NLSOM

Orchestrates multiple specialized AI agents as a 'society of mind' where agents are defined, coordinated, and communicate through natural language specifications rather than explicit code. Each agent maintains its own role, context, and decision-making logic, with a central coordinator parsing natural language instructions to route tasks, aggregate responses, and manage inter-agent dependencies. The system uses LLM-based interpretation of agent capabilities and constraints to dynamically compose agent teams for complex reasoning tasks.

Analyzes incoming tasks and automatically infers which agent roles from a society are best suited to handle them by matching task semantics against natural language agent descriptions. Uses LLM-based semantic similarity and constraint satisfaction to select and compose agent subsets without explicit routing rules. The system maintains a registry of agent capabilities expressed in natural language and performs real-time matching to determine optimal agent participation.

Decomposes complex natural language tasks into subtasks that can be distributed across specialized agents in the society. Uses LLM-based reasoning to identify task dependencies, parallelizable components, and required agent capabilities. The system generates a task graph with explicit dependencies and assigns each subtask to agents capable of handling it, enabling coordinated execution across the agent society.

Collects responses from multiple agents working on the same or related subtasks and synthesizes them into a coherent final answer through consensus-based reasoning. Uses LLM-based analysis to identify agreement, resolve conflicts, and weight responses based on agent expertise and confidence. The system can apply voting mechanisms, confidence-weighted averaging, or hierarchical aggregation depending on task requirements.

Maintains and manages context for each agent within the society, including conversation history, task state, and learned information from previous interactions. Implements context windowing and selective memory retrieval to keep agent context within token limits while preserving relevant historical information. Agents can access shared context (visible to all agents) and private context (agent-specific), enabling both collaboration and specialization.

Allows defining agent behaviors, constraints, and instructions entirely through natural language specifications rather than code. Agents interpret their role descriptions and constraints at runtime, adapting their behavior based on task context and society dynamics. The system uses LLM-based instruction following to implement agent behaviors without requiring explicit code for each agent variant.

Enables agents to communicate with each other through a message-passing system that routes messages based on natural language specifications of communication patterns. Agents can send messages to specific agents, broadcast to all agents, or send to agents matching certain role descriptions. The system handles message queuing, ordering, and delivery semantics without requiring explicit routing configuration.

Coordinates reasoning across the entire agent society to enable emergent behaviors that arise from agent interactions rather than being explicitly programmed. Implements mechanisms for agents to influence each other's reasoning, share insights, and collectively solve problems that no single agent could solve alone. The system monitors agent interactions and reasoning patterns to identify and amplify beneficial emergent behaviors.

Allows defining entire agent societies through natural language configuration files or descriptions rather than code. Specifies agent roles, relationships, communication patterns, and coordination rules in plain English. The system parses these descriptions and instantiates the agent society at runtime, enabling rapid prototyping and modification of agent societies without code changes.