A Revolutionary Queen-Drone AI Swarm Architecture with Constitutional Governance
Version 2.1.0 | QUEEN CompliantTraditional AI agents struggle with complex, multi-faceted tasks. They lack specialization, waste API calls, and often produce inconsistent results.
The Wild Research Framework solves this through a sophisticated Queen-Drone architecture where a master AI orchestrates a swarm of dynamically specialized agents, each equipped with precisely the right LoRA adapter for their specific micro-task.
Powered by a flagship LLM. Decomposes complex objectives into micro-tasks, manages state, and synthesizes final outputs.
RESTful API • Dynamic task batching • Docker containerization • Firestore persistence
Facilitates Queen-to-Drone and inter-Drone communication for complex workflows.
A single, high-level goal is submitted through the RESTful API. The system's security layer performs input sanitization to prevent prompt injection attacks.
The Queen agent analyzes the objective and creates a detailed execution plan, breaking it into dozens of micro-tasks with clear dependencies and required specializations.
For high-stakes tasks, the system can be configured to require human approval of the Queen's plan. This provides a critical checkpoint for cost control and safety before execution.
The orchestrator intelligently groups related tasks, dynamically spawns specialized drone agents, and manages parallel execution for maximum efficiency.
Each drone focuses on its task, working within strict context boundaries. The orchestrator provides advanced error handling and recovery, retrying or rerouting failed tasks to ensure process integrity.
Structured, audited outputs flow back to the Queen, which validates, integrates, and synthesizes them into a cohesive, production-ready deliverable.
The system operates under 20 immutable constitutional laws that ensure consistency and reliability.
Enhanced Constitutional Guardrails: As the system's autonomy grows, we are enhancing these laws with programmatic guardrails. This isn't just a list of principles; it's an active governance layer where dedicated QA Drones can validate the outputs of others, ensuring compliance and preventing agent drift. This is crucial for maintaining trust and safety in a fully autonomous system.
Input Sanitization & Prompt Security: All incoming objectives are processed through a security layer to neutralize potential prompt injection attacks, safeguarding the system's operational integrity from malicious inputs.
Detailed Auditing & Logging: Every action—from the Queen's initial plan to each drone's final output—is meticulously logged. This creates a transparent and immutable audit trail, crucial for debugging, performance analysis, and ensuring accountability.
To validate the architecture, I challenged the system with a complex task in a domain I knew nothing about: "Build a comprehensive, expert-level diving guide for the Caribbean."
The Result: A production-ready, professionally written encyclopedia covering marine biology, conservation efforts, diving sites, and safety protocols. The content was validated by an experienced diver and deployed directly to GitHub Pages—all from a single prompt.
LoRA adapters are loaded on-demand, creating hyper-specialized agents for each task, then discarded to free resources.
Related tasks are dynamically grouped, reducing redundant API calls by up to 70% and improving overall efficiency.
Each drone operates within strict context boundaries, ensuring focused, high-quality outputs without scope creep.
A planned SDK will empower developers to rapidly create, train, and register new drones, fostering a rich ecosystem of custom specializations.
We are architecting for a future migration to Kubernetes to enable dynamic, auto-scaling of drone instances for enterprise-level workloads.
Let's discuss how the Wild Research Framework can transform your AI development pipeline.