01 · Shift
From chat assistant to workflow executor
The first wave of enterprise AI was simple: give employees access to a model and hope they discover productivity gains. That helps, but it rarely becomes an organizational capability.
Agent-based work is different. An Agent has a role, a task boundary, access to tools, a defined workflow, and checkpoints where humans approve key actions.
- Copilots speed up a step.
- Agents own a repeatable workflow.
- Implementation shifts from prompting to process design.
02 · Entry Point
Do not begin with a universal company Agent
A company-wide AI assistant sounds attractive, but it is usually too broad for the first project. Knowledge scope, permissions, and user expectations become hard to control.
A stronger path is to start with a narrow workflow: daily research briefs, customer visit summaries, contract risk pre-screening, or production issue classification.
03 · Design
A usable Agent must define five things
A real Agent requirement cannot stop at 'automate this'. It must specify who uses it, what input it receives, what knowledge and tools it can access, what output it produces, and when it must stop for human approval.
This is the difference between personal AI and enterprise AI: companies need traceability, permissions, exception handling, and accountability.
- Role
- Input
- Knowledge sources
- Tool permissions
- Human approval points
04 · Execution
Ship a usable MVP in 2-4 weeks
Avoid starting with a large platform. Choose one high-frequency scenario, connect only the necessary data, prepare real test cases, and let a small group use it every day.
The MVP should validate usage, output quality, and time saved. If those three are proven, expand the knowledge scope and automation depth.
- Week 1: scenario and acceptance criteria.
- Week 2: knowledge and tool integration.
- Weeks 3-4: pilot, logs, and iteration.