​Description

​An agent can remember the facts of a task and still lose track of its work. Join Tokyo AI (TAI) for an evening on agent memory, execution state and recovery: what to retain for reasoning, what to record reliably, and how to continue after interruption.

​Designed for engineers building and operating agents, application architects and technical leads, this event connects memory and retrieval choices with the reliability requirements of long-running systems.

​Melanie Warrick, working on AI developer relations engineering at Temporal, will present What an Agent Needs to Remember, including a live demo. Two complementary talks are being developed; additional speakers and talk details will be announced once confirmed.

​When: Wednesday, October 14, 2026. The 18:00-21:00 JST window shown on this private draft is provisional and will be confirmed with the venue.
Where: Tokyo, venue to be announced.

​Agenda

​Provisional schedule, subject to venue and lineup confirmation. Each talk slot includes Q&A.

• ​18:00 - Doors open

• ​18:25-18:30 - Welcome

• ​18:30-19:00 - What an Agent Needs to Remember, Melanie Warrick

• ​19:00-19:05 - Changeover

• ​19:05-19:35 - Talk 2, speaker and details to be announced

• ​19:35-19:45 - Break

• ​19:45-20:15 - Talk 3, speaker and details to be announced

• ​20:15-20:20 - Closing takeaways

• ​20:20-21:00 - Networking

• ​21:00 - Doors close

​Talks

​What an Agent Needs to Remember

​Speaker: Melanie Warrick (AI developer relations engineering, Temporal)

​Abstract:

​Ask what an agent should remember and you’ll usually hear about working, episodic, semantic, and procedural memory. These describe information available to the agent as it reasons. But an agent can remember every fact about a task and still forget where it is in the task.

​Production agents also need reliable execution state: which tools ran, which side effects occurred, what a person told the agent, what work completed, and what should happen next. This talk distinguishes what an agent knows from the state of its work and explains why they have different correctness requirements. A live demo makes the distinction visible: an agent that remembers everything about its task but still cannot tell whether it already performed an action.

​Then we’ll take the problem into long-running agents, where preserving execution history creates another challenge: the history itself can become too large. Temporal provides the durable execution examples, but the architectural question applies more broadly: what must a system preserve so an agent can resume its work, not just recall what the work was about?

​Bio:

​Melanie Warrick works on AI developer relations engineering at Temporal, focused on building reliable AI systems and agents. She is also co-founder and CTO of Fight Health Insurance, an AI platform that helps people appeal denied US health insurance claims.

​Melanie has worked in AI for more than a decade, from implementing an open source neural networks platform (Skymind) and fine-tuning domain models (FHI) to deploying AI applications in production. Her broader engineering background spans distributed systems, developer infrastructure, and health tech, including work at Google Cloud and engineering leadership at startups.

​Talk 2, to be announced

​Speaker, title, abstract and bio will be added once confirmed.

​Talk 3, to be announced

​Speaker, title, abstract and bio will be added once confirmed.

​Organizers

​Ilya Kulyatin is an entrepreneur with work and academic experience in the US, Netherlands, Singapore, UK, and Japan. He holds a BA in Economics, an MA in Finance, and an MSc in Machine Learning. He's a 3x founder, now helping Japan grow the local AI ecosystem through a not-for-profit community, Tokyo AI (TAI), while building an AI-native system integrator and solutions provider, Foundry Labs株式会社.

​Supporters

​Foundry Labs K.K. is a Tokyo-based AI systems integrator and solutions provider, delivering end-to-end support for enterprises: from strategy design through implementation, deployment, and operations. They tailor AI to each client's operational, regulatory, and security requirements, with hands-on experience across finance, government, and industry, and a track record of shipping production systems in secure and regulated environments.

​About TAI

​Tokyo AI (TAI) is the largest international AI community in Japan, with 5,000+ members mainly based in Tokyo: engineers, researchers, investors, product managers, and corporate innovation leaders. Through 80+ events a year and 300+ speakers spanning startups, enterprises, and academia, TAI connects the people building AI in Japan with the global ecosystem, working to transform Tokyo into a global AI hub.

​Privacy Policy

​We will process your email address for the purposes of event-related communications and ongoing newsletter communications. You may unsubscribe from the newsletter at any time. Further details on how we process personal data are available in our Privacy Policy.