back_office_ops · saas · workflow
OpenAI Codex CLI agent loop: architecture, prompt caching, and context management
Building a production software agent loop requires managing ever-growing prompt length across many tool-call iterations, avoiding costly cache misses for inference efficiency, and preventing context window exhaustion during long conversations.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User input received
The agent takes input from the user to include in the set of textual instructions it prepares for the model known as a prompt.
Tools used
MCP serversLM Studio
Outcome
Codex achieves efficient inference through prompt caching that makes sampling linear rather than quadratic, automatic context compaction via a dedicated endpoint, and stateless request design that supports Zero Data Retention customers without sacrificing reasoning continuity.
What failed first
An early MCP tools integration introduced a bug where tools were not enumerated in a consistent order, causing expensive prompt cache misses.
Results
Cost replacedlinear rather than quadratic
Grounding & classification
Source type: technical build writeup
11 fields verified against source quotes, 5 dropped as unverifiable.
agentic workflowai agentcode generationcode diff prknowledge basebuilder submittedproduction runtime claimedworkflow describedsoftwareemployee productivitytechnical build writeupback office opsagentic task execution