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Every time Codex translates a plan created by Fable into code, the context is broken. Conversational prompts alone cause structural data to become mangled. Do not pass the entire text. Instead, create a lightweight structure called ControlState. A JSON containing only the current agent state and step is sufficient.
Instead of passing the entire file, pass only the Git commit SHA and file path as a reference table. Keep analysis results in a separate, independent semantic memory layer. This will significantly reduce instances of the model hallucinating while parsing unnecessary data. You can reduce the time spent on debugging by 40%.
API costs are the biggest obstacle for solo developers. Do not resend system prompts every single time. Use prompt caching techniques.
ephemeral flag to the cache_control header.The Notion engineering team used this approach for Claude-based features, reducing response times from 11.5 seconds to 2.4 seconds. By reducing redundant token costs per call, you can immediately save at least 25% on real-time deployment costs.
Throwing error logs at a model without refinement causes hallucinations. The 2026 AI Agent Failure Analysis Report identified this as a major cause of failure. Do not look at logs manually; use filtering middleware.
This task alone will save you 5 hours every week.
When you separate planning and implementation, inference time increases, leading to frequent timeouts. Abandon synchronous processing. When you receive a build request via FastAPI, do not wait; push the task to a queue using Celery's delay() method. Return only the task_id to the client immediately.
If you separate hotfixes into a critical queue and security checks into a default queue, bottlenecks will disappear. Store inference results in Redis and have the client poll for them. The system becomes much more robust as you can resend tasks even if a physical failure occurs.