D3X: Dependency Driven, Decentralised Execution for Scalable AI Teams

Abstract

Current agent frameworks such as OpenAI Deep Research and Manus AI rely on centralised planner–executor–verifier loops that re-append growing scratchpads at each step. While feasible for small pipelines, this pattern scales poorly: prompt load grows superlinearly (often quadratically) with the number of subtasks, and execution tends toward sequential behaviour, increasing cost and latency. We introduce a Dependency–Driven, Decentralised eXecution (D3X), a protocol that compiles a user request into a directed acyclic graph (DAG) of subtasks. Control is event-driven via a lightweight Operations Manager that activates nodes as soon as parents complete; execution is decentralised across workers that receive only dependency-local context and can run local self-review loops without blocking peers. When new information emerges, a Subtask Refiner edits only the affected subgraph, preserving progress elsewhere. An Aggregator then combines leaf artifacts into the final result. Under bounded parent summaries and dependency only routing, D3X reduces total prompt load from quadratic to near-linear in n - d (for fixed summary budget), and wall-clock time approaches the critical path with sufficient parallelism. More generally, latency follows Θ( τ[L + (n−L)/ w ]) plus low, event-driven scheduling overhead and linear-time aggregation. In our evaluation, we observe mean speed-ups up to 4.00× and input-token reductions up to ∼ 83% (37–83% across tasks)