Taking frontier models from flawless demo to dependable system: memory, orchestration, evaluation, and the governance that lets high-autonomy agents be trusted. I lead a regional AI engineering practice at Microsoft Services and build in the open as copilot-agents-dojo.
Memory, state, and orchestration: the architecture that makes an agent dependable, not just impressive in a demo.
Read more → 02The discipline that separates a demo from a system. Benchmarks, eval harnesses, traces, and the numbers that tell you it actually works.
Read more → 03Opt-in execution, verifiable guardrails, soul files, live controls. Engineering controls, not compliance theatre.
Read more → 04Hiring, coaching, and holding a uniformly high bar; executive advisory as a consequence of credibility, not the headline.
Read more →A benchmark went around last week: one setup beating Opus by 8% and GPT by 11%, with no new model and no special access. I've been running the same trick in my own agent for a while. It isn't a smarter brain — it's three ordinary ones and someone to chair the room. Here's what it actually buys, the bill nobody mentions, and why it's the same lesson that made me build a harness for my team.
Read →I had sixty minutes and fifty final-year students to answer one question: what actually separates an agent that does the job from a demo that falls apart the moment a tool call times out? Here's the talk — the definition, the loop, three real exemplars in healthcare, education and the public sector, and where I'd embed Responsible AI so it survives contact with production.
Read →PARA gets you started, then it breaks. Six months in, your weekend vault is a junk drawer and your agent can't find anything in it. Here's how I reshaped Tiago Forte's four folders into deterministic memory categories an AI can actually ground on — frontmatter contracts, progressive summarisation written for a machine, and the weekly loop that keeps a second brain honest.
Read →I lead the architects and engineers who turn the AI frontier into enterprise reality.
The work isn't only technical. It's translating ambition into platforms that are secure, resilient, and accountable — and building the world-class, diverse teams that can deliver them at the highest levels of customer impact.
Accelerated time-to-innovation, with AI moved safely from pilot to production.
Strengthened security postures across cloud and AI estates.
Platforms built to stay reliable under enterprise-scale load and scrutiny.
Cost optimization that holds up as platforms grow.
New business and revenue models unlocked by AI and cloud.
World-class, diverse teams capable of sustaining all of the above.
What I'm building in the open — pulled live from github.com/andreaswasita.
A behavioral governance framework for GitHub Copilot agents — skills.md and instructions to make AI coding agents think like senior engineers.
View on GitHub →A skills & discipline framework for Microsoft 365 Copilot Cowork — for knowledge workers using AI as a coworker.
View on GitHub →Mid-program feedback session. Reviewing student progress, unblocking technical decisions, and sharpening the path to the final showcase.
The capstone presentations — students share what they built, the design decisions behind it, and the lessons they took from the program.
I'm Regional CTO and Director of the Azure Cloud + AI Architecture practice at Microsoft Services, leading high-caliber architects, engineers, and cross-functional partners across APAC and beyond.
I'm an avid reader and a lifelong student of both technology and the financial industry, and I build in the open. copilot-agents-dojo and copilot-cowork-dojo are working frameworks for making AI coding agents behave with the discipline of senior engineers and collaborate as a team — where I prototype the agent patterns I bring to enterprise scale.
Always architecting the future — and deeply grateful for the teams and customers who make it possible.