I Built an Agent Loop by Hand. Then I Tried to Migrate It to the OpenAI Agents SDK
A few weeks ago I wrote a small Kubernetes operations agent without using any agent framework. It is one Python for loop that talks to an LLM, calls cluster tools through MCP (Model Context Protocol), and writes a health report. Then I read the example in the OpenAI Agents SDK repo. It wraps everything I had written by hand in a few clean abstractions: Agent , Runner , tools, tracing. The obvious…
A few weeks ago, the author crafted a small Kubernetes operations agent using a single Python for loop. This agent interacted with an LLM, utilized MCP (Model Context Protocol) to call cluster tools, and compiled a health report. Upon discovering the OpenAI Agents SDK example, the author recognized that their handcrafted agent could be migrated to the SDK, which offered clean abstractions like Agent, Runner, tools, and tracing.
To validate the feasibility of the migration, the author listed every feature of their current agent, cross-referenced it with the SDK's source code, and created a proof-of-concept script to assess the more complex scenarios. Ultimately, the migration proved feasible and even exceeded the author's initial expectations. However, preserving the guardrails required spreading the loop logic across three distinct areas.
The author also noticed a philosophical difference between their own code and the SDK's approach, which forms the crux of this post.
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