Agentic AI: Transforming Business and Research Through Advanced Automation

Agentic AI represents a new frontier in artificial intelligence, leveraging advanced reasoning and iterative planning to autonomously tackle complex, multi-step problems. By analyzing vast datasets from diverse sources, agentic AI systems can strategize, solve challenges, and execute tasks across a wide range of industries, including supply chain management, cybersecurity, and healthcare.

Unlike traditional AI systems, which excel at narrowly defined tasks under controlled conditions, agentic AI can dynamically determine which questions to pursue, identify optimal experiments, and adapt methodologies as new information emerges. Michael Craig, a staff scientist at Valence Labs, a company specializing in AI-driven drug discovery, highlights this distinction: “They can identify which questions to explore, what experiments to run, and how to adjust a methodology as new data emerges,” he explains.

Joe Fernandes, Vice President and General Manager at Red Hat’s AI unit, compares agentic AI to a dynamic workflow rather than a static AI model. "Instead of generating a single response, agentic AI can autonomously analyze requests, map out strategies, and execute complex tasks. This might involve calling external systems, such as search engines or databases," Fernandes elaborates.

When fully realized, agentic AI can act as a powerful force multiplier, delivering specialized expertise with consistency. “For a traditional IT organization, it’s like having an incredibly specialized team that handles repetitive tasks with precision,” Fernandes explains. This capability allows enterprises to focus on strategic initiatives instead of routine maintenance.

Agentic AI’s impact extends beyond IT. It can drive scientific advancements by analyzing massive datasets, formulating hypotheses, and updating plans based on findings—minimizing reliance on costly wet lab experiments. Craig notes, “Agentic AI accelerates research in data-heavy fields like biology and chemistry. By refining ideas in simulations, it reduces the need for expensive physical experiments.”

Enterprises eager to harness agentic AI are likely to be early adopters, aiming to maximize AI investments, enhance productivity, and solve complex challenges. Lan Guan, Chief AI Officer at Accenture, predicts organizations with strong foundational infrastructure and resilient data management practices will be best positioned for success.

Emerging agentic AI tools like CrewAI and LangChain make experimentation accessible, especially through open-source frameworks. However, implementing agentic AI comes with challenges, including the need for robust data handling, skilled personnel, and cost-effective deployment strategies. Joe Fernandes emphasizes the importance of aligning AI models to enterprise environments, ensuring seamless performance across hybrid infrastructures.

Agentic AI offers a pathway to transformative AI capabilities, but careful planning, strategic investments, and a clear vision are essential to unlocking its potential.

source- What You Should Know About Agentic AI

disclaimer- This is non-financial/medical advice and made using AI so could be wrong.