Aquablu · 2026–present
Aura2
Product direction, agent-built capabilities, and delivery workflows.
Aura2 brings connected water devices, servicing, support, and customer operations into one platform. Its assistant needs to understand the user’s job and operate within their permissions.
Led the definition and delivery of Aura’s AI experience, building the capabilities with AI agents. Developed agent-driven engineering workflows that embedded automation, safeguards, and quality checks throughout the delivery pipeline. The broader platform scope was defined collaboratively.
Make the experience useful
I built the assistant’s tools, memory, skills, and routines around roles and organizational scope, with privacy, audit, and cost controls.
Make quality testable
I identified the need for an evaluation system and built it: adversarial cases, calibrated model judges, statistical gates, and evidence for release certification.
Connect AI to the product
I contributed to data-quality detection, operational analytics, Go APIs, React interfaces, and planning. Support demand helped inform capability priorities.
Make operations understandable
The observability experience connects spending to completed work through cost per successful run, token and cache behavior. Measurement coverage makes gaps visible, while failure and latency views support investigation of reliability and response time.
View full-size interface Improve how the team delivers
I optimized test setup and CI execution, built a shared change-aware validation runner, and extended shipping automation with pipeline monitoring and deployment-drift checks. These workflows made safeguards executable rather than dependent on remembered steps.
My contributions extended an existing, collaboratively developed engineering workflow. They do not imply sole ownership of the platform or its delivery framework.
Go · React · TypeScript · PostgreSQL / AlloyDB · Vertex AI · MCP · OpenAPI