AgentRPC

AgentRPC is a universal Remote Procedure Call (RPC) layer that bridges AI assistants with backend services regardless of language, network topology, or deployment environment. It tackles the common friction where an AI model needs to invoke a function that lives behind a private VPC, inside a Kubernetes cluster, or across multiple cloud providers. By exposing every registered function through a single, standards‑compliant endpoint, AgentRPC removes the need for custom networking or gateway configurations.

At its core, AgentRPC wraps any callable function—whether written in TypeScript, Go, Python, or .NET—in a lightweight wrapper that speaks the Model Context Protocol (MCP) and OpenAI‑compatible tool definitions. Once a function is registered via the SDK, the AgentRPC platform automatically provisions an MCP server that external AI models can reach. The server handles serialization, authentication, and routing to the appropriate backend service, all while keeping the original function’s semantics intact. This means developers can continue to write code in their preferred language and framework, then expose it to AI assistants with a single line of SDK configuration.

Key capabilities include multi‑language support, private network integration (no open ports required), and long‑running function execution via long polling. AgentRPC also offers full observability—tracing, metrics, and event logs—to give teams end‑to‑end visibility into every tool invocation. Automatic failover logic inspects health checks and retries failed calls, ensuring high availability without manual intervention. Because it natively supports MCP and OpenAI SDK‑compatible agents, integration into existing AI workflows is straightforward: any assistant that understands MCP can discover and call AgentRPC‑exposed tools as if they were native commands.

Real‑world use cases abound. In a fintech environment, an AI assistant could query a risk assessment service running inside a secure VPC without exposing that service to the public internet. In an e‑commerce setting, product recommendation logic deployed in a Kubernetes cluster can be invoked by a conversational AI to personalize user interactions. Multi‑cloud orchestration is also simplified: the same AgentRPC instance can expose services spread across AWS, Azure, and GCP, letting AI agents navigate a hybrid infrastructure seamlessly.

What sets AgentRPC apart is its combination of network‑agnostic exposure and developer‑friendly tooling. By abstracting away the intricacies of cross‑border RPC, it empowers teams to focus on building intelligent assistants rather than plumbing. The result is a scalable, secure, and observable bridge that turns any backend function into an AI‑ready tool with minimal effort.