What Makes AI Agents Work: Lessons Beyond the Hype

A product-thinker’s lens on when, why, and how to build them

0. What Is an AI Agent, Really?

Let’s start with a crisp definition from Yage, an AI scientist at a logistics tech company:

To qualify as an AI agent, a system must have:

• Tool use — it can call external APIs or programs

• Autonomous decision-making — it chooses how to act toward a goal

• Multi-step reasoning — it adapts based on intermediate results

That means: not just a chatbot with plugins.
A real agent knows how to explore, adapt, and stop — like a junior analyst who can decide when to dig deeper and when to wrap up.

1. Agents Thrive in Repetitive, Structured Workflows

The best agent use cases aren't wildly ambitious. They’re grounded in repeatable, high-friction tasks.

Take Yage’s internal GitHub Copilot project. Instead of just suggesting code, the agent could:

• Read a GitHub issue

• Fetch relevant files

• Suggest edits

• Open a pull request

But the challenges were real:

• Repos had inconsistent structure

• API interfaces drifted

• Developers didn’t trust auto-generated PRs

• Tasks were often vague or fragmented

Insight: Don’t chase full autonomy.
Agents shine in structured, narrow, repetitive workflows.
Look for tasks like “locate files” or “refactor safely,” not “fix the issue.”

2. Demos ≠ Products: Don’t Fall for the Illusion

Manus had a flashy demo: an agent that searched the web and drafted Notion docs.

It looked magical. Until it wasn’t.

Behind the scenes was a stack of manual prompt hacks, handcrafted demos, and brittle pipelines. When real users came in:

• Web context broke

• LLM output drifted

• Patience wore thin

Insight: The leap from demo to real-world product is huge.
If your agent only works in staged inputs, it’s not a product yet.

3. In B2B, Narrow-Scope Agents Quietly Win

Sara, a logistics firm, built internal agents to help ops teams track inventory anomalies.

It worked beautifully. Why?

• Tasks were clearly scoped

• Data was structured and trusted

• Operators wanted “co-piloting,” not full automation

Think of it like a smart macro embedded in backend systems — not a general-purpose assistant.

Insight: B2B agents succeed in controlled environments
where users want “just enough automation,” not a black box.

4. Generalist Agents Backfire Without Constraints

Xiaoyou from Moonshot let an agent manage internal workflows like onboarding and documentation.

It seemed efficient. But then…

• It wrote misleading how-to guides

• Overwrote correct data

• Wasted everyone’s time

They pivoted to small, domain-specific agents like:

• "Fetch employment policy"

• "Format onboarding email"

Insight: Generalist agents act too boldly and lack guardrails.
Constrain scope, show work, and treat agents like interns — not ops managers.

5. Agents Need Infrastructure, Not Just Interfaces

Many products slap a chat UI on top and call it an agent. But real agents require:

• Memory and autonomy

• Tool and data integration

• Side effects — not just conversations

Even when you do all that, another problem appears: infrastructure.

• No clear way to log actions

• No retry/fallback mechanisms

• No standards for rollout or rollback

As one investor put it:

“Don’t confuse interface innovation with agent behavior.”

Insight: Build observability and resilience before launching agents externally.
Treat agent systems like software, not stage magic.

Should You Build an Agent? Use This 5-Point Check

If you can’t check at least 3–4 of these, think twice.

Think Like a PM, Not a Magician

AI agents are exciting. But the real wins come from:

• Thoughtful scoping

• Clear observability

• Utility over novelty

• Trust over surprise

Don’t try to impress people with sci-fi.
Solve a boring, painful workflow — and do it better than a script could.

That’s how agents go from hackathon demo → to something users actually return to.

Let’s Connect

I’m actively building in this space — real-world LLM agents, vertical automation, UX feedback loops.
If you’re building, investing, or exploring — I’d love to trade notes.