From LLMs to SLMs to SAMs: How Agentic Models Are Transforming the AI Landscape

The artificial intelligence (AI) landscape is undergoing a transformative shift, moving its center of gravity from large language models (LLMs) to small language models (SLMs). This transition highlights not only the compactness of these models but also features such as specialization, security, and sovereignty, which are becoming increasingly crucial for enterprise value creation.

The Evolution of AI Models

In recent discussions around generative AI, it has become evident that LLMs and SLMs are evolving into what is now referred to as small action models (SAMs). This innovative concept encapsulates a system of small models that can exhibit agency, enabling them to operate autonomously while aligning with business objectives. With these “S-models” come a harmonization of data, empowering a suite of agents that can work synergistically to deliver substantial business outcomes.

“The collection of these ‘S-models’, combined with an emerging data harmonization layer, will enable systems of agents to work in concert and create high-impact business outcomes.”

As the software industry braces for this shift, organizations are expected to benefit from a new paradigm of productivity. By leveraging a system comprising multiple specialized agents, companies can harness AI’s capabilities in transformative ways.

An illustration of the transition in AI models

Investment Trends in AI

Reflecting on current investment dynamics, research from Enterprise Technology Research indicates a growing confidence in AI technologies among enterprises. Investment in AI and machine learning (ML) has surged from 34% to 50% over the past year—a significant 16-point growth. Notably, AI and ML are now experiencing the highest velocity of spending across all technology categories, even outpacing traditional sectors like container technologies and robotic automation.

Such a dramatic increase in investment underscores the importance businesses are placing on AI capabilities, pointing towards a future where generative AI models are not just tools but integral components of business strategy. The advent of models like Meta’s Llama 3.1, particularly the 405B model, signals a frontier level of advancement most comparable to GPT-4. As these models mature, organizations will need to strategically implement them to maximize both immediate and long-term benefits.

The Shift Towards Multi-Agent Systems

Further complicating this landscape are the implications of implementing multi-agent systems. As businesses pivot from pilot projects to fully integrated AI systems, they must navigate a balance between current returns and their long-term ambitions for comprehensive AI utilization. The framework of agent control emerges here as a critical factor, allowing organizations to leverage diverse agents to align with key performance metrics and business objectives effectively.

Exploring the framework of multi-agent systems in enterprises

Concluding Thoughts

In conclusion, the AI industry stands on the brink of a major transformation, largely driven by new layers of foundational enterprise software. The integration of AI agents and the harmonization of diverse business models promise to redefine productivity within organizations. As we usher in this era of multi-agent collaboration, businesses are poised to achieve unprecedented capabilities that were once thought unattainable.

To navigate this evolving landscape, stakeholders must stay informed about trends in small action models and invest wisely in AI technologies. By doing so, they’ll not only adapt to changes but thrive in the new AI economy.

For further insights and developments, consider exploring related topics on generative AI or keep up with the latest in AI investment trends.