DocumentationPricing GitHub Discord

Queen Bee

Stop hardcoding brittle workflows. Define your outcomes and let the Queen Bee architect, deploy, and evolve your autonomous agent swarm.

5x Faster

Time-to-Agent

Recursive Depth

0%

Manual Edge Mapping

100%

Self-Evolving Logic

Problem & Solution

The "Linear Bloat" Problem

Flowcharts are for Puppets, Not Agents.

Traditional frameworks like LangGraph force you to manually draw every edge and node. The moment a real-world edge case hits, the flowchart breaks. Aden replaces static chains with a Recursive Logic Engine that thinks in outcomes, not just steps.

Problem & Solution

Meet the Queen Bee

The Queen Bee: Goal-to-Code Synthesis.

You don't write the worker code; the Queen Bee does. By defining a Goal Object through natural language, the Queen Bee generates the node graph, connection code, and test cases. It’s TDD for the Agentic Era.

Problem & Solution

Eval-to-Evolution

Failures are the Fuel for Improvement.

When an agent fails, the Hive doesn't just log an error. It captures the semantic failure, updates the Goal Object, and triggers the Queen Bee to refactor the worker nodes. Your agents get smarter every time they hit a wall.

Module

Architect, Deploy, & Evolve Autonomous Swarms

Stop hardcoding brittle workflows. Aden uses a recursive, outcome-driven engine to synthesize agent logic from natural language goals. Build reliable workforces that self-correct and improve with every execution.

Outcome-Driven Development (ODD)

Learn more

Adaptive Worker Swarm

Learn more

Filesystem Memory & State Layer

Learn more

Join the community of developers killing the "linear era" of AI. Deploy the Hive today.

How It Works

The Execution Engine for High-Agency Swarms

The complete infrastructure to deploy, audit, and evolve your AI workforce. Move from brittle, manual chains to validated, self-healing outcomes.

Align & Synthesize

Start with a Goal Definition Session where the Queen Bee aligns with your desired outcome. The coding agent then synthesizes the entire worker graph, dynamic edges, and connection code automatically.

Execute & Observe

Deploy your swarm into a standardized container environment. Use the real-time streaming interface to monitor node-to-node communication, decision recording, and tool utilization with absolute transparency.

Detect & Evaluate

The framework automatically identifies semantic and logical failures. Every session is evaluated against your Goal Object to determine if the system achieved the outcome or requires architectural refinement.

Evolve & Redeploy

Failure data feeds back into the Queen Bee to refactor the graph. The system autonomously updates agent nodes, fixes edge-case logic, and redeploys the improved version without manual code changes.

The Execution Engine for High-Agency Swarms

The complete infrastructure to deploy, audit, and evolve your AI agent workforce. Move from brittle code to validated outcomes.