AI Agents That Actually Ship
Every week we meet a team that just spent three months building an "AI agent" that can do exactly one thing in a sandbox and nothing in production. The demo looks magical. The handover never happens. The model gets blamed.
The model is rarely the problem.
Start with a job, not a model
The teams that actually ship AI in operations don't pick a model first. They pick a single, painful, repetitive job — usually owned by one person who hates doing it. Then they wrap an agent around that exact workflow with humans in the loop on the inputs and outputs.
Make the boring parts boring
Logging, retries, fallbacks, evals, and cost ceilings are not optional. They are the product. If your agent runs once a week and silently fails, it is worse than no agent at all.
Ship to one team first
Never roll an agent across the org on day one. Pick one team, instrument everything, and let them break it for two weeks. The patterns you'll find are not in any blog post.
If you want a partner who has done this loop ten times, book a call.