Author(s): Sai Insights

Originally published on Towards AI.

I Built a Team of AI Agents That Manage Themselves — Here’s the Orchestrator Pattern Behind It

A tested, running hierarchical multi-agent system that plans a research question into subtasks, runs specialist agents in parallel, catches its own weak answers, and retries them — with every log in this article captured from a real execution

The article explains why today’s “single agent, single pass” approach struggles with complex tasks, then introduces hierarchical multi-agent orchestration as a fix: an orchestrator decomposes a problem into independent subtasks, worker agents execute those parts in parallel, and a critic evaluates confidence and triggers retries for weak results. It outlines the core building blocks (task decomposition, orchestrator–worker pattern, a shared blackboard for coordination, and confidence-gated retry loops) and provides a mini-project that implements these components with a demo (local, no external API) and an optional live mode (Claude). The walkthrough covers the project’s architecture, data/knowledge base, and orchestration loop (plan → dispatch → review → retry → synthesize), plus concrete test cases showing both well-covered and poorly-covered questions, error handling for missing dependencies/API keys, and performance/cost trade-offs. It concludes with limitations (demo planner scope, keyword-based retrieval, small static knowledge base, no cross-subtask contradiction checking, fixed retry cap) and best practices for building robust agentic systems.

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Published via Towards AI