AI and Agent Systems in Production
Shipping in Days, Not Months
BY ANDY HOUSTONContext
Teams excited about AI but stuck between flashy demos and real systems that reliably do useful work.
Problem
POCs impressed leadership but stalled before production. Tooling was fragmented. No one owned the end-to-end path from idea to deployed agent or automation.
Approach
Treated AI projects like any other production system. Scoped the narrowest high-impact problem, designed the agent workflow, then wired in the right mix of LLMs, tools, and guardrails. Focused on monitoring, failure modes, and operator experience, not just model cleverness.
POC-to-production transition flow with monitoring gates.
Stack
- LLMs (Claude, OpenAI)
- APIs
- Python
- TypeScript
- Queues and schedulers
- Existing SaaS tools
Result
Shipped working agents and automations in days or weeks that quietly handled research, data preparation, or repetitive operations, freeing humans for higher-leverage work.
Days-to-ship counter and hours-saved cumulative tracker.
Impact
Moved AI from "innovation theater" to a genuine operating capability inside the business.
Lessons
The win is a workflow that survives bad inputs and gets better as the corpus grows. Guardrails and observability matter as much as prompts.
Why this matters to you
For operators and founders who want AI to show up on the P&L, not just in slide decks.
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