Thomas Hain

Established the research programme that translated academic machine learning methods into production agent behaviours used on the Fetch.ai platform. Work included defining reinforcement learning and decentralised coordination approaches that underpin Autonomous Economic Agents and their decision processes. Coordinated collaborations with university research groups and oversaw publication of applied research that validated agent learning in simulated and real‑world pilots. These collaborative projects produced reference implementations and benchmarks integrated into the Fetch.ai agent framework and SDKs, influencing how developers design token‑driven agent services. Advised on the selection and integration of ML models and tooling for on‑node inference and off‑chain computation, shaping trade‑offs between accuracy, latency and on‑chain cost that determine agent deployment patterns. Scientific choices regarding model architectures and training pipelines were operationalised into platform releases affecting token utility. Contributed to the technical narrative used in investor and partner demonstrations, providing empirical evidence of agent capability. This scientific grounding supported ecosystem adoption, influenced partnership approvals and helped convert research prototypes into tools that drive transactional demand for FET.