I have a few core observations about work that influence how I approach AI:

1. There is one-time work (development, personal assistant) where the input and output are net new. There is also back office work (billing, FP&A, questions about data) that is quite repetitive--working within existing datasets.

2. Back office work exists within SaaS products all of which are including AI within their platforms. Those SaaS products are often integrated with each other.

3. Business logic needs to be transferred between systems in order for each SaaS AI to be accurate. (Asking a Salesforce AI and a Xero AI "What is our revenue?" may get you different answers if either system has custom logic that defines any input to that question differently.)

Based on this, I believe that the optimal path forward for back office work is:

1. Centralize agents (business logic) across an organization

2. Decouple agents from orchestration

Agents that exist at the end of an API call--which can be from a SaaS, Airflow, vibe-coded app, or model harness--are a better answer than platform specific AI. They provide a centralized repository for business logic while reducing vendor reliance (and lock in). Additionally, you get better observability and auditabilty.

Sleeper Service is the result of this thinking--an enterprise AI framework.

Larger write up of my thought process behind Sleeper Service here: https://zero2data.substack.com/p/agents-as-a-service