HOW I BUILD
AI SYSTEMS.

Every system starts with precision. I define exactly what the agent does and doesn’t do — with edge cases, guardrails, and measurable success criteria. Not “build something to help with support.” More like: handle these ticket types, escalate at this sentiment threshold, log every decision with a reason code. Machines need exactness. I close the gap between what you want and what the agent understands.

AI is fluently wrong. It produces polished, confident output that sounds correct but isn’t. I build evaluation systems that catch this — automated quality harnesses that test agent output at scale, not manual spot-checks. Simulation runs before deployment. Longitudinal metrics after. If an agent starts drifting, the eval system flags it before a customer ever sees the result.

One massive agent is a single point of failure. I decompose work into multi-agent systems where specialized agents handle discrete tasks and hand off cleanly — a research agent feeds a drafting agent feeds an evaluation agent. Each with its own scope, guardrails, and communication protocol. A planner agent orchestrates the sequence. The system is modular, testable, and replaceable at the component level.

AI fails in specific, recognizable patterns. Context degradation — quality drops as sessions get long. Specification drift — the agent subtly forgets the original intent. Sycophantic confirmation — it agrees with bad data instead of pushing back. Cascading failure — one agent’s mistake amplifies through the chain. Silent failure — plausible output that’s quietly wrong. I build detection for all of them because catching failures is cheaper than cleaning up after them.

Where do agents act alone, and where do humans stay in the loop? I map every decision point by blast radius, reversibility, and verification difficulty. Low-stakes, reversible, high-frequency tasks get full autonomy. High-stakes, irreversible, hard-to-verify decisions get human checkpoints. The line isn’t drawn by gut feel — it’s drawn by the math of what happens when things go wrong.

Agents are only as good as the information they have. I design three-tier context systems: persistent knowledge available across all sessions, domain-specific context loaded per agent role, and session-level data pulled dynamically per interaction. I build dirty-data layers that flag stale information and route agents to verified sources. Most AI failures in production aren’t model failures — they’re context failures. I fix that first.

Before you spend a dollar, I show you the ROI. Token economics, model routing, blended cost calculations across multi-step workflows. I know when a lighter model handles 80% of the volume at a fraction of the price, and when the premium model is worth the cost for the remaining 20%. Not every task should be automated. I’ll tell you that too — and save you from six-figure mistakes.