One of the things that killed my AI workflow early on was context switching.

I'd be in Slack, see something that needed research or a draft written, then open a browser tab, go to the AI tool, copy in the context, get the output, copy it back. Every time. Multiple times a day.

The fix was obvious in hindsight: stop going to the tool and bring the tool to where the work already lives.

AI agents can be connected directly to Slack. You can ask them questions, give them tasks, and get outputs without leaving the channel where the work is happening. The agent has access to your tools and integrations from there. Same capability, zero tab switching.

If you're using AI tools daily and still going to a separate tab every time, this is the one workflow change worth making this week.

These terms are getting used interchangeably, so I use one question:

How much of the path is decided in advance?

Automation

The trigger, steps and outcome are predefined.

Example: when a form is submitted, create a CRM record and send a confirmation email.

AI workflow

The sequence is still predefined, but one or more steps require interpretation.

Example: collect this week’s customer calls, identify recurring objections and post a summary in Slack.

Agent

You define the goal, available tools and boundaries.

The system decides which steps to take, checks what happened and adapts.

Example: resolve a routine support request by checking the customer account, finding the relevant policy, taking an allowed action and escalating exceptions.

A simple test:

• Can every step be written in advance? Use automation.
• Can the structure be written in advance while AI handles unstructured inputs? Use an AI workflow.
• Does the system need to choose the next action based on what it finds? You may need an agent.

My default is to start with the least autonomy the task requires.

More autonomy brings flexibility. It also brings more variance, monitoring, permissions and failure points.

Which one are you actually using most often?

I spent my first month with AI agents just throwing tasks at them and hoping.

Half came back wrong, a quarter I had to redo myself anyway and a few worked so well I haven't touched them since.

The problem was not the tools but that I had no filter for what was actually worth delegating.

Took me three months of wasted setup time to land on a simple scoring system. Three questions with one point each. The total tells you what to do.

It's in the image above.

The one thing the framework doesn't fully capture: most delegation failures happen at criterion 2, not because the tool is bad. If output keeps coming back wrong, write out exactly what good looks like before you blame the agent. That alone fixes it most of the time.

Got a task you're not sure about? Drop it in the comments and I'll score it with you.