Memory

In distributed systems, split-brain is what happens when a partition convinces two halves of a cluster that each is the sole leader, and both start accepting writes. Multi-agent systems build the identical topology every time more than one agent reads and writes a ‘shared’ memory store with no lock, no leader, and no version check — and the failure that follows is a genuine multi-agent memory consensus problem, not a bug in any one agent. The mechanism, a concrete incident-response failure, and what to instrument before it costs you a duplicate action.

“Context engineering” is getting used as a rebrand for prompt writing, and that undersells it into uselessness. The actual discipline is deciding, every single turn, what earns a place in a bounded window and what gets evicted — and living with the fact that every eviction is a bet you’re making about the future of a run you haven’t finished yet. Here’s the physical constraint underneath the buzzword, the packing decision that’s actually hard, what it costs to get it wrong, and how to test whether your packing policy works.

When the context window fills up, the standard fix is to summarize the old turns and keep going. That summary is a lossy compression step, and the thing it silently drops is usually the one early constraint the agent needs a hundred turns later. Here’s why recency-based compaction fails, a simulation of how often the load-bearing fact survives, and the rule that actually protects it.

Treating the context window as append-only memory is how agents get slow, expensive, and quietly wrong. The fix is to run it like a cache with a budget and an eviction policy: decide what earns its tokens every turn. Here’s the cost math, the accuracy failure mode, and a working context manager.