Many organizations are finding it difficult to move Generative AI projects beyond the pilot stage into full-scale production, largely due to concerns around privacy, quality, and cost. As a result, there is a growing shift towards ‘AI agent systems’; a trend that is set to accelerate this year.
An AI agent system enables businesses to build and operationalize an AI agent (an intelligent application designed to automate and enhance human productivity) or set of AI agents that can perform complex tasks by combining multiple interacting components.
An AI agent system goes beyond using a single, stand-alone model to integrate a myriad of components, such as large language models (LLMs), classical machine learning (ML) models and business data and tools, to achieve very specific goals more efficiently.
The rising interest in AI agent systems is no coincidence. Businesses require more than just general intelligence. They need ‘data intelligence’: a new standard of relevance, governance, precision, and trust in their data.
Director, Field Engineering at Databricks.
The rise of AI agent systems to deliver tailored solutions
Unlike general-purpose AI models that aim to answer everything (and sometimes miss the mark), AI agent systems rely on multiple underlying components to deliver a better performance for users, allowing them to simplify or entirely automate very specific tasks and objectives.
The AI agents in the system have a distinct role and are created using specialized LLMs and pre-configured functions. For example, a customer support agent can collaborate with a financial forecasting agent within the same system, but each of them is performing optimally because they’re purpose-built for their domains.
This approach ensures enterprises get solutions tailored to their workflows, customers, and industries—something general models struggle to deliver well. With AI agent systems, it’s not about being ‘all-knowing’; it’s about ‘exactly knowing’.
Eliminating AI uncertainty
Many UK businesses may still fear rolling out new AI projects because of errors, bias, or unpredictable outputs. AI agent systems tackle this head-on by integrating human oversight and AI-based validation mechanisms. Many organizations opt for ‘human in the loop’ grading systems combined with tools that evaluate, cross-check, and refine AI outputs before they’re deployed.
These layers of validation create more trust. For enterprises, this means smoother adoption, greater confidence, and better outcomes.
Laying the groundwork for AI
To build such trusted systems, a robust data foundation is essential. Data is the lifeblood of any AI agent system – we hear this time and again. Enterprises today are racing to become data and AI companies, but the journey isn’t without challenges.
There is pressure to adopt AI, with all stakeholders wanting ‘in’ but few knowing where to start. Data is everywhere, and with fragmented datasets, unifying assets becomes a headache. And lastly, governance and security become paramount as more data can often equate to greater risks.
But despite these challenges, organizations are making strides, often starting with pilot projects that demonstrate ROI before scaling. This iterative approach is a strategic way to build the people, processes, and technology needed to sustain long-term AI transformations.
A key part of successful AI transformations is bringing data intelligence to the forefront. Organizations can do this through modern data architectures—such as data intelligence platforms—which unify, govern, and operationalize data in one place.
With natural language interfaces and private data integration, organizations can build custom models that truly understand their specific needs. These systems empower non-technical employees to more easily interact with data, democratizing AI and accelerating adoption across teams.
In fact, in a recent Economist Impact report, almost 60% of those surveyed anticipate that, within three years, natural language will become the primary or sole method for non-technical employees to engage with complex datasets.
The future of AI is agentic
The future of Enterprise AI lies in building integrated systems of specialized AI agents rather than simply developing ever-larger, standalone models. This shift towards a more interconnected approach enables organizations to address complex challenges with greater trust and precision.
With the right data platform, businesses can design AI agent systems tailored to their specific needs. By leveraging their own data, organizations can create domain-specific AI solutions that deliver reliable, high-quality results. This is made possible through the integration of key technologies, such as vector databases for precise data retrieval, fine-tuning and prompting for specialized reasoning, and monitoring frameworks to ensure safety and compliance.
The AI industry is evolving at an unprecedented pace, with AI agent systems redefining what’s possible. These systems go beyond solving problems; they enhance confidence, create value, and expand AI’s potential. For businesses ready to embrace this transformation, the future of AI is not just about ‘general intelligence’ but a new era of ‘data intelligence’.
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