Practical notes on enterprise automation diagnosis
Short field notes for deciding what to automate, what to keep under human approval, and when a free fit check should become PRD + workflow design.
Diagnosis lens
01 / Fit Check
Do not trust an AI agent after one good demo: run it with logs, evals, and feedback loops
AI agents need operating discipline after deployment. Logs, traces, evals, gold sets, human review, rollback paths, and feedback loops turn one lucky success into repeatable quality.
AI agent permission design: why read, write, and execute must be separated
Before connecting AI agents to company tools, separate what they may read, what they may write, and what they may execute. Permission design is the difference between useful automation and risky overreach.
What improves when AI agents can read company knowledge?
AI agents become useful at work when they can safely read SOPs, meeting notes, customer history, policies, and past decisions. The real work is deciding access, sources, logs, and cloud/local boundaries before automation.
The limit of rule-based automation: where workflows are enough and where agents are needed
Many AI automations are really rule-based routing plus LLM drafting. This guide separates stable workflows from agentic work that requires dynamic tool use, planning, and guardrails.
