Building AI Products That Ship: A Field Guide
Demoing an AI product is easy; shipping one that survives production is a craft. The disciplines that matter: when to use an agent, engineering context, applying product judgment, and designing for reliability.
Guillaume Rufenacht
AI Product Manager · Lisbon
Building an AI product that demos well is easy. Building one that survives production, real traffic, real edge cases, real cost, is a different craft. It comes down to a handful of disciplines: knowing when to use an agent at all, engineering context instead of stuffing it, and designing for reliability from the start. This is the field guide, with each part linking to a deeper piece.
It’s the work I do daily on the pipelines behind Geonimo. Here’s how the pieces fit together.
Key takeaways
- Don't build an agent where a workflow would do; reserve agents for ambiguous, high-value tasks.
- Context engineering, not prompt wording, is where most production quality is won or lost.
- The PM's leverage is in research, insights, and judgment, not in letting AI make the bets.
- Most production failures are design problems: context rot, sprawl, no verification, no evals.
- Simplicity, verification, and measurement are what make ambitious agents dependable.
Start simple: effective agents
The foundation is restraint: don’t build agents for everything, keep them as simple as the task allows, and think from the agent’s perspective. The three ideas are in building effective AI agents.
Engineer the context
As models got better at following instructions, the hard part moved from wording prompts to assembling the right context, memory, state, retrieval, tools, kept minimal but sufficient. That discipline is in context engineering.
Apply product judgment
AI changes the product manager’s job: lean on it for research, insights, and prototyping, but keep the vision and the strategy bets human. The playbook is in the AI product manager’s playbook.
Design for production
The gap between demo and dependable is where projects die. The failure modes, context rot, subagent sprawl, missing verification, no evals, unbounded autonomy, and their fixes are in why AI agents break in production.
The through-line
Taking AI from prototype to production is what I do. See what I’ve built or get in touch.
Frequently asked questions
Why do AI products fail in production?
Usually design problems, not model weakness: context rot, subagent sprawl, missing verification, and no evals. The gap between a demo and a dependable system is where most projects die.
When should I build an agent versus a workflow?
Use a workflow when you can map the decision tree; reserve agents for ambiguous, high-value tasks where exploration justifies the cost. Don't build agents for everything.
What's the highest-leverage skill in building AI products?
Context engineering, assembling the right memory, state, retrieval, and tools, kept minimal but sufficient. As models improved, this overtook prompt wording as where quality is won.
Work with me
Want a system like this built for your pipeline?
I help teams take AI from a clever prototype to dependable production, outbound engines, lead intelligence, and the LLM pipelines underneath. See what I have shipped or get in touch.