Ask ten AI researchers what AGI means, and you'll get twelve different answers. It's a mess. Most people are stuck debating 'human-level intelligence' or consciousness. But here's the reality - vague definitions keep us waiting forever. I got tired of waiting for some magical moment when OpenAI or Google announces 'We did it! AGI is here!' while the rest of us just watch. If you want to actually capture value in this market, you have to stop philosophizing and start engineering. We need to replace these fluffy concepts with something radical: an operational, testable definition that lets us build right now.
The status quo is broken
Let's look at the status quo. The Turing test? That just measures whether a machine is good at deception. That's not intelligence - that's a parlor trick. When we rely on these philosophical benchmarks, we trap ourselves in stagnation. We treat AGI like a distant religious event rather than an engineering problem.
The game has changed, but our definitions haven't caught up. If you're waiting for a model that 'feels' human, you're missing the point. You're letting semantic debates paralyze your ability to execute.
I realized this when I looked at my own roadmap. I couldn't orchestrate a strategy around a concept nobody could agree on. The question isn't 'is it alive?' or 'does it think like us?' The question is 'what can it do reliably, and how does it scale?' We need to flip the script. Instead of asking what AGI is, we need to define what AGI does in a way that we can test, measure, and verify in code. This shift from philosophy to operations is the only way to take ownership of the technology rather than being a passive consumer of it.
An operational definition
So, what does an operational definition look like? Here is the framework I use to build systems that actually work.
First, it must be a self-improving, opinionated system. It's not a blank slate - it has a perspective on how to solve problems.
Second, it needs a complete memory stack. I'm talking about distinct episodic, semantic, procedural, and working memory. Most LLMs today just have a context window - that's not enough. Real intelligence requires remembering the past (episodic), understanding facts (semantic), knowing how to do things (procedural), and holding current tasks in focus (working).
Third, it acts and optimizes toward specific goals. It doesn't just chat; it executes. And critically, it scales up.
When you use this definition, you stop asking if GPT-5 will be AGI. Instead, you look at your current agent architecture and ask: 'Do I have procedural memory implemented? Is this system self-improving?' This is high signal. This allows you to build sophisticated, agentic workflows today that outperform the 'magic' models everyone else is waiting for. You don't need to wait for the future - you can engineer it.
Building operational AGI today
The era of theoretical AI is over. It's time to get your hands dirty. At Ability.ai, we don't wait for AGI to arrive - we build the architectures that make AI agentic and operational today. If you're ready to move from philosophy to production, let's talk about how to orchestrate this for your business.
Building testable systems
This allows you to build sophisticated, agentic workflows today that outperform the 'magic' models everyone else is waiting for.
Engineering the future
At Ability.ai, we use these practical principles to orchestrate secure, autonomous AI agents that actually deliver ROI.