Keep the Harness, Own Your Inference

Claude Code without Claude. Codex without an OpenAI model.

We’ll put both harnesses to work on a real codebase with open-weight models running on a DGX Spark. They’ll inspect the repository, change code, and test their work. The terminal experience stays familiar. The entire stack runs on hardware you control.

Drawing on our work building vLLM Agentic API, we’ll show how we’re bringing familiar coding harnesses to inference engines and distributed serving stacks including vLLM, SGLang, NVIDIA Dynamo, and llm-d. The goal is to make the harness, model, and serving infrastructure independent choices.

With Codex Cloud, the coding harness itself runs remotely. Behind that harness, the Responses API adds another layer of agentic execution: the server can manage conversations, execute tools, and retrieve context. We’ll open up that machinery and explore how responsibility gets divided between the harness and the agentic server.

Drawing on our work building vLLM Agentic API, we’ll dig into conversation state, memory through retrieval and file search, and compaction: what the agent remembers, how it finds what it needs, and what survives when its context fills up. As more of the agent runs on servers, owning your inference means understanding—and controlling—this execution stack.

You’ll leave with the setup and verification steps to run your own coding agent backend—from a DGX Spark on your desk to a shared GPU cluster. Choose the model, choose the engine, and scale the infrastructure while keeping the coding tools you already use.

Key takeaways

• Run it locally: connect Codex and Claude Code to open-weight inference on an NVIDIA DGX Spark.
• Take it to a cluster: connect the same agent-facing API layer to distributed serving with llm-d or NVIDIA Dynamo.
• Make the whole session work: preserve conversation history, tool handoffs, and streaming across backends, then verify the result through a completed coding task.