Portfolio

AI Agent Case Studies

Public-safe, sanitized case studies showing product, program, governance, and evaluation thinking for enterprise AI Agent work.

The following case studies are sanitized public versions and do not represent any specific employer, customer, or production system.

Enterprise Browser Automation Agent

Enterprise internal systems with repetitive browser operations.

Problem: Employees repeatedly entered forms, selected dates/routes, saved drafts, and checked results across legacy web systems with high manual effort and inconsistent traceability.
My role: Product/program lead defining scope, automation boundaries, operational correctness, and vendor delivery governance.
Solution design: Scoped browser automation around low-risk draft and assistive actions, with human review before final responsibility actions. Defined exception handling, audit events, and success metrics.
Metrics: In selected workflows, the pilot showed approximately 30% reduction in manual effort. Target metrics also include task success rate, manual intervention rate, retry rate, p95 step time, and exception frequency.
What it proves: Demonstrates enterprise workflow decomposition, risk-aware automation, and governance-first agent design.

Internal HR inquiry workflow with large policy-document search load.

Problem: A large share of handling time was spent searching across many internal policy documents before drafting responses.
My role: Mapped workflow, selected representative use cases, defined pilot metrics, and designed human-in-the-loop controls.
Solution design: Designed an assistive Copilot-style workflow requiring citations, mandatory human verification, and SOP-based operating rules.
Metrics: Pilot-oriented metrics include workload reduction, citation coverage, answer usefulness, human correction rate, and adoption readiness.
What it proves: Demonstrates practical GenAI adoption, operating-model design, and safe rollout for internal knowledge workflows.

Financial operations scenario requiring accuracy, evidence, and auditability.

Problem: A single chatbot cannot safely own financial workflow tasks that involve untrusted documents, system-of-record checks, approvals, and downstream actions.
My role: Designed architecture principles and workflow separation for production-grade financial agents.
Solution design: Separated reader, validator, critic, and resolver roles; used typed handoffs, least-privilege tools, artifact storage, and human final approval.
Metrics: Evaluation includes false positive/negative rate, evidence completeness, schema validation success, review time, and audit-trail completeness.
What it proves: Demonstrates systems thinking for regulated enterprise AI Agent architecture.

Global B2C operations with repeated research, planning, and response workflows.

Problem: Operations teams spent significant time on repetitive research and content preparation as customer volume grew.
My role: Owned Japanese requirements, PRDs, acceptance criteria, prompt iteration, engineering coordination, and adoption support.
Solution design: Introduced GenAI workflows, backend prompt improvements, SOP updates, feedback loops, and team enablement mechanisms.
Metrics: In selected operations workflows, the pilot showed approximately 30% reduction in work time, with annualized saving potential estimated from the pilot assumptions.
What it proves: Demonstrates end-to-end GenAI transformation from requirement definition to measurable business impact.