Workshop on Sep 12: shipping LLM systems that actually survive production

Reddit r/AI_Agents 新闻
reddit ai agents

摘要

There's a hands-on masterclass on Sep 12 for anyone building agents who wants real engineering discipline instead of finding out something broke from an invoice. Covers: Tracing, token/cost monitoring, latency budgets, and caching, so problems show up in dashboards, not surprise bills Tool-using agents with function calling, validation, guardrails, retries, and fallbacks, so a bad call degrades gracefully instead of compounding Versioned prompts with regression tests, so an edit can't silently d

There's a hands-on masterclass on Sep 12 for anyone building agents who wants real engineering discipline instead of finding out something broke from an invoice. Covers: Tracing, token/cost monitoring, latency budgets, and caching, so problems show up in dashboards, not surprise bills Tool-using agents with function calling, validation, guardrails, retries, and fallbacks, so a bad call degrades gracefully instead of compounding Versioned prompts with regression tests, so an edit can't silently degrade quality A real eval harness combining deterministic checks and LLM-as-judge Bootstrap confidence intervals and paired significance testing for model comparisons Evaluated RAG with retrieval metrics (recall@k, MRR) Led by Bruno Gonçalves, PhD, founder of Data For Science, who trains engineers at Fortune 500 companies on this exact stack. Link in comments.
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