Agent Framework

A modular framework for orchestrating autonomous AI agents across complex, multi-step tasks. The system enables multiple agents, powered by large language models or specialized engines, to collaborate in real time through a structured communication protocol with built-in constraint enforcement and rule-based governance.

Architecture

Designed for flexibility and scalability, the framework provides a composable architecture where agents are assembled from reusable building blocks:

• Perception modules: Ingest and normalize inputs from diverse sources (APIs, documents, sensor data, user interactions)
• Reasoning engines: Plan and execute multi-step strategies with configurable planning horizons and fallback policies
• Tool connectors: Interface with external services through a standardized plugin system with automatic retries and circuit breaking
• Memory systems: Maintain short-term working memory and long-term knowledge stores with configurable retention policies

Each agent operates within a well-defined execution sandbox with configurable permission boundaries, ensuring that autonomous actions are always bounded by organizational policies and safety constraints.

Coordination and Communication

The communication layer implements a publish-subscribe model with typed message channels, enabling agents to coordinate on shared goals while maintaining independent execution contexts. A decentralized coordination model using a shared blackboard architecture and capability-based task claiming allows agents to advertise capabilities and autonomously claim tasks they are qualified for, with conflict resolution handled through a priority and constraint satisfaction system.

Governance and Observability

Governance rules are compiled directly into the agent's planning phase, shaping the solution space before the agent begins reasoning about actions. This "constrain and guide" model, rather than "check and block", reduces policy violations while improving task completion rates.

The framework supports seamless integration of new tools, models, and external services through a standardized plugin interface. Built-in observability provides full execution traces, decision audit logs, and real-time monitoring of agent behavior across distributed deployments. An event-sourced architecture for state management ensures deterministic state reconstruction and time-travel debugging capabilities.