# Agent architecture — AI Engineer Sydney 2026 > An agent is part model, part software system and part operating environment. Explore how teams structure context boundaries, tools, orchestration and interfaces so capability remains understandable as systems grow. Canonical page: https://webdirections.org/ai-engineer/topics/agent-architecture/ 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… - [From Prompt Rules to Structural Guarantees: The Harness Behind a Production Analytics Agent](https://webdirections.org/ai-engineer/speakers/jiggy-kakkad/) — Jiggy Kakkad & Tinus Willemse Checkout AI answers open-ended questions about retail sales data in natural language. It plans, calls analytics tools over MCP, executes Python in a sandbox, and returns a written analysis with charts. In a system like this, failure is rarely a crash: the chart renders, the prose is fluent, and an incorrect figure reaches a decision-maker unchallenged.… - [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.… - [It's time for a new kind of software](https://webdirections.org/ai-engineer/speakers/rupert-manfredi/) — Rupert Manfredi Applications package a predetermined interface, data model and set of capabilities around somebody else’s idea of a task. Generative models can now produce software on demand, but generative UI today either sits atop the existing application stack or replaces the richness of the app with an interface disconnected from the data and capabilities needed to do… - [From REST to Agentic: Trusted APIs in the age of AI](https://webdirections.org/ai-engineer/speakers/leigh-whiting/) — Leigh Whiting Enterprise platforms don't get to start from scratch. When AI agents need to act on behalf of users, they inherit the API surfaces those users already depend on — surfaces evolved over many years for human-driven workflows, not autonomous reasoning. This talk explores the practical journey of adapting established, trusted API layers to serve agentic… - [How to Count to One Hundred](https://webdirections.org/ai-engineer/speakers/hugo-oconnor/) — Hugo O'Connor Agents don't work well together in chat rooms: put a few in a channel and they talk past each other. Natural language and unrestricted JSON feed an open-ended pipeline of reasoning and tool calls with full Turing power. Deciding whether to accept a message based on what arbitrary computation will do is undecidable in general. Meanwhile, the communication… - [My Agents broke APIs - Fixing Multi-Agent Systems with MCP](https://webdirections.org/ai-engineer/speakers/anannya-roy-chowdhury/) — Anannya Roy Chowdhury Modern AI agents struggle not because of reasoning limits, but because of interaction with tools on interfaces designed for humans. In agentic systems, this mismatch leads to incorrect tool selection, redundant calls, increased latency, & weak workflows that fail under real-world conditions. As MCP emerges as a standard for how models connect with tools &… - [We Deleted Most of Our Agents. Everything Got Faster](https://webdirections.org/ai-engineer/speakers/khang-nguyen-hoang/) — Khang Nguyen Hoang The default advice is to add agents: specialised roles, an orchestrator, handoffs between them. We built that. It was a genuinely useful way to explore the problem space, and under real production traffic it was slow and expensive. When an agent system is slow, the instinct is to reach for a faster model. That was the wrong lever. Our latency wasn't… - [Your website is a terrible API: serving agents a different page at the edge](https://webdirections.org/ai-engineer/speakers/jack-bear/) — Jack Bear Every enterprise site we work on was built for a human with a browser. When an AI agent fetches the same URL it receives navigation, cookie banners, client-rendered components and marketing prose, then has to guess at the facts underneath. Retrieval quality suffers and the citation goes to whoever structured their data better. Our first attempt put the… - [Does This Agent Make My Context Look Big? Right-Sizing AI Architectures for Production](https://webdirections.org/ai-engineer/speakers/hamish-songsmith/) — Hamish Songsmith All-in-one personal agent harnesses showcase the incredible potential of capability-rich AI assistants. But deploying a monolithic "do-it-all" agent into production often leaves teams struggling with context dilution, fragile tool calls, un-evaluable execution paths, and massive security blast radiuses. However, swinging to the opposite extreme—decomposing… 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)