Programme topic
Context, memory & knowledge
Useful agents need more than a very large prompt. These talks examine memory, provenance, retrieval, ontologies and tacit knowledge—how to give systems the right organisational context while keeping its source and meaning intact.
9 published talks
Talks about context, memory & knowledge
The Elephant and the Goldfish: Architecture Patterns for Cutting 70% of Agent Token Costs in Production
Tanya DixitForward Deployed Engineer, Google
LLM providers sell you a 2-million-token context window like an elephant that never forgets. If you actually build production agents that way, your latency explodes, your retrieval drifts, and your CFO will shut you down in 90 days. In production, the best…
The guess never becomes a fact - provenance-governed, model-free memory for LLMs
Jade XinPrincipal Research Scientist, TensorPRO
Long context solves within-session coherence, not cross-session persistence. Once an assistant starts storing memories, a quieter failure appears: its own inferences can be written back, retrieved in later sessions, and presented as user facts. Hallucinations…
Building Agentic Memory for an AI SOC: Why Our Semantic Cache Was the Wrong Answer
Mukesh SinghPrincipal Detection and Response Engineer, Atlassian
We run a fleet of AI agents against production security detections at Atlassian. One tunes noisy detection rules, another reviews new ones, and more are coming for alert triage and hunting. Every one of them started blind and stateless, re-deriving the same…
From Documents to Defensible Evidence: Rethinking RAG for Audits
Sharat MadanapalliFounder, InTune AI
Audits impose strict requirements for evidence, traceability and expert judgement. Each entity being audited supplies a new body of evidence, made up of manuals, records, tables, diagrams, scans and multilingual documents. Assessment against an audit standard…
The Model Wasn't the MOAT: How 10 Design System Engineers Turned Platform Knowledge into Enterprise AI
Sudharsanam NarasimhanSenior engineering manager, Design Systems & AI Developer Platforms, Atlassian
Most enterprise AI strategies begin with models, central AI teams, and a list of potential use cases. We started somewhere else: with a platform organisation that already shaped how more than 2,000 frontend engineers built software. A team of 10 engineers…
Adaptive Learning: Encoding Clinician Edits as Memory
Vlad GavrilovSenior AI Engineer, Heidi
Clinicians edit their AI-generated notes for many reasons: adding or removing content, fixing spelling, and preferences for structure, wording, or terminology. Some of these edits are contextual, applying in some situations but not others. The data is noisy,…
Ontologies: AI’s Operating Manual For Your Business
Gareth WilliamsPrincipal Engineer, Wesfarmers
Tools churn. Factories commoditise. When everyone has the same models, the advantage goes to whoever gives their agents the clearest blueprint of how the business works - what exists, how it connects and the rules it runs on. That blueprint is an ontology. In…
Extracting Tacit Knowledge for Production Agents
Khali Kalpa-YoungFounder, Alchymie
An eval is a definition of "good" written down well enough for a machine to grade against. In a real business, "good" isn't written down anywhere. It lives in someone's reaction, in what past work actually did, and in how a person talks. So the eval comes…
The Benchmark Ends. The World Doesn’t: Building Persistent Engineering Environments for Continual Learning
Theodoros GalanosFDE @ APAC, Nomic
Large language models can complete sequences of tasks—but continual learning is not just doing more tasks. Most agent environments reset after every episode: state disappears, required follow-up vanishes, delayed effects are cut off, and the next task arrives…