I build the systems behind fast, reliable, cost-aware engineering.

Reliability is not about writing perfect code. It is about finding problems before customers experience them and limiting the impact when something goes wrong. AI-generated changes should clear the same review, test, and SLO bar as human work.

TRY THISStart by defining what reliable means for the customer, then let those signals influence release decisions.

An external model in a critical journey brings its latency, limits, and outages with it. Plan for timeouts, controlled retries, and a simpler fallback that keeps the customer moving. Most importantly, practise recovery rather than only documenting it.

TRY THISList the dependencies in one critical journey and decide what should happen when each becomes slow or unavailable.

AI-generated code can look convincing while carrying unsafe assumptions or excessive permissions. Give agents only the access needed for the task, keep that access easy to revoke, and require clear human approval for security-sensitive changes.

TRY THISReview what every agent can read, change, and execute. Remove anything it does not genuinely need.

AI workloads can be bursty, and one feature may multiply model calls, searches, database requests, and background jobs. Test the whole customer journey under sudden demand and keep a human in the important architectural trade-offs.

TRY THISTest for spikes and dependency limits, not only comfortable averages.

AI can multiply weak engineering habits very quickly. Every new capability also creates ownership, maintenance, cost, and on-call responsibilities. If the team cannot explain who will operate and eventually retire it, the design is not complete.

TRY THISName the owner, operating model, and retirement path before adding another service.

Tokens, model calls, and agent actions can create unfamiliar cost patterns. Engineers should understand the cost of a useful customer outcome while they can still improve the design. Cost belongs beside reliability, latency, security, and scale.

TRY THISMeasure cost per successful outcome, then make that information visible to the team building the feature.

AI is valuable when it groups alerts, explains an incident, spots unusual behaviour, or recommends the next step. For destructive or difficult-to-reverse actions, AI should propose and a person should approve until the system has earned greater trust.

TRY THISBegin with AI as an observer and adviser. Expand its authority only when actions are bounded, visible, and reversible.