Reliability first
Regression safeguards, expanded test coverage, and acceptance-criteria validation aren't a phase they're the contract. The system stays stable while features land.
Engineering · AI
AI Engineering: Production Agent Systems
AI engineering becomes real when an agent leaves a prototype and starts handling customer interactions. At Upfirst, an AI answering service for small businesses, I implemented infrastructure for agent-based workflows that let customers configure actions, then helped keep those services observable and stable in production. The work joined software engineering, cloud operations, and the failure handling required when AI sits in a live customer path.
Upfirst
AI agents in a live customer product
Upfirst uses AI receptionists to answer calls, collect information, answer questions, route callers, and connect to business tools. My contribution sat beneath that product surface: deploying and operating agent-based services, supporting customer-defined actions, and building monitoring and incident detection around the workflows.
What I built and operated
Agentic workflows
Implemented workflow infrastructure that allowed customer-configured agents to trigger user-defined actions. The execution model was event driven, with explicit boundaries around each action and recoverable failure paths.
Production AI infrastructure
Managed deployment and operational stability for agent-based services on serverless GCP infrastructure. The focus was a predictable path from feature rollout to production behavior.
Observability for agent systems
Built automated monitoring, alerting, and incident-detection pipelines that surfaced issues before they reached customers and reduced mean time to detection for production incidents.
Reliability under change
Kept the platform stable through continuous feature rollout by treating telemetry, failure isolation, and operational readiness as part of the feature rather than follow-up work.
AI engineering capabilities
The useful skill boundary is broader than model integration. It includes the workflow engine, cloud runtime, observability, and operational controls that make an AI feature dependable.
AI agent infrastructureAgentic workflowsTool calling and custom actionsEvent-driven AI systemsProduction AI observabilityServerless GCP
How I approach production AI
Observability over guesswork
Logs, metrics, and traces aren't a nice-to-have, they're how the system warns you before users do. Mature observability is what turns 3am pages into morning tickets and shrinks MTTR by the only number that matters: actual incidents.
Test what matters
From SQL-dialect compilers to API contracts to Terraform plans, tests guard the boundaries that fail loudly. Coverage is a means, not the goal.