10 posts tagged with "AI Agents"

Ten years ago, "the cloud" was controversial. CIOs debated whether it was safe to put critical workloads on someone else's infrastructure. Five years ago, "AI" was experimental. Companies ran pilots, but production deployments were rare outside tech giants.

Today, cloud is infrastructure. AI is expected. The debates have moved on.

Agentic AI is where cloud and AI were at their inflection points. Right now, it's new and uncertain. In five years, it will be assumed. The enterprises that figure it out early will have compounding advantages over those who wait.

We've made every mistake in the book—and a few that aren't in any book yet.

Some of these mistakes cost us months. Others cost us team members. A few almost cost us the company.

I'm sharing them because the AI agent space is so new that there's no playbook. Every team is learning by doing. If our scars can save you some pain, they're worth exposing.

Every enterprise executive I talk to has the same question about AI agents: "How do I know it won't do something catastrophic?"

It's the right question.

When you give AI the ability to take actions—not just answer questions—you're accepting a new category of risk. This isn't ChatGPT suggesting a response you might send. This is an agent sending that email, modifying that document, executing that code.

I'm going to tell you about the problems that almost killed our project. Not the polished "challenges we overcame" version—the real ones. The bugs that took weeks to find. The architectural decisions we reversed three times. The features we built, shipped, and then ripped out.

If you're building agentic systems, you'll hit these walls too. Maybe this saves you some scars.

The best developer tool is the one you don't have to think about. It should feel like an extension of your hands, not an obstacle course.

When we started building FAOSX, we had a choice: optimize for power users who'd read 50 pages of docs, or optimize for developers who want to ship something in an afternoon. We chose the afternoon.

"Enterprise-grade" is the most overused term in B2B software. Every startup claims it. Few deliver it.

When an AI agent runs in production, it's not just executing code—it's making decisions that affect your business, your data, and your customers. The question isn't "Can this agent do the task?" It's "Can I trust this agent at 2 AM when no one is watching?"

Imagine ten senior engineers working on a complex project. Now imagine they can't talk to each other, don't know what the others are doing, and there's no project manager. That's what most multi-agent AI systems look like today—capable individuals creating chaos together.

The challenge isn't building smart agents. It's making them work as a team.

Ask ChatGPT to review your code, and you get generic feedback. Ask it to review your marketing copy, same tone. Ask it to plan your architecture, still the same voice. The AI is capable—but it's not specialized. It's like having one employee who claims to be an expert in everything.

In the real world, your best architect thinks differently than your best marketer. They use different vocabulary, apply different frameworks, and bring different perspectives. They've spent years developing intuition in their domains.

We asked ourselves: what if AI agents could work the same way?

When we started designing FAOSX, we had a whiteboard full of questions and zero answers.

Should agents be stateless or stateful? How do you coordinate ten agents without creating chaos? What happens when an agent makes a mistake at 2 AM? Where does the "intelligence" live—in the agent, the orchestrator, or somewhere in between?

One of the most common questions I get is: "Is 10x productivity real, or just marketing hype?"

Fair question. Let me show you how we achieve it at FAOSX using FAOS itself.