Gareth Williams. Independent consultant specialising in agentic engineering, AI-native architecture and the organisational side of getting AI into production.
I work with engineering teams and leadership across the full arc - from strategy to shipping.
Designing and building AI agent systems, evaluation frameworks and production pipelines. From proof-of-concept to something that ships.
Interim and fractional CTO. Scaling teams, establishing engineering practices and driving delivery when it matters most.
Sourcing, assessing and onboarding engineering talent at every level. Finding the right people and setting them up to succeed.
AI readiness assessments, cloud architecture and DevSecOps for organisations modernising at pace. Practical, not theoretical.
I built Itinerate Consulting on a simple premise: the best technology decisions come from people who have shipped real systems under real constraints. I work across strategy, architecture, delivery and leadership - often all at once.
My focus right now is agentic engineering - the discipline of taking AI from a compelling demo to a system that runs reliably in production. That means evaluation frameworks, agent orchestration, human-in-the-loop design and the organisational changes that make it stick.
I'm based in Australia and work with clients globally. Engagements range from short advisory stints to longer embedded partnerships.
Notes on agentic engineering, AI in production and the practice of technology leadership.
The most important architectural decision in AI isn't technical — it's ethical. This piece argues that building human-centred AI systems, where machines handle tedious work and humans retain judgement, is both a moral obligation and a competitive advantage. The alternative — reverse centaurs where humans become accountability shields for automated decisions — is already here, and technologists must actively choose against it.
AI agents fail at complex business tasks not because the model is wrong, but because it has no machine-readable model of how your organisation actually works. This piece makes the case for enterprise ontologies — semantic layers that unify process models, data schemas and business rules into a single queryable graph — and shows why LLM accuracy on business queries jumps from 16% to 72% when one is in place.
Anthropic positions itself as the responsible steward of advanced AI, but its actions tell a different story. This piece examines the Mythos model release and what it reveals about how commercial pressure and government contracts consistently override stated safety principles — and why the concentration of critical AI infrastructure in a handful of American companies is a geopolitical problem every nation needs to take seriously.
I'm selective about new engagements - I take on work where I can make a real difference. Tell me what you're building and where you're stuck.
I typically respond within one business day.