Kosuke Yokoyama

An AI agent runtime is the execution environment that moves an agent’s task from request to result: it runs the agent loop, coordinates model and tool calls, tracks state, and handles interruptions or failures. To choose or build one, start with the execution guarantees your workflow needs—not just

To build an MCP server, define a tool, register it with an MCP SDK, test it with an MCP client, and connect it to the application that will use it. Start with a local, read-only tool; add real data, credentials, and state-changing operations only after you have defined how access and failures will b

Tool calling lets an AI model request a named tool with structured arguments so an application can retrieve information or perform an action. The model proposes the call; trusted application code decides whether to execute it, returns the result, and controls when the process stops. Key Takeaways A

An agent-friendly API design specification tells an agent what an operation does, which inputs it accepts, what it returns, and what to do when the outcome is uncertain. Start with a valid machine-readable API contract, then make the details an agent needs to choose, execute, and recover from a call

The right AI orchestrator depends on what you need to control. Choose a workflow orchestrator for a defined process, an agent framework for model-directed decisions within a task, and a tool-access router when the main problem is connecting those applications to external services. Many teams need mo

A service API defines how software interacts with a particular service. The Model Context Protocol (MCP) defines how an AI application communicates with a server that offers tools or context. They are different layers, not mutually exclusive choices: an application can call a service API directly, c

An API timeout occurs when a caller or intermediary stops waiting because a time limit has been reached. It means a response did not arrive within that boundary—not that the remote operation necessarily failed or never ran. Before retrying a request that changes data or triggers an action, determine

An agentic workflow is a sequence of steps in which an AI agent makes some decisions about how to reach a defined outcome, while the surrounding application sets the task, checks results, and controls when work ends. The design challenge is not to make every step autonomous; it is to decide where ju

The safest way to expose an existing API to an AI agent is to put a controlled execution layer between the model and the API. The model proposes a tool call; your application validates it, checks the user’s authority, obtains the right credential, and decides whether to execute or request approval.

Building production-grade AI agents requires connecting foundational models to real-world data and action APIs, yet integrating dozens of external services quickly introduces fragile codebases, sprawling vendor contracts, and unexpected downtime. We built Relvo as an intelligent gateway and router f