Production AI agents rarely fail on reasoning alone. Once an agent moves from answering questions to running operations, every decision depends on the tools it calls: how fast they return, whether the state they report is current, and whether the agent can write a result back before the conversation moves on. A slow or stale tool layer leaves even a capable model stalling, or acting on data that changed while it was thinking.
In this 10-minute episode of Data in Charge, Adam Majcher, Senior Presales Engineer at Volt Active Data, revisits the TM Forum Catalyst LIA FieldOps project through an MCP lens. One LLM agent manages a telecom field-service operation through eight MCP servers, each mapped to a TM Forum Open Digital Architecture (ODA) component and backed by VoltDB. The live demo shows the agent reading current state, making a decision, and committing the change within a single conversational turn. Adam also explains how this tool layer fits alongside an existing streaming architecture rather than replacing it.
The session is built for telecom architects, agentic AI builders, and engineers working on MCP tooling. You’ll come away with a working blueprint for an agent backend that acts on live data instead of only describing it: scoped access through one server per ODA component, tool calls fast enough to finish inside the conversation, and transactional consistency so every call works from the same version of the truth. To go hands-on, explore real-time decisioning in the Demo Lab or connect your own agent through the AI Agent Builder path in the Developer Hub.