AI agents are systems in which an artificial intelligence model decides part of its own path and uses tools to reach a goal: checking documents, updating a file, preparing or sending a message. The difference from a chat is that the agent does not only answer. It acts within defined limits.
For someone running a company or building their own business, the useful question is not technical. It is which work the agent will take on, by what criteria and under what oversight.
AI agents and workflows: what is the difference
The distinction Anthropic draws between workflows and agents helps frame the choice. In a workflow, the sequence of steps is defined in advance. In an agent, the system itself dynamically decides part of the path and the tools it uses.
Not every job needs an agent. When the sequence is always the same, a predictable workflow can be simpler to oversee. The architecture should follow the work that needs doing, not the other way around.
Roles only make sense with real responsibilities
Giving agents names and titles can help organize an idea, but the structure has to match real responsibilities. Before creating a coordinator and several specialists, describe the result each role must deliver and how the parts relate.
When preparing a market analysis, one role might gather sources, another examine what they support and a third organize the presentation. Splitting helps when there are different quality criteria. But it creates handoff work: if context gets lost between roles, the split makes the deliverable worse.
Start by describing one complete responsibility: inputs, expected result, available resources, limits on action and situations that call for help. Then judge whether splitting part of it would solve a difficulty you actually observed.
Who recognizes that the work is done
A coordinator needs to know what is missing for the whole to be ready. Receiving three files does not mean they answer the original question. The review checks consistency, coverage and fit with the goal.
Also define what gets recorded for the next run. Current decisions and recurring corrections are useful; discarded hypotheses need to be marked as history. Instruction files organize these agreements, but running them requires an environment that reads them and has the necessary access.
A personal agent needs to know your priorities
A personal agent can be the front door for organizing requests, checking context and preparing follow-ups. To do that, it needs a useful picture of your priorities. "Put customers first" does not say how to handle a new request, an existing commitment and an internal meeting competing for the same hour.
Turn priorities into observable criteria. Record fixed commitments, important deliverables and what should be treated as a constraint rather than a preference. An executive who wants to protect time for a strategic decision can ask the agent to flag conflicts and propose alternatives. Moving meetings would require a specific permission.
Test it on a week you know and look at the differences. They reveal criteria you have not yet explained. And the agent should not turn a one-off remark into a permanent rule without that being visible.
What leadership decides before expanding
AI agents start to make sense when it becomes easier to explain who does what and to recognize a complete deliverable. The number of agents can grow as verifiable reasons appear to split the work, and autonomy can increase as the work becomes easier to oversee.
In AI Thinking, Dani Hayoshi's method, this is the central question: who does each step, and how is it checked? To keep going:
Delegating to AI agents: what leaders need to define
How to test whether an AI agent is ready to use
AI governance: 4 decisions for leaders
Dani Hayoshi is the author of AI Thinking. Her work connects artificial intelligence, education and entrepreneurship. About Dani.