Run a planner and cheap executor subagent coding pipeline
The commenter uses Codex with GPT-5.5 as planner and a GPT-5.4 Mini agent as executor so cheaper models handle tasks that would otherwise cost too much. Task: Suggested implementation: A custom coding pipeline where GPT-5.5 acts as a planner to generate task breakdowns, while GPT-5.4 Mini serves as the executor for file edits and code generation. The user manually assembled this routing logic instead of using Codex's default agent flow, requiring human oversight to define the handoff between planning and execution.; tools: Codex. Human role: The original post does not say how a person stayed involved.; limitations: Prerequisites include manual workflow construction and integration of separate model endpoints, as the default tool does not support this routing natively. The source links to a GitHub project but provides no data on setup time, maintenance overhead, or error handling for failed executions. Reliability is unverified due to lack of performance metrics..
Tools
Published on Agent Work Atlas:
Overview
The commenter uses Codex with GPT-5.5 as planner and a GPT-5.4 Mini agent as executor so cheaper models handle tasks that would otherwise cost too much.
Workflow
Suggested implementation: A custom coding pipeline where GPT-5.5 acts as a planner to generate task breakdowns, while GPT-5.4 Mini serves as the executor for file edits and code generation. The user manually assembled this routing logic instead of using Codex's default agent flow, requiring human oversight to define the handoff between planning and execution.
Reported result
The claimed outcome is reduced operational costs for coding tasks by reserving the expensive model for planning only. However, no specific metrics, success rates, or cost savings figures were provided in the source. A measurable acceptance check would be comparing token usage costs per completed task against a baseline single-model workflow.
Human role
The original post does not say how a person stayed involved.
Limitations
Prerequisites include manual workflow construction and integration of separate model endpoints, as the default tool does not support this routing natively. The source links to a GitHub project but provides no data on setup time, maintenance overhead, or error handling for failed executions. Reliability is unverified due to lack of performance metrics.
Try it
I need to set up a cost-optimized coding pipeline using [replaceable: GPT-5.5] for planning and [replaceable: GPT-5.4 Mini] for execution. First, provide me with a sample project context and a specific coding task description. Step 1: Use the planner model to generate a detailed step-by-step execution plan. Step 2: Review the plan for logical errors before proceeding. Step 3: Pass the approved plan to the executor model to generate the necessary file edits or code snippets. Step 4: Output the final code changes in a unified diff format. Please perform a dry run on a small, non-critical file first. Do not apply changes to the main repository until I explicitly confirm the diff is correct. If the executor fails to produce valid code, stop and report the error instead of retrying automatically. Ensure the output includes a cost estimate comparison between this two-model approach and using the planner model for the entire task.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.