Auto-itera – autonomous experimentation engine for AI engineering decisions
Auto-itera is an autonomous experimentation engine designed to streamline AI engineering decisions. It automates the process of evaluating various models and strategies, providing defensible verdicts based on real production data. The system allows teams to focus on defining goals and candidates while it handles the rigorous testing and iteration process.
- ▪Auto-itera automates the experimental execution and iteration for AI engineering decisions.
- ▪Users provide a goal, candidate models, and thresholds, while the system conducts a series of autonomous stages to reach a verdict.
- ▪The process includes sampling, parallel scoring, diagnosis, and a final test pass to determine whether to ship, scope narrowly, or kill a candidate.
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🧪 auto-itera Autonomous experimentation engine for AI engineering decisions. Define a goal. Give it the candidates. Get back a defensible ship-or-kill verdict in hours — sourced from real production data, scored across arms in parallel, sprint-iterated with discipline, and signed off on a sealed test set. From handcrafted trial-and-error to autonomous scientific search Every team shipping an LLM product has decisions like these on the table: Prompt optimization — does the new system prompt actually beat the current one? Model selection — Sonnet, Haiku, or Opus for this hop? Retrieval strategies — BM25, dense, or hybrid on real customer queries? Workflow tuning — single-call vs two-call orchestration; sync vs queued? Architecture experiments — does adding a router LLM help or just add…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.