Compaction: distill your agent's context before it hits 100% and cut tokens 84%

Given everything above about token burn, this is the most immediately useful thing you can do this week. Anthropic's engineering team documented compaction: when a context window nears its ceiling, you high-fidelity-summarize it so the agent continues with minimal performance loss instead of either truncating or paying for an enormous window. On a 100-round web search task, compaction dropped token consumption by about 84%. (Anthropic Engineering)

Eighty-four percent. On long-running tasks. That's not a tuning tweak, that's the difference between a viable product and one that loses money on every run.

The key detail that's easy to miss: trigger compaction before you hit 100% capacity, not after. If you wait until the window is full, you've already paid for all those tokens and you're forced into a lossy compression under pressure. Budget the compaction to fire earlier, while you still have room to do a clean summary. Pair it with structured note-taking so the agent reloads only distilled state rather than re-reading its entire history every turn.

I've been doing a crude version of this by hand on my own agent loops, dumping intermediate state to a notes file and clearing the working context. The 84% number tells me I've been leaving a lot on the table by not formalizing it. The principle generalizes beyond Claude: any long-horizon agent loop accumulates context that's mostly dead weight after a few rounds. The early decisions matter, the intermediate reasoning mostly doesn't, and you're paying full price to keep re-reading all of it.

This is the practical answer to the OpenRouter problem. Agents burn tokens because their context grows unbounded over a long task. Compaction caps that growth. If you build agents and you're not doing this, it's the highest-leverage cost change available to you right now, and it costs you an afternoon of implementation.