Model Context Protocol (MCP) and Agent-to-Agent (A2A) serve different but complementary purposes in an agentic architecture.

• MCP, an open source standard, enables AI systems and agents to securely access tools, data, and functions inside enterprise applications. In the context of Aria Billie Connect, MCP allows AI models and external systems to query billing data or trigger billing actions in a governed way.
• A2A, an open protocol standard, enables agents to communicate, coordinate, and delegate tasks to other agents. This allows multi-agent workflows that span different systems, domains, or business processes.

In simple terms:

• MCP connects agents to systems.
• A2A connects agents to other agents.

Together, they enable secure access to enterprise capabilities and coordinated multi-agent execution.

While traditional APIs require custom integrations, rigid contracts, and ongoing maintenance for each system and use case, Model Context Protocol (MCP) provides a standardized, AI-native interface that lets any AI system dynamically discover, understand, and invoke available tools and data without bespoke engineering.

With MCP, capabilities are exposed in a way AI models can immediately understand — including the context, intent, and guardrails required to use them correctly. This eliminates the need for hard-coded integrations, accelerates time to value, and ensures consistent security and governance across every interaction.

Aria Billie is the UI copilot used in Aria Billing Cloud for various user functions. Aria Billie Connect is the broader connectivity and agent framework that powers it, and that enables any external AI system, agent, or platform to access Aria Billing Cloud’s data, functions, agents and tools.

Aria is designed to be plugged into enterprise wide eco-systems, including their AI systems and platforms. For this Aria Billie Connect provides native AI access.

Yes. You can build custom agents using your preferred AI platforms and have them interact with Aria Billing Cloud data, functions and tools through Model Context Protocol (MCP) or orchestrate activities with our agents using A2A.

This allows your agents to securely access billing data, execute actions, and orchestrate workflows while operating within your enterprise security and governance controls. Aria’s eventing framework can also be used to trigger your own agents for proactive and predictive revenue management.

This approach gives you the flexibility to extend agent capabilities, embed domain-specific logic, and innovate without agent dependence on Aria Systems.

Aria doesn’t train external AI Models (LLMs) with customer information for its agents.

Instead, prompt enrichment is used, where all the necessary data is retrieved (RAG), tokenised with PCI compliance and provided with the question to the LLM for processing and response. This is also done in ‘forget the question and answer’ mode so the LLM doesn’t learn from this data either.