Talks

A WeAreDevelopers World Congress talk on why stronger models do not remove the need for better context systems around them.

My main argument was that as LLMs get better at reasoning and tool calling, the bottleneck shifts from whether the model can do something to whether we can get the right context to the model at the right time.

I focused on agent memory, traces, and the idea of a context engine: an abstraction layer between agents and the data they need to act, including structured data, unstructured data, APIs, state, history, memory, search, and caching.

My first time speaking at the Edinburgh Data Science and AI meet-up.

I spoke about semantic caching: the idea that LLM applications can reduce cost and latency by recognising when two differently worded questions are really asking the same thing. The simple version sounds easy, but production use brings real design choices: what to cache, how to scope responses to the right users, how to handle false positives, and when cached answers should expire.

The questions and interactions afterwards were excellent. I also enjoyed Stelios Christodoulou’s talk on the ethical, technical, and philosophical considerations of scraping web data in an AI world, especially his framing of a “considerate scraper’s code of conduct”: reuse in-memory copies where possible, respect licensing and attribution, and work with data providers through APIs and alternative sources where you can.

Massive thanks to Emily Takeva, JP Hwang, and David Wood for putting together an excellent event. First time attending this meet-up. Definitely not the last.

A London Java Community talk on building production-ready multi-agent systems with Spring AI and Redis.

I spoke about semantic caching, routing, vector search, and agent memory server, and I also demoed Raphael De Lio’s stock analysis agent app, which tied those ideas together in a practical Spring AI workflow.

There were roughly 50 people in attendance, with most staying through to the end, and the questions were excellent: from short-term versus long-term memory transfer to embedding model choice and why cross-encoders are not usually used in the first stage of vector retrieval. The organisers also asked for a follow-up workshop, which made it feel like a genuinely strong session for the London Java community.

A Devoxx UK conference talk on the complexity that appears when agent memory is split across many tools, databases, and retrieval paths.

The session made the case for a unified memory layer: one abstraction that can handle working memory, persistent knowledge, and agent data access without turning every new memory source into another bespoke tool call.

A Devoxx UK conference talk connecting human memory and practical AI agent design.

Raphael De Lio and I looked at how cognitive science can help frame the problem of agent memory, then tied that back to production concerns: what to store, how to retrieve it, and how our work at Redis led toward a production-ready agent memory server.

A Devoxx France conference talk connecting human memory to the practical challenges of building AI agents with memory.

Raphael De Lio and I looked at how cognitive science can help frame agent memory design, then connected that to production concerns like what to store, how to retrieve it well, and how our work at Redis led toward a production-ready agent memory server.

A first Redis Developer Advocate conference talk on why production AI agents need structured memory.

Raphael De Lio and I covered recurring memory patterns, short-term and long-term memory management, retrieval, decay, and how Redis Agent Memory Server helps teams move beyond stateless agent prototypes.