#AIAgentFrenzy AI Agents and Knowledge Extraction: The Future of Autonomous Solutions

The concept of AI agents is an exciting glimpse into the future, but right now, it feels more like a pipe dream. Why? Because there’s yet to be a single economically viable AI agent that autonomously solves tasks even at the level of a junior employee in any domain — except maybe in entertainment or crypto-token operations. And let’s face it, those aren’t exactly solid business models. 🧐

It’s crucial to differentiate between AI Automation, AI Assistants, and AI Agents — these terms are often confused, but they’re not the same.

When I talk about an economically viable AI agent for business, I mean one that can autonomously handle tasks at a human level for an entire month and cost less than that person’s salary. If you know of such agents, please share in the comments! For instance, the much-hyped Devin doesn’t even come close to solving half of a junior developer’s workload.

Why Isn’t This Reality Yet? 🤔

The situation may improve soon as:

- Tokens become cheaper.

- Models grow smarter and more specialized.

- Fine-tuned language models emerge for specific domains.

However, there’s a major roadblock: digitizing expertise. For an AI agent to replace a human, it must understand and replicate their skills, but businesses rarely document their processes thoroughly. To create a successful AI agent, this expertise must not only be documented but also structured into formats like:

- Knowledge graphs

- Algorithms

- State machines

A Promising Direction 🌟

Fields like Process Mining and Knowledge Extraction are crucial for advancing AI agent development. Even manually formalizing a specialist’s work — gathering knowledge, structuring decision-making principles, and creating frameworks for knowledge extraction — would be 80% of the work done.

If we go further and combine:

- Domain-Driven Design (DDD)

- Digitized expertise (e.g., JSON or Neo4J database)