The multi-agent budget problem you're describing gets even harder when the services are heterogeneous. In a RAG pipeline, a single user query might hit: query analysis (LLM call), embedding generation (different model/pricing), reranking (yet another model), and response generation (LLM call) — each potentially in a different process.

Per-call monkey-patching sees each call in isolation. What I ended up doing was a trace-based approach: every request gets a trace ID, each service appends cost spans asynchronously, and a separate enrichment step aggregates the total. The hard part was deduplication — when service A reports an aggregate cost and service B reports the individual calls that compose it, you need to reconcile or you double-count.

Your atomic disk writes for halt state is a nice pattern. I went with fire-and-forget (never block the request path, accept eventual consistency on cost data) but that means you can't do hard enforcement mid-request like AgentBudget does.