Exploring Agentic AI: Transforming the Future of Intelligent Systems

Wright emphasized the critical distinction between generative AI and agentic AI, a concept often misunderstood in today’s AI conversations. While generative AI, such as large language models (LLMs), excels at creating content (e.g., text, code, images), agentic AI encompasses a broader architecture. It integrates multiple AI models working collaboratively with memory, reasoning, goal-orientation, and decision-making capabilities.

He illustrated agentic AI through the analogy of a beehive: many simple worker agents (the bees) perform individual tasks, coordinated by higher-level control systems to achieve complex goals (producing honey). Unlike generative AI, which focuses mainly on “creating,” agentic AI adds “thinking” and “collaborating,” allowing multiple agents to coordinate and reason over tasks with both short-term and long-term memory.

This coordination enables agentic AI systems to perform complex workflows such as software design, coding, testing, and deployment, all while continuously learning and adapting based on real-world feedback.

The Evolution of AI in Enterprise

Jonathan Wright traces the evolution of AI from early automation and machine learning platforms to today’s generative AI boom, sparked by the introduction of Transformer models around 2017. His team at Eggplant launched one of the first generative AI platforms focused on code generation in 2022 and introduced their first agentic AI platform in 2023.

Highlighting the growing maturity of AI in enterprises, Wright shared examples from industries like finance, healthcare, and manufacturing where agentic AI is transforming workflows:

• Software Development: AI agents collaborate to generate, test, debug, and deploy code autonomously, increasing productivity by up to 20-30%.
• IT Operations: AI agents monitor systems, manage incidents, and optimize network configurations with continuous real-time adaptation.
• Engineering and Manufacturing: Digital twins and neuro-symbolic models enable sophisticated simulation, testing, and automation of physical processes.

However, Wright cautioned that these gains should be focused on workforce augmentation rather than outright replacement. Agentic AI empowers skilled professionals to perform better and faster, widening the gap between junior and senior resources, rather than leveling the playing field for less experienced workers.

Navigating Risks: Governance, Compliance, and Ethics

An important part of Wright’s discussion was AI governance and compliance. With growing scrutiny from regulators worldwide, such as the European Union’s AI Act and U.S. Executive Orders, organizations must implement frameworks like ISO 42001 for AI risk management and assurance.

Wright stressed the need for transparent, explainable AI systems free from cognitive bias and the importance of human oversight in all AI decision-making loops. He also pointed out the severe legal and financial consequences companies may face if their AI systems violate regulations, particularly in regulated sectors like finance, healthcare, and defense.

Diving deeper, Wright described the technical components underpinning agentic AI platforms:

• Model Gardens: A suite of diverse AI models, including large language models, vision models, fine-tuned domain-specific models, and large action models working in concert.
• Memory and Reasoning: Incorporating both short-term (e.g., 8k token limits) and long-term memory to inform decision-making and avoid repeating unproductive actions.
• Computer Use Agents: AI agents capable of interacting with software interfaces autonomously, interpreting on-screen elements through computer vision (e.g., YOLO, OCR), and performing complex tasks like navigating enterprise applications.
• Digital Twins and Knowledge Graphs: Dynamic models representing real-world systems and organizational knowledge to provide context-aware AI reasoning.

One compelling demonstration Wright shared involved an agentic AI platform ingesting thousands of requirements and test cases from Jira and ISO standards to autonomously generate, execute, and fix test scripts, accelerating quality assurance processes in software and hardware projects.

Practical Takeaways for Professionals

Jonathan Wright encouraged professionals to embrace lifelong learning and develop skills as “AI wranglers” experts who manage and orchestrate multiple AI agents and systems. Key skills include understanding AI architectures, mastering prompt engineering, ensuring compliance with AI governance standards, and leveraging domain knowledge effectively.

He also recommended that organizations avoid using public generative AI tools for sensitive intellectual property or regulated data, instead opting for air-gapped, secure AI platforms tailored to their environments.

Looking Ahead: The Agentic AI Economy

The final part of the webinar touched on the broader societal and economic implications. The future of agentic AI 2025 is set to drive a new AI economy where intelligent platforms autonomously manage complex workflows, enabling organizations to innovate faster and operate more resiliently. Professionals and companies that adapt early to this paradigm will gain a competitive edge.

Wright’s closing advice was simple but profound: “Never stop learning.” As AI technologies continue to evolve, staying informed and certified through programs like GSDC’s Agentic AI Professional course is essential for thriving in the future workplace.

Agentic AI Certification is a credential validating the skills required to develop and maintain autonomous AI agents capable of reasoning, adapting, and acting independently. The areas covered include multi-agent collaboration, integration with LLMs, ethical considerations, and automation pipelines-training professionals capable of building intelligent systems capable of performing complex tasks with minimal human supervision.

GSDC is an internationally recognized certification body that offers professional credentials across emerging tech domains like AI, blockchain, cybersecurity, and DevOps. GSDC certifications have been developed in line with industry to ensure practical relevance, employability worldwide, and the current requirements of the job market, thus proving to be an ideal option for re-skilling or up-skilling.

This masterclass provided a rare deep dive into the complexities and opportunities of agentic AI, far beyond the popular buzz of generative AI. Jonathan Wright’s expertise and practical insights equip AI professionals and organizations to navigate the challenges of AI adoption responsibly and effectively.

For those interested, GSDC offers Agentic AI certification programs and lifelong memberships that include access to workshops, case studies, and a vibrant community, ideal resources to stay future-ready in the AI era.