Behind a customer's API, a high-quality answer isn't enough. It has to be usable, which means on time.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Towards Data Science.
Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request. We replace this inference-time coding loop with an agentic tool-making pipeline that compiles repeated SOP steps into validated, versioned tools before deployment.
arXiv:2607. 08010v1 Announce Type: cross Abstract: Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request.
A deep dive into the Codex agent loop, showing how WebSockets and connection-scoped caching reduced API overhead and improved model latency.
The article describes a real‑world case of scaling an enterprise integration pipeline from 500 to 8,000 events per second. It emphasizes that during this throughput increase, two correctness guarantees were strictly maintained and never compromised. The post illustrates how to achieve high performance while preserving essential data integrity constraints.
The paper introduces a tail‑risk‑aware scheduling strategy for agentic LLM workflows that decouples readiness from immediate release of model turns. By jointly selecting which ready turn to release and controlling the amount of unfinished work kept in the queue, the method uses a mean‑CVaR objective to adapt to evolving tail risk and online turn‑work estimates. Experiments on real software‑engineering task traces show comparable performance to eager release under light load and a significant reduction in the 95th‑percentile workflow flow time, achieving up to a 3.5× speedup under contention.
arXiv:2609.06128v1 Announce Type: new Abstract: Production LLM agents execute tool-calling loops, retrieval chains, and compositional workflows in multiple modes, yet execution semantics are often co...