BUILD PLAYBOOKS & PRODUCTION LESSONS
Practical writing on shipping AI agents to production. From a team that has shipped 80+ agents across fintech, SaaS, and enterprise tech.
RPA replays mouse clicks. Workflow automation runs predefined branches. AI agents reason about novel situations. Each fits a different problem — and choosing wrong wastes budget and burns trust with your team.
Most AI proofs-of-concept die for predictable reasons that have nothing to do with the underlying model. Here are the five killers we see most often, and how to design around each one before you start.
The token bill is the smallest line item. Here is the full cost model for a production AI agent — what to budget, what surprises teams, and where the real money goes.
Build when the workflow is your moat, the data is sensitive, or no vendor sells what you need. Buy everywhere else. Here is the decision framework that has saved our clients millions in over-built bespoke agents.
Five concrete production failures we have personally encountered or rescued — what went wrong, what shipped to fix it, and what to put in place from day one to avoid each.
Most AI pilots stall in evaluation purgatory. The teams that actually ship use a tight 5-day playbook with non-negotiable scope, eval set written before the prompt, and a production-readiness gate baked in from day one.
We've built production agents on all three. Here is when to use LangChain, when CrewAI is the better call, and when both are wrong and you should write the agent loop yourself in 200 lines of Python.
Common patterns we see when founders first try to use AI in their company. Most of the mistakes are predictable — and most are correctable in a single conversation.
Klarna replaced 700 reps with one AI agent. Sierra and Decagon are powering some of the largest support orgs in tech. Here is the pattern they share — and how to apply it without the public stumbles others have had.
Vibe-coding is real, useful, and producing actual revenue. So is rigorous production AI agent engineering. The two patterns serve different goals — and conflating them is how teams waste budgets.