A Modular Multi-Agent Coordination Framework for Persistent Autonomous AI Assistants with Tool Orchestration and Long-Horizon Task Management

Autonomous AI assistants are evolving from reactive, single-session language models into persistent, toolintegrated systems that can execute long-horizon tasks. However, most existing assistant architectures rely on either monolithic control loops or loosely structured agent delegation patterns that lack formal coordination protocols, governance safeguards, and dependency-aware orchestration. This study presents a modular multi-agent coordination framework built on an extended OpenClaw autonomous agent substrate designed to support persistent tool-augmented AI assistants operating across heterogeneous workflows. The proposed framework introduces (1) a shared task-ledger coordination protocol, (2) a dependency-aware task graph model, (3) role-isolated specialist agents with synthesis control, and (4) governance layers that incorporate approval gating, prompt-injection defense, and security monitoring. To evaluate the framework, we designed a synthetic benchmark environment to model event-driven automation, parallel advisory councils, knowledge retrieval pipelines, and long-horizon scheduled workflows. Across controlled simulation trials, we analyzed the coordination overhead, task completion rates, conflict resolution latency, token consumption growth, and dependency-coupling sensitivity. The results indicate that structured multiagent coordination improves task throughput under medium coupling regimes while introducing measurable synchronization costs under high interdependency conditions. The findings contribute empirical clarity to the design of persistent AI assistant systems and establish a reproducible evaluation methodology for tool-augmented multiagent orchestration frameworks.