Abstract
As models improve, we are starting to build long-running, asynchronous agents such as deep research agents and browser agents that can execute multi-step workflows autonomously. These systems unlock new use cases, but they fail in ways that short-lived agents do not.
The longer an agent runs, the more early mistakes compound, and the more token usage grows through extended reasoning, retries, and tool calls. Patterns that work for request-response agents often break down, leading to unreliable behaviour and unpredictable costs.
This talk is aimed at use case developers, with secondary relevance to platform engineers. It covers the most common failure modes in async agents and practical design patterns for reducing error compounding and keeping token costs bounded in production.
Speaker
Meryem Arik
Co-founder and CEO @Doubleword (previously TitanML)
Meryem is the Co-founder and CEO of Doubleword (previously TitanML), a self-hosted AI inference platform empowering enterprise teams to deploy domain-specific or custom models in their private environment. An alumna of Oxford University, Meryem studied Theoretical Physics and Philosophy. She frequently speaks at leading conferences, including TEDx and QCon, sharing insights on inference technology and enterprise AI. Meryem has been recognized as a Forbes 30 Under 30 honoree for her contributions to the AI field.
