Whiz is designed to provide a robust foundation for developing intelligent agents that can perform complex conversational and task-oriented workflows. Using Whiz, developers define "tools"—Python functions or external APIs—that the agent can invoke when processing user queries. A built-in memory module captures and retrieves conversation context, enabling coherent multi-turn interactions. A dynamic planning engine decomposes goals into actionable steps, while a flexible interface allows injecting custom policies, tool registries, and memory backends. Whiz supports embedding-based semantic search to fetch relevant documents, logging for auditability, and asynchronous execution for scaling. Fully open-source, Whiz can be deployed anywhere Python runs, enabling rapid prototyping of customer support bots, data analysis assistants, or specialized domain agents with minimal boilerplate.

AgentFence provides a unified security layer for AI agents, covering policy enforcement, access control, and anomaly detection. It offers SDKs for Python, Node.js, and REST APIs to easily integrate with your LLM applications. With real-time monitoring dashboards and detailed audit trails, compliance teams gain full visibility into agent behavior. Customizable policies let you define allowed actions, data use rules, and user roles. Automated alerts notify stakeholders of policy violations, while historical logs support forensic analysis and regulatory reporting.