Artificial intelligence (AI) is entering a new phase with the rise of agentic AI – systems capable of acting autonomously to achieve goals rather than simply responding to prompts. Unlike traditional AI models that wait for instructions, AI agents can plan tasks, make decisions, gather information and execute actions with minimal human intervention. This shift has the potential to transform how organizations work, automate processes and interact with information. This interesting but important topic came to us from Forrester in their article, “Build Meaning Before Machines: Why Semantics, Ontologies, And Knowledge Graphs Matter For Agentic AI.”

However, agentic AI does not exist in isolation. Its effectiveness depends on several adjacent and supportive technologies working together behind the scenes.

Large language models (LLMs) provide the reasoning and language capabilities that allow agents to interpret requests and generate responses. Retrieval-augmented generation (RAG) systems help agents access current and authoritative information rather than relying solely on training data. Knowledge graphs, taxonomies and ontologies supply the structured context needed to understand relationships between concepts and improve decision-making.

Cloud computing and edge computing provide the infrastructure required to process data and execute tasks efficiently. Emerging technologies such as digital twins, predictive analytics and robotic process automation further extend agent capabilities by providing real-time insights and automated execution paths. Governance frameworks, cybersecurity controls and data quality initiatives also play a critical role in ensuring that autonomous systems remain trustworthy and accountable.

As agentic AI continues to evolve, its success will depend not only on advances in AI itself, but on the broader ecosystem of technologies that support intelligent, reliable and responsible automation.

Everyone is looking at AI. Everyone is getting mixed results. The main issue is that data science has not changed, and scientific content is very complex and needs more attention to get the most out of the new AI engines. This is not new for Access Innovations.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, uniquely positioned to help you in your AI journey.