A sophisticated recursive architecture that spawns and manages LLM-driven agents via a master-delegator model in the form of layers — much like neural network layers — with task-specific prompt tuning, execution constraints, and multi-agent recursion without human intervention.

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#Architecture

#How It Works

The system follows a recursive workflow:

• Task Analysis: The Master Agent analyzes the input and generates a comprehensive task breakdown with dependency structure
• Agent Creation: For each subtask, the Master Agent spawns a specialized Child Agent with custom, fine-tuned prompts and dependency context
• Recursive Delegation: Child Agents work on their tasks and can create their own Sub-Child Agents when further specialization is needed
• Layer Coordination: Child Agents are rearranged based on the dependency graph and coordinate with other agents while sharing context
• Hierarchical Results: Results flow back up the hierarchy for integration and final output
• Layered Synthesis: Results flow through layers of agents with shared context and are interpreted into a final output

The key innovation is that agent creation and prompt engineering happen automatically at runtime in layers, with no predefined agent structures or human-designed prompts.

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#Caveats

The system runs on a recursive layers method, building agents upon agents as needed. This can result in complex agent layer networks as shown below:

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#Citations & Resources

1. Emergence AI's 2025 Orchestrator
   Automatically creates agents and assembles multi-agent systems with minimal human intervention, continuously refining tools through recursive self-improvement.

2. ReDel's Recursive Systems (2024)
   Introduces systems where a root agent "decomposes tasks into subtasks then delegates to sub-agents" rather than using human-defined agent graphs.

3. Beyond Better's Orchestrator (2025)
   Implements "sub-agents created with specialized capabilities for token efficiency and parallel processing" through dynamic task analysis.