ORCHESTRATOR.md

⚙️ NeuralAI Agentic Orchestrator (v7.0 Prototype)

The Orchestrator is the "brain" of the agentic layer. It transforms NeuralAI from a reactive chatbot into a proactive operator capable of decomposing complex goals into executable sub-tasks.

🏗️ Orchestration Architecture

1. The Manager-Worker Pattern

NeuralAI operates as the Manager Agent. For complex, long-horizon, or parallelizable tasks, the Manager spawns Worker Agents via the /zo/ask API.

• Manager: Handles goal decomposition, resource allocation, synthesis of results, and final quality assurance.
• Worker: A stateless, task-specific Zo invocation optimized for a single objective (e.g., "Research Topic X", "Audit File Y", "Generate Component Z").

2. Task Decomposition Workflow

1. Goal Analysis: The Manager analyzes the user request to determine if it is "Simple" (single turn) or "Complex" (agentic).
2. Plan Generation: If complex, the Manager generates a Directed Acyclic Graph (DAG) of tasks.
3. Worker Dispatch: Workers are called in parallel or sequence using the /zo/ask API.
4. Synthesis: The Manager aggregates worker outputs, verifies them against the original goal, and presents the result.

🛠️ Implementation Tools

• /zo/ask API: The primary mechanism for spawning Workers.
• Knowledge Base: Shared context provided to Workers to ensure alignment.
• Task Registry: A log of active and completed sub-tasks to prevent redundant work.

🚦 Execution Protocols

• Parallel Execution: Use Python asyncio or run_parallel_cmds to trigger multiple worker calls.
• Verification Loop: Every worker output must be validated by the Manager before being integrated into the final response.
• Fallback: If a worker fails, the Manager attempts one retry with a refined prompt before reporting a blocker.