turns and summarizing instead of hard-truncating took GPT-5.6 Sol from 13.3% to 38.3% with about 6x fewer output tokens (vendor-reported). Cold restarts and hard truncation are exactly the unmanaged patterns the lifecycle view predicts will underperform.
\nCutting the right tokens beats cutting the most. In the protocol-preserving trimming study, recency, relevance, and summarization strategies saved about 60% of tokens but reached only 66.6-77.3% task success. Trimming that preserves state the interaction protocol depends on reached 92.2%; adding an adaptive budget floor reached 96.0% while still saving 56%. The budget below which failures spike rises with workflow complexity, so a floor tuned on simple workflows under-provisions complex ones.
"},{"heading":"How to apply","html":"context-compaction-safety — compaction breaking safety constraints instead of recall.