My approach
A useful solution starts with a shared understanding of the problem. From there, I make the decisions, tradeoffs, and evidence visible throughout the work.
Understand the organization, stakeholders, workflows, and constraints.
I listen to the people who use, support, and depend on a system. Their work reveals requirements that a feature list alone can miss. I bring assumptions into the open and establish the business objective.
Convert business needs into clear priorities, functional requirements, and acceptance criteria.
I distinguish essential behavior from optional scope, identify dependencies, and agree on what a useful result looks like. Clear acceptance criteria give everyone a practical basis for evaluating progress.
Design an appropriate technical solution with clear system boundaries.
I weigh platform fit, operational cost, access control, integration needs, and long-term maintenance. I make tradeoffs visible so the architecture serves the organization’s constraints as well as its ambitions.
Implement the product through hands-on engineering and carefully directed AI-assisted workflows.
I break implementation into bounded tasks. When delegating to AI coding agents, I specify context, permitted scope, constraints, and acceptance criteria. I evaluate proposed changes and remain responsible for what becomes part of the system.
Test behavior, challenge assumptions, diagnose defects, and verify requirements.
I review the implementation against the original workflow, test consequential paths, and investigate failures using evidence. Deployment planning includes verification, a recovery path, and a realistic way to roll back a change.
Refine the solution from operational evidence, feedback, and evolving business needs.
I use what happens in practice to guide the next change. User feedback, defects, and operating constraints help separate valuable improvements from unnecessary complexity.
AI, with accountability
AI has expanded what I can accomplish, but it has not changed who is accountable for the decisions.
I define the problem, direct the tools, evaluate their output, and retain engineering responsibility. Coding agents can help implement and investigate, but their work still needs context, constraints, testing, and review.
I distinguish a plausible answer from a verified result. When a system affects operations, its permissions, failure modes, and recovery paths deserve the same attention as its main features.
Stakeholders need to understand what is being built, why a tradeoff was made, and what remains uncertain.
I use requirements and acceptance criteria to keep that conversation concrete. Before deployment, the discussion includes what will change, how it will be verified, and how to recover if the result differs from expectations.
See the judgment behind a recoveryA conversation is a good place to start
Whether you're a business looking to improve operations or an organization seeking someone who combines business insight with hands-on technical execution, I'd welcome a conversation.