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Context engineering: what to keep, what to fold, what to drop

Every long agent run ends the same way: the window fills, and something gets evicted. The interesting question is who decides what gets evicted. The harness, with a fixed truncation rule. The model, deciding for itself. Or nobody, in which case the context rots quietly and the agent starts contradicting decisions it made an hour ago.

The position I keep landing on after reading this corner of the literature: context is not a log, it is a workspace. Agents that treat it as a log lose. Agents that treat it as something they can actively sculpt, fold, prune, rebuild, last much longer.

The three families

Current work splits into three families:

  1. Procedural compaction. Fixed rules applied by the harness: summarize on threshold, drop the oldest turns. Cheap, predictable, and lossy in ways nobody audits. This is what most shipping harnesses do, including the one I am running this session in.
  2. Agentic compression. The model decides when to consolidate and what to prune, with the harness providing the operations (a knowledge block, a prune tool); the trade-off moves into the agent.
  3. Workspace reconstruction. Stop maintaining history as a continuous stream. Periodically rebuild a compact working state from what matters, and let the old trajectory die.

One paper per family. Where this sits in my failure taxonomy: I file compaction as a type-4 concern in the agent taxonomy. The policy can be right and the run still fails, because the memory of the run breaks silently. It also compounds with run length, which is the subject of my long-horizon agents notes.

Active Context Compression: the agent as its own compactor

Active Context Compression: Autonomous Memory Management in LLM Agents (arXiv 2601.07190, January 2026) proposes Focus, an agent architecture where the model decides when to consolidate key learnings into a persistent Knowledge block and when to withdraw raw interaction history. The paper's stated inspiration is Physarum polycephalum, slime mold, which grows wide and then retracts the branches that did not pay off.

The mechanics matter more than the metaphor. The scaffold gives the agent two operations: consolidate, folding learnings into the Knowledge block, and withdraw, pruning raw history. With aggressive prompting, Focus cut tokens by 22.7% across five context-intensive SWE-bench Lite instances (14.9M down to 11.5M tokens) while keeping accuracy identical (3/5 for both agents), averaging six autonomous compressions per task and saving up to 57% on individual instances, all on Claude Haiku 4.5.

Caveat first: N=5 is a demo, not a benchmark. But the design claim survives the small N. Capable models can self-regulate their context when the harness gives them the operations and a place to put what they learn. The Knowledge block is the load-bearing idea. Compression without a home for the compressed knowledge is just deletion.

AgentFold: folding at the right granularity

AgentFold: Long-Horizon Web Agents with Proactive Context Management (arXiv 2510.24699, October 2025) attacks the same problem from the summarization side. Its complaint about fixed full-history summarization: irreversible loss of critical details. Its answer is a learned folding operation that manages history at multiple scales, either granular condensations that preserve fine-grained detail or deep consolidations that abstract away entire multi-step sub-tasks.

This is the right axis. Naive compaction fails because it folds everything at the same granularity: the file path you still need gets summarized as lovingly as the error you already recovered from. AgentFold learns when to fold at what scale, and the results are striking for a 30B model: 36.2% on BrowseComp and 47.3% on BrowseComp-ZH from simple supervised fine-tuning, surpassing OpenAI's o4-mini and matching or beating DeepSeek-V3.1-671B-A37B. No RL, no continual pretraining. Folding turns out to be a learnable skill.

IterResearch: stop preserving, start reconstructing

IterResearch: Rethinking Long-Horizon Agents with Interaction Scaling (arXiv 2511.07327, November 2025) makes the most radical move: give up on the continuous history entirely. The paper was first posted as "Rethinking Long-Horizon Agents via Markovian State Reconstruction" and is now camera-ready at ICLR 2026 under the interaction-scaling title. It reformulates deep research as an MDP where the state is a strategic workspace rebuilt periodically. An evolving report serves as memory; periodic synthesis resets the interaction context.

The Markovian point is that the agent does not need the trajectory, it needs a state carrying everything the trajectory implied. The interaction-scaling result is the cleanest demonstration of the payoff: on the paper's deep-research suite, performance climbs from 3.5% to 42.5% as runs extend to 2048 interactions, exactly where mono-contextual agents suffocate. On that suite, a reconstructing agent holds flat up to 2048 interactions.

Where compaction loses state that matters

Across all three, the same losses show up. This is the audit checklist I would run on any compaction design:

  1. Reversed decisions. The discarded tail contains the path not taken. When the agent later needs to know why a file was deleted, a summary of "explored X" is useless. The decision record is gone, and compaction rots into contradiction.
  2. Early constraints. Instructions from the first turns ("don't touch prod", "keep the tests green") are cheap to state and easy to summarize away. They are the most expensive things to rediscover, because the agent only learns they mattered after violating them.
  3. Evidence versus verdicts. A summary says "found a bug in config.py". The agent may still need the actual error output to act on that verdict; folded prose loses the referent.

The pattern: compaction systems preserve conclusions and reliably lose the objects those conclusions point at.

What I would build

Three rules, drawn directly from these papers:

  1. The agent folds, the harness protects. Let the model decide when to consolidate and what to prune, the Focus lesson. But keep an invariant ring the model cannot fold: initial constraints, verification commands, anything with side effects. AgentFold shows granularity is a choice; make it a constrained one. In my own system the ring is already concrete: at PiPlan.ai the append-only event log is the unfoldable artifact. Folds can reshape the view the model works from, but every accepted change stays in the log as an event, and no compaction decision is allowed to rewrite it.
  2. Fold into structure, not prose. Compressed knowledge lands in a structured store, the event log, a key-value state, a report, not in a summary paragraph. IterResearch's report-as-memory is the pattern: the fold target is queryable, not merely readable.
  3. Audit the folds. Record every compaction decision: what was folded, when, at what granularity, by whom. A fold log turns "the context got weird" from a mystery into a diff. If you can replay a run's compactions, you can debug its rot; if you cannot, you are debugging blind.
raw history ──► fold ──► structured store (queryable)
     │
     └──► prune ──► gone, but the fold log says why

The throughline: long-horizon agents do not fail because they run out of context. They fail because nobody decided what the context is for. Give the model the operations, protect the invariants, record the deletions, and the run lasts a lot longer before something has to give.

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