FFC NL 2025 TALKS
FFC UK 2026
Organisational Anti-Patterns When Teams Share an AI Agent
My team’s AI strategy looked clean on paper: one shared agent, multiple product teams plugging in via MCP tools. In practice, it surfaced every team topology anti-pattern we thought we'd solved. In this talk, I share what actually happened when our stream-aligned teams tried to build on top of a shared agentic infrastructure owned by a separate Team AI. Conway's Law showed up immediately: team boundaries became agent capability boundaries. Coordination overhead ballooned as every agent feature required sign-off from Team AI, TechOps, Security, and Product simultaneously. Engineers started building against agents before anyone understood what agents could do. And the cognitive load of switching between "what does the agent know?" and "what does our product know?" turned out to be its own engineering problem.
If you're about to introduce an agentic capability into your platform, the patterns in this talk will help you fail faster and smarter.
MEET THE SPEAKERS
Maryleen Amaizu
Machine Learning Engineer at Redgate
Dr. Maryleen Amaizu is a seasoned machine learning engineer and AI specialist with extensive experience developing privacy-aware AI and data solutions across diverse domains including healthcare, finance, public sector, and beyond. She holds a PhD in Computer Science from the University of Leicester, where she focused on privacy-preserving machine learning systems.
At Redgate Software, Maryleen builds privacy-aware AI-integrated software to ensure compliance and safe test data sharing. She has worked on synthetic data generation, text anonymisation, LLM-based masking dataset generation, intelligent PII classification, and privacy evaluation framework.
Her expertise and impact have been recognized with several accolades, including the AI Champion of the Year award by Bupa Everywoman in Technology in 2025 and the Excellence Award in Data Protection and Information Privacy from the Young CISO Network in 2022.