Analysis
Two independent infrastructure vendors reached the same architectural conclusion: agents should write code instead of calling tools. The tooling layer is standardizing on a pattern nobody has named.
Two independent infrastructure vendors arrived at the same architectural conclusion within months of each other, and the agent tooling layer is quietly standardizing on a pattern that nobody has formally named. Bifrost, the open-source AI gateway from Maxim AI, and Cloudflare, through its Agents SDK, both replaced large MCP tool catalogs with direct code generation. The convergence is not a coincidence. It is a structural signal that the tooling layer is shifting from declarative configuration to imperative execution.
The mechanism is straightforward but significant. Traditional agent architectures rely on tool catalogs – long lists of predefined functions that an LLM can call. Each tool requires a description, a schema, and sometimes a complex chain of dependencies. As agents grow more capable, these catalogs balloon. The input tokens consumed by tool descriptions alone become a significant cost center. Both Bifrost and Cloudflare independently concluded that the solution is to let the agent write code instead of calling tools.
Bifrost’s Code Mode, released in February 2025, replaces large MCP tool catalogs with four generic tools. Instead of presenting the LLM with hundreds of specific tool definitions, Bifrost exposes a Starlark sandbox where the agent writes code directly. Starlark is a Python subset designed for safe, deterministic execution. The result is a 92.8% reduction in input tokens. The agent does not need to know about every tool; it needs to know how to write code that accomplishes the task.
Cloudflare’s Code Mode, part of the open-source Agents SDK, takes a different runtime path to the same architectural conclusion. Introduced in February 2025 and updated through October 2026, Cloudflare’s approach lets LLMs write TypeScript against a typed SDK and execute in a sandboxed Worker via the Dynamic Worker Loader API. The token reduction is approximately 81%. The agent writes code that runs in a production-grade serverless environment, not a sandboxed Python interpreter.
The convergence matters because these are not incremental improvements to existing tool catalogs. They represent a fundamental shift in how agents interact with external systems. As Anthropic’s own engineering team noted in November 2025, code execution with MCP is a natural extension of the tool-use paradigm. But Bifrost and Cloudflare went further: they made code execution the primary interface, not an optional extension.
This pattern connects directly to the infrastructure bottleneck thesis that the industry is now grappling with. The TGVP report identified four layers that need to mature: memory, execution, tooling, and governance. The tooling layer is the one that determines how efficiently agents can interact with external systems. If every agent call requires loading a full tool catalog into context, the cost and latency scale linearly with the number of available tools. Code generation breaks that dependency. The agent can access any tool through code, regardless of whether it was explicitly listed in the catalog.
For builders, the practical implication is clear: the tooling layer is standardizing on code-generation-over-tool-calling. Whether you use Bifrost’s Starlark sandbox or Cloudflare’s TypeScript Workers, the architectural direction is the same. The tool catalog as the primary interface is being replaced by a more flexible, more efficient, and more scalable paradigm. The question is no longer whether agents should write code instead of calling tools. The question is which runtime will dominate the emerging pattern.
Blair Hayes works for Forkast.
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