The Automation That Bit Back
A founder messaged me the other day — pumped. He’d just built a slick automation: ChatGPT writes content, and it auto-posts to LinkedIn, Facebook, and Medium. Full hands-off publishing.
“No more content bottlenecks,” he said. “This thing writes and ships for me while I sleep.”
Until it didn’t.
One morning, he woke up to an absolute mess: a half-baked, off-brand, error-filled post — published across every channel.
The problem? No quality check. No review step. Not even a basic sanity filter. ChatGPT wrote it, and the system posted it — no questions asked.
It was automation, not intelligence.
Why That Matters
Most founders are getting comfortable with automation tools. They know how to trigger an action when something happens. They’ve built a few flows. Maybe even connected GPT to do some writing or summarizing.
But automation only gets you so far.
It’s great at repeating instructions. It’s terrible at knowing when something’s off.
What this founder really needed wasn’t just automation. He needed a system that could evaluate its own output. That could say, "This looks wrong. I should fix it before publishing."
That’s where AI agents come in.
So, What Is an AI Agent?
Think of it like this:
- A chatbot gives you an answer.
- A traditional automation follows a trigger.
- An AI agent finishes the job — even when it gets messy.
An AI agent is software powered by a language model like GPT or Claude that can reason, take action, and keep iterating until a goal is met. It doesn’t just run a script — it thinks through a problem and adapts as it goes.
And unlike classic automations, an agent can decide what to do next based on the result of its previous step.
Real-World Example: Scheduling with Context
Let’s say you want to schedule a meeting with a client.
A typical chatbot might say: “Sure, when would you like to meet?” A Make.com automation might find an available time and send a link.
An AI agent will:
- Check your availability.
- Look up the client's timezone.
- Avoid lunch hours.
- Propose three reasonable times.
- Draft the email.
- Notice if there’s no reply after two days.
- Follow up.
And it will do all of this without needing you in the loop.
The Core Capabilities
To understand what makes AI agents different, you need to understand how they work under the hood:
1. Reasoning
They use LLMs to break down tasks, plan next steps, and make decisions based on the situation. It’s not just "if this, then that" — it’s "given what I know, what makes the most sense to do now?"
2. Action
Agents can call APIs, update spreadsheets, send emails, move files, or trigger workflows. They don't just respond with text — they do things.
3. Iteration
If the task fails or the result doesn’t meet expectations, the agent can try again. Adjust the prompt, switch the tool, or change the strategy — without starting over.
4. Retrieval
Using a method called RAG (Retrieval-Augmented Generation), agents can search your files, docs, or database before making decisions. This gives them up-to-date context.
5. ReAct Loop
Many agents use the ReAct framework: Reason, Act, Observe, and repeat. This allows them to reflect on what just happened before moving forward.
So How Is This Different from Automation?
Traditional automation is linear. Trigger → Action. Maybe a couple of conditionals.
It doesn’t handle uncertainty well. It doesn’t adapt. And it definitely doesn’t ask, "Did that actually work?"
An agent does.
It’s not just following steps. It’s trying to achieve a goal.
That goal might be: "Write a post that sounds like me." Or: "Schedule a meeting next week that actually fits." Or even: "Only send this email if it makes sense based on what the client said."
Why Founders Should Care
When you're running lean, most systems depend on your judgment. Not just to make decisions — but to notice edge cases, catch failures, and fix what automation misses.
AI agents are your way out.
They let you scale yourself without hiring. They take the repetitive, low-leverage decisions off your plate. And they do it with more context-awareness than any automation tool you've used so far.
Common Mistakes When Building Agents
Most people get excited and skip straight to prompting. They wire up GPT to a tool and expect magic.
What they get is something that kind of works until it doesn’t — like our founder with the autoposting disaster.
Here’s what trips people up:
- No validation of results
- No memory or state tracking
- No error handling or fallback
- No separation of reasoning and action steps
That’s why most AI-coded agents feel more like demos than real products.
What to Do Instead
If you're building or thinking about building an AI agent, start here:
- Define the outcome clearly. What does success look like?
- Decide what tools the agent can access. Calendar, email, CRM, etc.
- Plan for things going wrong. How should it recover?
- Add checks and balances. When does a human need to be involved?
- Test like crazy. Agents make decisions. Bad decisions are expensive.
Final Thought
You can think of an AI agent like a new team member. It’s fast, tireless, and can handle chaos — but only if you train it right.
The best agents don’t just execute tasks. They understand what “done right” looks like.
And that’s what separates automation from intelligence.