Feb 24 - Melbourne | Feb 26 - Sydney
Evolutionary architecture and fitness functions for AI that works
Many organisations are investing in AI, but only a few can run it safely and reliably at scale.
In this Technology Exchange, Thoughtworks Distinguished Engineer James Lewis will show how to make AI a stable, well-governed part of your technology landscape rather than a collection of disconnected use cases. He will unpack the architecture and platform patterns that keep AI dependable and cost-effective, explain how evolutionary architecture supports continuous change, and demonstrate how fitness functions turn expectations for quality, behaviour, security and cost into automated guardrails.
James will also outline the platform capabilities that matter most for operational AI including observability, policy as code, CI/CD for ML and model registries and how these bring consistency to delivery, monitoring and governance across teams.
Key takeaways
- Evolutionary architecture: Enabling frequent change without disruption
- Fitness functions: Automated checks for quality, security, cost and compliance
- Platform capabilities: How observability, policy as code, CI/CD for ML and model registries support consistent AI delivery and operations
- Practical actions: Steps to strengthen the foundations around the AI you already run.
Melbourne
Date: Tuesday, February 24, 2026
Time: 5:30-7:30pm AEDT
Location: Thoughtworks Melbourne office
Level 35/360 Collins St, Melbourne VIC 3000
Sydney
Date: Thursday, February 26, 2026
Time: 5:30-7:30pm AEDT
Location: TBC
Agenda
5:30pm AEDT
Arrive early to check in, grab your name badge, and enjoy a something to eat as you connect with other attendees before the event begins.
6:00pm AEDT
6:15pm AEDT
James Lewis, Thoughtworks Distinguished Engineer
James Lewis will share practical patterns for running AI safely and reliably at scale. He will cover how evolutionary architecture supports continuous change, how fitness functions create automated guardrails for data quality, model behaviour and cost, and which platform capabilities matter most for stable, governed AI in production.
6:45pm AEDT
7:30pm AEDT
We'll wrap up with key insights from the evening before inviting you to relax, connect, and take in the view over food, drinks, and engaging conversation.
Speaker
James Lewis, Distinguished Engineer, ThoughtworksJames Lewis is a programmer and Director at Thoughtworks UK and is an internationally recognized expert on software architecture and design and on its intersection with organizational design and lean product development. He’s proud to have been a part of Thoughtworks’ journey for 19 years and of its ongoing mission to deliver technical excellence for its clients and to revolutionize the IT industry. As a member of the group that creates the Technology Radar, he contributes to industry adoption of open source and other tools, techniques, platforms and languages.
Host
Brigid O'Brien, Executive Partner, ThoughtworksBrigid O'Brien's three decades of technology leadership span across Australia, Asia and Europe - from startups and scaleups to multi-country enterprises; from not-for-profits to banking. Brigid has spent the last decade at Thoughtworks working alongside Executives and senior leadership teams as they accelerate the pace of change in the evolution of their organisations. She is passionate about modernising organisations, simultaneously promoting both human and technology potential.
Fireside with Martin Fowler
Fireside with Martin Fowler, Chief Scientist, Thoughtworks and hosted by Andy Nolan, Director of AI Technologies, Thoughtworks
Martin Fowler reflects on the evolution of software from the Agile Manifesto to AI-driven platforms. He shares perspectives on building AI products, modernising legacy systems, using RAG for interrogation, and what defines great tech talent today.
Operationalizing AI for business impact
Heiko Gerin, Technical Director, APAC, Thoughtworks
The mainstreaming of AI — and generative AI in particular — is continuing apace. But as AI proliferates, it’s more evident that successfully operationalizing AI models and bringing them to production remains a challenge. From questionable output to unintended consequences, there are a host of real and projected scenarios that prevent organizations from leveraging AI to its full potential.