OpenAI’s Codex has introduced a new wave of AI-assisted development, enabling engineers to turn natural language into functional code. But while Codex can generate good solutions, it still depends on high-quality inputs to be effective. That’s where StepChange’s APX comes in.
10x Faster Production Debugging Starts with APX
Let’s say you’re dealing with a production performance issue. APX detects the problem and surfaces it with a complete breakdown: the root cause, affected components, and a step-by-step resolution plan. No log-diving, no guesswork.
What makes this workflow even more powerful? With a single click of “Copy for LLM”, APX formats and transfers all of this context directly into Codex. This includes the impacted endpoint, slow SQL query, optimizations and potential speed boost along with all the other information needed to implement it.
Paste it into OpenAI Codex, and you get high quality code output - not just generic suggestions. It’s like pair programming with an AI that understands your system.
Without APX:
- Engineers manually piece together context from scattered logs, alerts, traces.
- Inputs to Codex are often ambiguous or incomplete, leading to weak outputs.
- Debugging becomes iterative, slow, and frustrating.
With APX:
- APX generates well-structured, LLM-ready contexts instantly.
- Codex returns high-quality fixes based on complete and accurate input.
- Engineers move from alert to resolution in a fraction of the time.
A Purpose-Built Workflow for AI Engineers
Codex is a leap forward, but it works best when integrated into a system designed for it. APX was built to close the loop by detecting issues, generating LLM-optimized context, and guiding AI agents like Codex toward fast, reliable solutions.
The synergy between APX and Codex doesn’t just reduce toil it transforms how engineering teams respond to incidents, build reliability, and reclaim time for innovation.
Try it for yourself: http://tryapx.com