SallyAnn DeLucia

How we solved Context Management in Agents — Sally-Ann Delucia

May 10, 2026 · 16:17

Sally-Ann Delucia of Arize explains how her team solved context management for their AI agent Alex, which analyzes trace data from Arize's observability platform. She details the failure of naive truncation and LLM summarization, and the success of smart truncation preserving head/tail with a retrievable memory store. Long sessions are handled with evals that test context at every 10 turns, and heavy tasks are offloaded to sub-agents to keep main context lean. She notes that Claude Code uses a similar truncation strategy, and emphasizes that context engineering matters more than prompt engineering for agent success.

Build a Prompt Learning Loop - SallyAnn DeLucia & Fuad Ali, Arize

Jan 6, 2026 · 52:08

SallyAnn DeLucia and Fuad Ali from Arize present prompt learning, a method to continuously improve AI prompts by using feedback from evals and human annotations. They argue that agent failures often stem from weak instructions rather than weak models, and that adding rules to system prompts can yield 15% improvements on SWE-Bench without fine-tuning or architecture changes. The talk compares prompt learning to GEBA, noting it achieves better results in fewer loops by leveraging text explanations. A case study shows Kline's performance improved from 30% to 45% on SWE-Bench-Lite after optimizing prompts. The workshop demonstrates building an optimization loop that ingests input-output pairs, evaluates them, and iteratively refines the system prompt using OpenAI.