Multi-Agent Financial Planning Prototype
Exploration of function calling, agent orchestration, deterministic boundaries, confidence gates, governance, and security testing for production-style AI workflows.
Projects & Applied Work
Building to understand what production really requires.
Selected personal, research, and prototype work across AI agents, cloud intelligence, data systems, APIs, and operational automation.
AgentsMulti-agent orchestration & autonomous workflows
DataAPIs, knowledge graphs & information platforms
CloudAWS-native prototypes & operational intelligence
Selected Projects
Research prototypes and hands-on systems.
These examples highlight technical themes without exposing proprietary employer architecture or confidential implementation details.
GA³Ops Research Framework
Graph-augmented agentic AIOps using trust-governed digital twins, knowledge graphs, forecasting, and autonomous workload intelligence.
X-AD
Explainable anomaly detection for serverless systems, focused on operational insight rather than black-box anomaly flags.
RegAgent
LLM-powered autonomous agents for adaptive financial regulatory monitoring, reasoning, and reporting workflows.
Personal Data & API Experiments
Hands-on explorations of local-first data layers, multi-source APIs, party information services, retrieval, and intelligent data access patterns.
Autonomous Reconciliation
Multi-agent research prototype for exception handling, reasoning, reconciliation workflows, and controlled automation in wealth-management contexts.
Engineering Principles
Patterns that show up repeatedly in my work.
Deterministic where it matters
Use AI where ambiguity and reasoning add value; keep money movement, policy, validation, and critical controls deterministic where appropriate.
Design for failure
Resilience, observability, graceful degradation, recovery, and operational simplicity are architecture features, not afterthoughts.
Human governance, not human bottlenecks
Human oversight should be targeted to uncertainty and risk instead of inserted indiscriminately into every automated step.
Optimize the whole system
Technical elegance is useful only when it also supports delivery, maintainability, cost, security, business outcomes, and team ownership.
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