Your first agent in production is not a demo you finally deployed. It's an agent someone owns, monitors, and can stop. Most "first agents" fail the checklist before day one.
The first agent is where startups learn that production is a different bar. The model was the easy part. The checklist is everything else.
Prepare-to-Launch Checklist
- **A named owner.** Not "the team" — a person who answers when it breaks.
- **A confidence threshold and an escalation path.** What it decides alone, and what it routes to a human.
- **An audit log.** Every decision, every input, every output, exportable.
- **A kill switch.** Stop the agent without breaking the business process it sits in.
- **An eval.** A repeatable test that catches regressions before users do.
First Things: Scope Before You Build
The most expensive first-agent mistake is scope. Startups ship an agent that "does the whole workflow," then discover nobody owns the edge cases. Ship the agent that does one defined task with one defined escalation — then expand.
What Breaks on Day One
- The agent gets a malformed input and silently does the wrong thing.
- The kill switch exists but the business process can't actually run without the agent.
- Nobody notices the drift for a week because nobody's reading the log.
The Honest Test
The test isn't "does the agent work." It's "if the agent goes wrong, does someone know, can they stop it, and can they explain it?" If you can't answer those three, you have a demo, not a production agent.
How FACTA Frames It
FACTA's startups and forward-deployed work is built around shipping one real workflow in production in 4-8 weeks — with the ownership, monitoring, and escalation that make it a system, not a demo. The checklist is the deliverable, not an afterthought.
Conclusion
Your first agent in production is defined by ownership, not capability. Pass the checklist — owner, escalation, audit, kill switch, eval — before you call it shipped.
About FACTA
FACTA helps startups and growth-stage teams turn AI into production systems that keep running — not demos that impress once.
We design the architecture around the parts that actually break under real usage: tooling you own, credentials you control, failover, cost controls, observability. The boring infrastructure that keeps a system alive after launch.
Led by Matías Baglieri and Carolina Fogliato, we focus on one thing:
AI leadership that builds. Not just advises.
Run your first agent against the checklist above.
Tell us which boxes you can't check, and we'll help you close them before launch. See how multi-agent architecture scales for when one agent isn't enough.
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