I have built around 300 agents, worked at 5 startups. Here’s what I learnt about AI Agent
When I built my first AI agent, it barely worked.
It would get stuck halfway through a task, loop endlessly, and occasionally “hallucinate” an entire project plan that didn’t exist.
Still, that moment changed everything.
Fast-forward to now — I’ve built around 300 agents, from customer-support bots to research assistants to automation systems running entire workflows.
I’ve also worked at five AI startups, each obsessed with building the “perfect agent.”
And here’s the truth no one tells you: AI agents are not magic. They’re messy, fragile, brilliant — and still wildly misunderstood.
1. Agents don’t think. They plan and execute
One of the biggest misconceptions about AI agents is that they think like humans.
They don’t.
What they really do is break problems into steps and call APIs or tools to complete those steps.
Their intelligence isn’t in “reasoning” — it’s in coordination.
Think of them less like a genius intern and more like a hyper-efficient project manager who doesn’t sleep.
Once you understand that, you start designing better workflows — ones that play to their strengths rather than expecting them to “figure it out.”
2. The biggest challenge isn’t the model — it’s the memory
Everyone obsesses over GPT-4 vs Claude vs Gemini.
But in practice, the memory system makes or breaks your agent.
Without context persistence, your agent forgets what it did 5 minutes ago.
With too much memory, it becomes slow and bloated.
I learned this the hard way. One startup I worked at had a customer support agent that was “too smart.” It tried to remember everything — every conversation, every client note — and ended up producing responses that were slow and confusing.
Good agents don’t remember everything.
They remember what matters.
3. Tool use is where the magic happens
An AI agent without tools is like a brain without hands.
Once you let your agent use APIs — like sending emails, reading documents, or scraping data — that’s when it starts feeling alive.
At one startup, we built a “sales assistant” agent that could find leads on LinkedIn, research them via Google, write a personalized email, and log everything in HubSpot.
It didn’t just talk — it acted.
And that’s when you realize: agents aren’t about conversation. They’re about autonomy.
4. 80% of your time goes into debugging
Here’s the part no one glamorizes on Twitter.
Building AI agents is 20% innovation, 80% debugging.
You’ll spend hours chasing invisible bugs — agents forgetting steps, API timeouts, JSON errors, or logic loops.
It’s frustrating. But that’s where you learn.
Each bug teaches you how to design better guardrails — validation layers, retries, context trimming, and feedback loops.
AI agents feel smart, but they still need constant supervision.
Until they don’t.
5. Agents are powerful when combined with humans
The most successful agents I’ve built were never fully autonomous.
They worked with humans — not instead of them.
A research agent drafts reports.
A human reviews and adds insights.
A writing agent generates content.
A human edits the tone.
The best systems are co-pilots, not replacements.
AI agents expand what’s possible, but it’s still humans who steer the mission.
6. You can’t scale chaos
At one startup, we rushed to ship a “multi-agent ecosystem.”
It sounded cool — dozens of agents collaborating on tasks.
But soon we realized: when every agent is autonomous, coordination becomes chaos.
They argued, duplicated work, and sometimes even contradicted each other.
Lesson learned: more agents ≠ more intelligence.
A single, well-designed agent with clear goals outperforms a messy swarm every time.
7. Simplicity always wins
Every time I tried to overengineer — custom memory, dynamic planning, multi-agent orchestration — it eventually broke.
But when I stripped things down to a single clear goal, a few tools, and good feedback, it worked beautifully.
The simplest agents are the most robust.
8. The future isn’t “smarter” agents — it’s connected ones
We’re entering an era where agents will talk to each other — not to replace humans, but to handle the web of small tasks between apps, people, and systems.
An agent that manages your calendar will talk to another that handles your email.
Your CRM agent will coordinate with your billing agent.
The end goal isn’t AGI.
It’s seamless collaboration — between humans and machines, across every workflow.
Final Reflection
After building hundreds of agents, I’ve learned that AI isn’t replacing us.
It’s teaching us to rethink how we work — what should be automated, and what still needs the human spark.
The real revolution isn’t in the agents themselves.
It’s in the people who learn to orchestrate them.
So here’s my question to you:
👉 Are you building agents to replace human work — or to amplify it?
Because the answer to that will define the next decade of innovation.
