| # ⚙️ NeuralAI Agentic Orchestrator (v7.0 Prototype) |
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| 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. |
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| ## 🏗️ Orchestration Architecture |
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| ### 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. |
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| - **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"). |
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| ### 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. |
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| ## 🛠️ 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. |
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| ## 🚦 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. |
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