# Context, memory & knowledge — AI Engineer Sydney 2026 > 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. Canonical page: https://webdirections.org/ai-engineer/topics/context-memory-knowledge/ Program status: The speaker lineup and talk descriptions are public. Session days, times, rooms and the full timetable have not yet been published. ## Published talks - [The Elephant and the Goldfish: Architecture Patterns for Cutting 70% of Agent Token Costs in Production](https://webdirections.org/ai-engineer/speakers/tanya-dixit/) — Tanya Dixit 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 agents think like elephants, but operate like goldfish. Details: While frontier models offer… - [The guess never becomes a fact - provenance-governed, model-free memory for LLMs](https://webdirections.org/ai-engineer/speakers/kexuan-xin/) — Jade Xin 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 then compound over time. We encountered this while building EDN, TensorPRO's external memory… - [Building Agentic Memory for an AI SOC: Why Our Semantic Cache Was the Wrong Answer](https://webdirections.org/ai-engineer/speakers/mukesh-singh/) — Mukesh Singh 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 context from Jira tickets, a Databricks lake, a rule repo and ATT&CK, or drowning in a raw dump of all… - [From Documents to Defensible Evidence: Rethinking RAG for Audits](https://webdirections.org/ai-engineer/speakers/sharat-madanapalli/) — Sharat Madanapalli 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 follows defined guidelines and requires precise citations. A plausible answer has little value if an… - [The Model Wasn't the MOAT: How 10 Design System Engineers Turned Platform Knowledge into Enterprise AI](https://webdirections.org/ai-engineer/speakers/sudharsanam-narasimhan/) — Sudharsanam Narasimhan 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 working across design systems, internationalisation, and accessibility had something a general-purpose… - [Adaptive Learning: Encoding Clinician Edits as Memory](https://webdirections.org/ai-engineer/speakers/vlad-gavrilov/) — Vlad Gavrilov 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, but inside it is useful signal that can be used to predict and make these edits before a clinician… - [Ontologies: AI’s Operating Manual For Your Business](https://webdirections.org/ai-engineer/speakers/gareth-williams/) — Gareth Williams 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 data.world's benchmark, an LLM querying enterprise SQL directly answered 16% of questions correctly.… - [Extracting Tacit Knowledge for Production Agents](https://webdirections.org/ai-engineer/speakers/khali-kalpa-young/) — Khali Kalpa-Young 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 second. First you get "good" out of wherever it lives, and each place needs its own method. The first… - [The Benchmark Ends. The World Doesn’t: Building Persistent Engineering Environments for Continual Learning](https://webdirections.org/ai-engineer/speakers/theodoros-galanos/) — Theodoros Galanos 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 as though the previous one never happened. Real engineering projects do not reset. They deal in… The timetable is not public. This page does not imply a day, time, room or track. - [Explore the whole programme](https://webdirections.org/ai-engineer/program/) - [Conference overview](https://webdirections.org/ai-engineer/index.md)