Evolve and maintain production systemRegression harnessIRISInfrastructure automationPlatform adoption
Regression Harness Engineering for InterSystems IRIS
Owning a 10,000+ test regression harness that translates QD regression testing needs into TAE-backed automation workflows.
This is Daniel's formal infrastructure-backed automation work. He owns a large-scale InterSystems IRIS regression testing harness used to run 10,000+ tests across active branches. The work translates Quality Development requirements into automation built on TAE capabilities for VM provisioning, IRIS deployment, environment configuration, and job execution. On top of that infrastructure layer, Daniel built custom resource querying and management, job queueing, platform targeting, failure lifecycle automation, and test exclusion/inclusion governance workflows. The harness reduced job errors by approximately 80% and reduced manual triage from 70%+ of active time to under 10%.
Shows
End-to-end ownership, platform fluency, cross-functional coordination, reliability instincts, and translation from ambiguous engineering needs to operational systems.
Maturity: Evolve and maintain production system
maintainedLLM workflowRAGStructured outputsSME review
Evidence-Grounded AI Failure Investigation for InterSystems IRIS Regression Workflows
A deployed and maintained AI workflow that turns regression failure artifacts into evidence-grounded investigation reports for SME review.
This self-initiated AI-assisted workflow addresses a formerly manual investigation process for IRIS regression failures. It assembles preprocessed unit test logs, preserved application and system logs, deterministic test code context, version-aware documentation retrieval, historical test result signals, and potentially relevant product changes. The system produces structured investigation reports with failure type classification, evidence citations, log correlations, failure signatures, and remediation steps, then enriches Jira tickets for subject-matter expert review. It reduces active SME analysis from minutes-to-hours of synchronous work to a few asynchronous minutes while keeping remediation decisions human-owned.
Shows
Practical AI deployment judgment: context engineering, retrieval quality, evals, guardrails, ticket integration, and reviewer control.
Maturity: Deployed and maintained AI-assisted workflow
builtObjectScriptCompiler signalsMCPDependency graph
Codebase Impact Graph for Safer Changes in ObjectScript Systems
A locally used ObjectScript dependency graph tool that gives engineers and AI coding agents deterministic codebase context.
As AI coding agents become more common, ObjectScript-heavy systems need better dependency context than generic code graph tools, grep, or oversized prompts can provide. This internal tool builds a deterministic call-dependency graph using source parsing plus IRIS compilation, so macro expansion, generated methods, inherited methods, and compiler-visible relationships can be surfaced at the symbol level. Engineers can query it from the command line or expose it through MCP for coding agents. It is locally used by Daniel and some other engineers, not a hosted service or automated production pipeline.
Shows
Ability to build deterministic context systems that make AI coding workflows safer, more inspectable, and more token-efficient.
Maturity: Built and locally used internal tool
designprototypeBug escape preventionTest strategyOracle gapsPrototype
AI-Assisted Bug Escape Prevention System
A conceptual design/prototype plan for moving from code-change impact analysis to gap analysis, test-plan generation, and reviewed test implementation.
This design-level AI initiative was broader than a test-plan critique tool. It proposed a phased bug escape prevention workflow: analyze every relevant code change before release, build impact and behavioral path artifacts, identify execution and oracle gaps, generate structured test intents, produce reviewed test-code changes, and attach evidence showing paths covered, gaps closed, mutation results, runtime impact, and flake risk. The project was paused when Daniel was asked to focus on broader AI adoption enablement, so it should be read as conceptual design and prototype-level work, not shipped production impact.
Shows
Upstream AI workflow design, human-in-the-loop automation, measurable quality signals, and honest maturity framing.
Maturity: Conceptual design/prototype-level
enablementAI adoptionMCPEnablementEvals
AI Adoption Enablement Across Quality Development
Advising leadership, mentoring engineers, and building reusable AI/MCP tooling for practical QD adoption.
Beyond individual AI systems, Daniel served as a first-mover, advisor, mentor, and tooling builder for AI adoption across Quality Development. The work included weekly leadership strategy discussions, education-first adoption planning, use-case discovery, reusable skills, local MCP tooling for internal workflows, guidance for engineers building their own tools, and mentoring around prompts, evals, guardrails, structured outputs, context engineering, least-privilege tool access, and human review. This was advisory and enablement work, not formal management or mandate ownership.
Shows
Ability to scale practical AI patterns across an engineering organization while keeping cost, risk, privilege, and review boundaries explicit.
Maturity: Department-level enablement/advisory work