The individual or team responsible for initiating development by providing or generating source code and interacting with the platform to deploy, test, and iterate on agentic systems.
The foundational logic used to create and define Services, MCP Servers, and AI Agents, encompassing both custom-written and AI-generated implementations.
General-purpose modules such as APIs, user interfaces, backend logic, data processors, scrapers, indexers, inference wrappers, or cryptographic tools. These provide reusable, foundational functionalities utilized independently or by MCP Servers and AI Agents.
Lightweight programs that expose specific capabilities through the standardized Model Context Protocol. They act as gateways to data sources, handling authentication, data retrieval, and formatting, enabling AI applications to access external data in a standardized manner .
Autonomous, intelligent entities backed by Large Language Models (LLMs), capable of reasoning, responding to events, automating tasks (e.g., chatbots, auto-traders), and coordinating in multi-agent systems. They perform complex, intelligent, or interactive tasks, acting as the active logic or “brain” within the platform.
A controlled, secure, and isolated environment facilitating the deployment of components for iterative testing and refinement before production deployment.
A secure, isolated runtime environment that ensures secure execution and maintains state/data persistence, crucial for testing stateful applications and iterative development. It also provides diagnostic data for debugging, performance monitoring, and iterative improvements.
Diagnostic outputs from the Execution Sandbox, essential for developers to understand system behavior, identify issues, and inform iterative improvements.
