First, What Are AI Agents?

AI agents aren’t just chatbots.
They’re systems that take action — sometimes simple, sometimes complex — based on goals, data, and feedback loops. While a chatbot answers questions, an agent follows instructions and drives tasks to completion.

Some examples of what agents can do today:

• File tickets in issue trackers

• Merge pull requests

• Run test suites

• Trigger alerts

• Provision new environments

These aren’t theoretical. These are real use cases being tested or deployed by teams working with large language models and automation frameworks.

When Are Agents Actually Useful?

Agents aren’t magical. They work best in very specific scenarios:

• Tasks are complex but repeatable
  Think DevOps processes or internal tooling where steps follow a pattern.

• The context is rich and well-defined
  Agents need to “understand” enough to avoid mistakes. The more structured the input, the better.

• There are clear guardrails
  You need systems in place to monitor and validate what an agent is doing — whether that’s review queues, approval flows, or tight scoping.

Used right, agents can save serious time. Used wrong, they can create noise, errors, or worse — break stuff silently.

Where Agents Fall Short

While it’s tempting to throw agents at every business process, the reality is they still struggle with:

• Ambiguous goals

• Real-time decision-making

• Shifting data models

• High-trust tasks without human review

The tech is improving quickly, but we’re not at full autonomy yet — especially in high-stakes environments like finance, legal, or healthcare.

So, What Should You Do?

Start small.

Pick a task that’s repetitive, safe to automate, and easy to review. Use agents to support your workflows, not replace them outright. The best use cases right now live in internal tools, customer ops, and engineering support.

Over time, you can increase complexity as the systems prove themselves.

Final Thoughts

Agents are more than hype — but only if you apply them with the right intent.

As Will Larson puts it in his post, we’re still early. The tools are promising, but not production-ready for everything. That doesn’t mean you shouldn’t experiment. It just means you should experiment intentionally.

👉 Read the full piece: What Can Agents Do? by Will Larson

Let me know if you want this turned into a downloadable PDF, newsletter format, or adapted for LinkedIn. I can also help with SEO title/meta description suggestions if you’re posting this on a blog platform.