Data Bites: January edition

Just one month into 2025, and the AI world is buzzing. Dive into Data Bites, a monthly newsletter, for essential updates on breakthroughs, blog posts, and expert insights.

We remain at the forefront of industry innovations, always informed and leading the way in state-of-the-art developments and trends. ??

EDITORIAL

Orchestrating intelligence (or auto-complete): The promise and challenge of AI agents

Christian Miranda | Senior Machine Learning Engineer at Tryolabs

While everyone else out there is debating whether AI will achieve human-level intelligence (or super intelligence), a quiet (or loud) revolution is reshaping how some AI systems actually work. The year is 2025 and we are witnessing the rise of AI agents - systems that go beyond simple input-output standardized system design to actively plan, observe and take action to achieve specific goals.

Although the definition is still converging, at its core, AI agents are autonomous systems that make decisions and interact with their environment using various tools and capabilities. Recent research shows these agentic systems significantly outperform traditional LLMs by using techniques like reflection, planning, and tool use. Their ability to break down complex problems, verify work, and adapt approaches dynamically represents a very interesting shift from traditional software development for the next few years—if it works out.

There is a catch, though. This so-called revolution comes with its own challenges. Through our work with several agent libs and frameworks, we’ve seen how these systems can sometimes propagate hallucinations, creating a cascade of errors that can break entire workflows.

Token consumption also remains a significant concern, especially for more complex agent architectures. And while larger models show promising results, smaller, locally-deployed models still struggle with reliability.

It’s also worth noting that some researchers, like Ilya Sutskever , Yan Lecunn, and Jeff Hawkins do not necessarily believe that agents are the future of AI.

The industry is at a crossroads. Traditional workflows are predictable but inflexible, while agentic systems offer powerful capabilities but require a lot of extra compute, time and careful error management. As processing becomes faster and cheaper, we’re likely to see more sophisticated agent architectures emerge. The future points toward specialized agents - models fine-tuned specifically for “planning”, “reasoning” or tool use, working together in carefully orchestrated systems.

For developers and organizations, the message is clear, understanding and implementing agent-based systems may become a core competency. A wild guess for the near future is: Whether using frameworks like CrewAI, PydandicAI or developing custom solutions, the ability to effectively design AI agents will be as crucial as web development skills were in the 2000s.

The age of active AI is starting (“the future is now old man”). The future belongs to systems that can actively engage with problems, plan solutions, and adapt to changing circumstances. As we move forward, our greatest challenge will be balancing innovation with responsibility. Success will come not from rushing to deploy agents everywhere, but from thoughtfully designing systems that enhance human potential while maintaining transparency and control.

?? Want to read the full newsletter? Subscribe to receive the complete view and future editions directly in your inbox.