Would you deploy an agent you don't control?

Claude Agent SDK packages a Python client with a bundled Claude Code runtime, making it well suited to products that intentionally need a coding agent with built-in shell, filesystem, project-instruction, plugin, permission, and agent-loop behavior, but potentially less suitable for unrelated applications because those embedded choices can affect behavior beyond the public API. The discussion argues that agent prompts, tools, permissions, retries, and control loops should be treated as meaningful dependencies, since SDK upgrades can alter bundled system prompts or tool definitions even when an application’s own code, model, and prompts remain unchanged. It contrasts this approach with Pydantic AI, which lets developers assemble an agent incrementally by selecting models, instructions, typed tools, outputs, dependencies, execution environments, and optional capabilities such as coding tools, memory, planning, or subagents. Pydantic AI can run tools locally or in interchangeable sandboxes including Modal, E2B, and Sprites, while observability through Logfire and regression testing through Pydantic Evals can expose prompts, tool calls, retries, and behavior changes during upgrades. The central recommendation is to choose Claude Agent SDK when Claude Code’s prebuilt coding-agent behavior is the intended product, and to use a more composable framework when the application requires direct control over its agent runtime and capabilities.

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