ループの大きさは何で決まるか — hook・MCP・Skill で読み解く「検証」と「reach」This article analyzes what determines the size of agent execution loops by…

Agent execution loops sit at the heart of how modern AI systems accomplish multi-step tasks, and the question of what makes those loops large or small has direct consequences for cost, reliability, and predictability. This article frames that question around three building blocks that developers increasingly reach for—hooks, the Model Context Protocol (MCP), and Skills—and argues that two properties, verification and reach, largely govern how many iterations an agent needs before it settles on a result.

An agent loop, at its simplest, is the repeated cycle in which a model proposes an action, executes it through some tool, observes the outcome, and decides whether to continue. The number of times this cycle runs is not fixed. It expands when the agent must gather more information or correct mistakes, and it contracts when the environment returns clear, trustworthy signals early. Understanding what drives that expansion or contraction is a practical concern, because longer loops consume more tokens, add latency, and introduce more opportunities for the agent to drift off course.

Hooks are a useful place to begin. In agent frameworks such as Claude Code, hooks are user-defined callbacks that fire at specific lifecycle points—before or after a tool call, for example. Their significance here is that they can enforce verification deterministically. Rather than trusting the model to check its own work, a hook can run a linter, a test suite, or a validation script and feed the result back into the loop. When verification is cheap and automated in this way, the loop tends to converge faster, because the agent receives an authoritative pass or fail signal instead of guessing.

MCP addresses reach. The Model Context Protocol, introduced by Anthropic in late 2024 and since adopted across a range of clients and servers, is an open standard for connecting models to external tools, data sources, and services through a common interface. An MCP server effectively defines how far an agent can reach: which files, APIs, databases, or systems it can touch. The article's framing suggests that reach is a double-edged factor. Broader reach lets an agent resolve a task in fewer external round trips because it has direct access to what it needs, but it also enlarges the space of possible actions, which can make loops longer and harder to bound if verification is weak.

Skills sit between the two. Anthropic's Agent Skills package instructions, scripts, and resources into reusable units that an agent can load when relevant. In loop terms, a well-designed Skill can compress several exploratory steps into a single, well-trodden path, reducing the loop's size by encoding known-good procedures. The trade-off is that a Skill's usefulness depends on how accurately the agent selects and applies it.

The central thesis, then, appears to be that loop size is best understood as the interaction between reach and verification rather than either alone. High reach with strong verification tends to produce tight, confident loops; high reach with weak verification risks sprawling, uncertain ones. Low reach forces the agent to work through intermediaries, which may lengthen the loop even when each step is well verified. Framing design decisions this way gives practitioners a heuristic: when a loop feels too large, ask whether the agent lacks the reach to act directly, or lacks the verification to know when to stop.

This perspective aligns with broader industry movements. The rapid standardization around MCP, the emergence of Skills, and the spread of hook mechanisms all reflect a shift from monolithic prompting toward composable agent infrastructure. Related efforts, including tool-use APIs from other model providers and orchestration frameworks such as LangChain or LangGraph, grapple with the same underlying tension between giving agents capability and keeping their behavior bounded.

For engineers building on these primitives, the takeaway is less about any single tool and more about balance. Deciding where to place a hook, how much reach to grant an MCP server, and which Skills to expose are all, in effect, decisions about how large you want the loop to be—and how much you trust the agent to close it.