Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks through multi-agent collaboration. However, existing approaches primarily focus on open-ended reasoning or simple task decomposition and lack structured frameworks for long-horizon decisionmaking with explicit planning, execution, and supervision. To address this, we propose Chain-of-Agents Planner (CoA-Planner), a framework that explicitly coordinates Planning Agents, Worker Agents, and a Supervisor Agent through a structured task graph. The Planning Agent decomposes complex tasks into a directed acyclic graph (DAG) of subtasks with dependencies,Worker Agents execute specialized subtasks, and the Supervisor Agent monitors progress and resolves conflicts at critical milestones. Our framework introduces adaptive role reconfiguration that dynamically adjusts agent assignments based on historical performance metrics. We evaluate CoA-Planner on several long-context benchmarks including document analysis (50K-120K tokens), codebase review, and technical report generation. Experimental results show that CoA-Planner achieves an average success rate of 67.9%, outperforming single-agent LLMs by 23.5 percentage points (pp), Chain-of-Thought with RAG by 17.9pp, and existing Chain-of-Agents frameworks by 10.4pp. The framework demonstrates efficient scaling with 4-5 worker agents and reduces error rates across all categories, particularly in context coverage and reasoning consistency.