Fuad Ali is a senior product manager at Arize AI building tools that make AI agents easier to observe, evaluate, and improve in production. His work applies a background in distributed systems to a central problem of autonomous software: understanding what an agent actually did, where it failed, and how to make its next attempt better.
Ali studied electrical and biomedical engineering at McMaster University before engineering roles at Tesla, SpaceX, and Twitter. At Tesla, he worked on mobile infrastructure and energy products; at SpaceX, on flight software and autonomous systems; and at Twitter, on distributed systems supporting content moderation. He later joined Federato, progressing from senior engineering into product leadership, before moving to Arize in 2025. His professional portfolio traces that transition from infrastructure engineering to AI product development.
Building agents that improve with evidence
Coding-agent tracing and evaluation: Ali co-authored Arize’s introduction to coding-harness-tracing, an open-source project that instruments coding agents including Claude Code, Cursor, Codex, and GitHub Copilot. Capturing prompts, tool calls, file operations, failures, latency, and token usage turns opaque sessions into material for debugging, experiments, and better team workflows.
Evaluation-driven prompt improvement: Working with SallyAnn DeLucia on a prompt-learning workshop, Ali helped demonstrate how examples, human feedback, automated evaluations, and revised instructions can improve agent behavior iteratively. For subjective tasks such as judging property photographs, he recommends starting with a basic good-or-bad assessment, then introducing specific criteria such as lighting as evaluators accumulate useful signal.
Specialized agents and effective handoffs: Ali argues that agents in a larger workflow should refine instructions for their own responsibilities. Targeted improvement can strengthen handoffs and build expertise around a particular codebase without forcing every component into an overly generalized prompt.
Ali also co-hosts The Next Iteration with Damien Joseph, interviewing builders, founders, physicians, and researchers about technology, careers, health, and personal development.
SallyAnn DeLucia and Fuad Ali turn agent failures and evaluation explanations into revised prompts, then build a JSON webpage experiment that preserves each candidate and its results.
Fuad Ali shows how a refund call can appear to recover in text while still failing in audio and execution—and how linked traces, audio evaluations and replayed experiments make those failures easier to find and fix.
A correction in the transcript does not prove that the action changed: the refund example still processed order forty instead of fourteen.
