Architecture Philosophy

There is no intelligent agent without a capable API.

Before we talk about Large Language Models, we must ensure the underlying machinery is sound. My work with Apigee and Dell Boomi has taught me that the "hands" of the system must be reliable. An AI agent is only as effective as the tools (APIs) it can invoke.

I prioritize designing "Agent-Ready APIs"—endpoints with clean schemas, predictable error handling, and granular authorization scopes that allow autonomous systems to interact safely with core business logic.

In the enterprise, context is everything. Generic models are commodities; proprietary data is the asset.

I focus on building robust data pipelines that feed the "reasoning engine." This means structured logging, consistent event schemas, and clean data warehouses (like Snowflake or BigQuery). Whether it's incident logs for ITSM automation or traffic metrics for API quotas, the quality of the input determines the validity of the AI's output.

I treat AI as a component, not the whole system. It is a probabilistic function call within a deterministic workflow.

• Hybrid Logic: Use code for what code does best (math, rules, routing) and AI for what it does best (summarization, pattern matching, intent).
• Governance: Strict guardrails are non-negotiable. I apply the same quota management and security policies to AI tokens as I do to API calls.
• Observability: You cannot improve what you cannot measure. I advocate for deep tracing of the "thought process" of agentic systems to ensure auditability.

Moving from a notebook POC to a production system requires a shift in thinking. It involves CI/CD pipelines, unit testing for prompts, latency optimization, and cost control. My background in platform engineering ensures that the cool new AI feature doesn't bring down the production environment.