Large language models appear remarkably capable in isolation until they meet the systems the business actually runs on: ERP, ITSM, HR and procurement platforms with decades of customisation, undocumented integrations and hard-won internal standards. The bottleneck is rarely the model; it is context, data quality and governance. In this talk, I will walk through practical patterns for giving agents a reliable map of an organisation's systems, processes and dependencies; making instances and data "AI-ready" before agents touch them, and keeping humans in the loop with approval gates, standards checks and documentation trail, whether a person or an agent made it. We’ll explore what it takes to move from agents that can call enterprise tools to agents that can safely reason about and act within complex enterprise environments.
Zoe Yan is a founding AI engineer at Echelon AI, where she builds AI agents for long-horizon enterprise workflows and automation, serving some of the world’s largest enterprises. Previously, she worked at Porsche Digital on large-scale AI systems, multi-agent workflows, and emerging deep-tech applications in the automotive industry. Her background combines applied research with hands-on engineering across LLMs, agentic systems, and real-world AI applications. She is also active in the AI community as a speaker, hackathon mentor and judge, and helps build and grow AI communities.
