Building with AI agents means designing review, not just speed
Speed is the easy part. The harder design problem is review architecture: how correction, escalation, and quality control should work once agents enter the system.
Founder knowledge engine
Arif KhanTag
#operating-model
8 posts
I'm building a company where humans and AI agents have real jobs
The shift I care about is not AI as a clever tool. It is a human-agent operating model where agents carry recurring responsibilities inside a real review system.
Masaya is a proof point, not the whole story
Masaya is the hospitality AI product inside Rightful, but the larger story is the founder thesis behind it: a human-agent operating model for building companies.
What AI agents should actually own inside a company
The useful question is not whether agents can do work. It is what they should own, what humans must keep, and what needs shared review in a practical ownership matrix.
What changes when AI agents stop being demos and start becoming coworkers
The real shift is not that the tools get smarter. It is that delegation, accountability, management, and institutional memory start to feel different.
AI systems should survive contact with real operations
Most AI work fails because teams optimize for demos instead of operating reliability. Real leverage appears when workflows, owners, and review loops are explicit.
What breaks first when agents enter a company stack
Not tools. Governance, handoffs, and clarity. Agent systems fail at the seams where nobody owns decisions.
Content as infrastructure, not promotion
A founder site should compound trust over time. Publishing is not cosmetic; it is an execution layer for ideas.
