Organisations are struggling to transition Generative AI projects from pilot to full-scale production due to privacy, quality, and cost concerns. This has prompted a new movement towards ‘AI agent systems’ which will only accelerate this year.
Agentic AI is essentially made up of systems that require a specialised, multi-agent approach. In contrast to general AI – which drives popular models like ChatGPT and Claude – which leans on vast, internet-wide datasets to provide broad solutions, AI agent systems take a more tailored route. They’re built on interconnected AI agents, each leveraging unique tools, functions, or large language models (LLMs), and they’re designed to tackle precise, organisational and domain-specific challenges.
And there is a reason AI agent systems are gaining traction. Organisations need more than just ‘general intelligence’. They need ‘data intelligence’ – a new era of relevance, precision, and trust in their data.
Meeting specific organisational needs
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 specialised 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’.
Building trust through validation
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 organisations 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.
The importance of solid data foundations
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, organisations 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. Organisations can do this through modern data architectures—such as data intelligence platforms—which unify, govern, and operationalise data in one place. With natural language interfaces and private data integration, organisations can build custom models that truly understand their specific needs. These systems empower non-technical employees to more easily interact with data, democratising 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.
AI agent systems are the future
Enterprise AI’s future isn’t about building larger, standalone models—it’s about crafting systems of specialised AI agents that work harmoniously. This approach fosters trust, delivers precision, and enables organisations to address their unique challenges.
Organisations can build their own AI agent systems tailored to their specific needs when supported by the right data platform. A robust data platform allows companies to harness their proprietary data to create customised, domain-specific AI agent systems that deliver reliable, high-quality outputs. By combining tools like vector databases for precise data retrieval, fine-tuning or prompting for domain-specialised reasoning, and monitoring frameworks to ensure safety and compliance, organisations can craft intelligent applications uniquely suited to their business goals.
AI agent systems represent a paradigm shift in the adoption of generative AI. These systems do not simply solve problems; but also build confidence, drive value, and redefine the possibilities of AI. For enterprises ready to take the plunge, the next generation of AI doesn’t mean ‘general intelligence’ but instead welcomes a new age of ‘data intelligence’.
Courtney Bennett
Courtney Bennett is Director, Field Engineering at Databricks. With over 18 years in customer-facing roles across multiple disciplines and industries, Courtney has experience in SaaS solutions including big data, customer engagement, digital marketing, AI/NLP, conversational chatbots, customer experience and web analytics.
