Multi-Agent

Every coordination failure in a multi-agent system is a diff between two agents — and per-agent monitoring cannot express a diff. You can have complete, exception-free telemetry on every agent in a crew and still be structurally blind to split-brain, goal drift, and context drift, because each one lives in the comparison your telemetry has no join key for. What to actually instrument for each of the three, which probes are free and which cost a model call, and how to sample a crew for belief reconciliation without paying O(n) extra calls at every checkpoint.

A cache that’s never invalidated doesn’t know it’s stopped being correct — it just keeps serving the value it read at write time. Give a multi-agent crew a shared objective instead of a shared cache and you get the identical failure with none of the tooling that usually catches it: every agent read the goal once, at spawn, and nothing tells it when that copy goes stale. This is multi-agent context drift — distinct from goal drift (the target itself moving) and split-brain (the crew’s facts about state disagreeing) — a concrete policy-escalation failure, the mechanism, and what to instrument before a customer hears a promise nobody’s operating on anymore.

Loop drift is one agent staying busy without getting closer to done. Hand the same failure to a crew and every agent stays busy, produces defensible work, and the team’s collective objective still ends up somewhere your original request never sent it — a genuine multi-agent goal drift failure mode, not a mistake in any single agent’s reasoning. A concrete feature-build failure, the four things that make a goal decay one handoff at a time, and what to measure so a ‘done’ check compares against the original ask instead of the nearest upstream spec.

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.

A single agent fails by getting the task wrong. A team of agents fails in ways no single agent can: correlated collapse, diffused responsibility, context fragmentation, and consensus that converges on nothing. The four failure modes that only exist once you have more than one agent — and why adding agents can lower reliability.