AI isn’t just answering questions anymore.
It’s doing the work.
In this post, I’ll explain what AI agents are (in plain terms), why treating them like tools can backfire, and how smart teams are managing AI like digital teammates—with real tasks, goals, and results.
What’s an AI Agent, Really?
You’ve used chatbots before. Maybe even a GPT.
But AI agents are a step further. They don’t just respond—they act.
Agents can:
- Onboard a new hire
- Approve an expense
- Send a follow-up email
- Pull and summarize reports
They work toward a goal, follow rules, and can make decisions.
Think of them like interns: they need instructions, access, and check-ins.
Where Teams Go Wrong
A lot of companies test AI by running one-off pilots.
- HR builds one bot
- Finance builds a different one
- No coordination, no tracking, no shared logic
This leads to:
- Messy data
- Duplicate work
- Low trust in results
What Works Better
Treat your AI agents like junior team members:
- Give each one a clear role (e.g. onboarding, reporting)
- Set permissions so they only access what they need
- Track KPIs like time saved, error rate, task completion
- Use a central system to manage them all
Example: Workday runs multiple agents—HR, payroll, compliance—under one orchestration layer. It’s organized, trackable, and scalable.
TL;DR
- AI agents are more than bots—they take action
- Isolated experiments = chaos
- Central tracking = trust and ROI
- Manage them like people: roles, access, feedback
Want a Simpler Way to Learn AI?
I built a custom GPT called 100 Days with AI.
It first asks:
- What’s your current role?
- What’s your skill level with AI?
Then it helps you learn AI with short, practical steps based on what you do.
Marketing? Ops? Tech? It adapts.
Check it out here → https://chatgpt.com/g/g-688c44b9869c81919d0374b0078d9f29-100-days-with-ai
And let me know what you think!