Empowering Intelligence: How Agentic AI Is Reshaping Business and Careers

Adam Sandman categorized AI’s growth into three distinct phases:

Phase 1: Generative AI

• Focused on content creation using LLMs (Large Language Models)
• Tools like ChatGPT enable text-based outputs: blogs, whitepapers, and code snippets
• Revolutionized digital marketing, content writing, and initial programming tasks

Phase 2: Agentic AI

• AI can now perform actions, click buttons, and navigate applications
• Introduction of LAMs (Large Action Models) that mimic human UI interaction
• Common use cases include software development, testing, and workflow automation

Phase 3: System-Level AI

• AI begins operating at the system level using APIs and protocols
• Emergence of frameworks like MCP (Model Context Protocol) and A2A (Agent-to-Agent Communication) to enable cross-platform, multi-agent collaboration
• Future vision includes AI building, deploying, and maintaining software autonomously

From Automation to Innovation: The Practical Use Cases

Agentic AI is actively transforming key domains by streamlining traditional workflows. In software development, AI-driven “vibe coding” allows users to describe requirements in plain language while the AI builds full applications, including frontend, backend, and deployment processes. 

Testing and quality assurance are also being enhanced, with vision models enabling AI agents to scan interfaces, detect design flaws, and adjust test scripts automatically, improving both functionality and usability.

Workflow automation is another area seeing rapid change. Agentic AI agents can now handle routine operations like booking travel or processing reports with minimal human input. 

According to Sandman, these agents do more than automate; they optimize workflows, detect issues, and adapt in real-time. Their ability to integrate with enterprise platforms like Salesforce and Outlook makes them indispensable in modern digital environments.

Architecture and Integration: MCP, A2A, and Beyond

A significant portion of the masterclass focused on how Agentic AI achieves such autonomy. At the core are technologies that allow Artificial Intelligence to interface effectively with systems. MCP, or Model Context Protocol, functions as a set of instructions for AI, helping it understand how to access and interact with APIs, databases, and files. It essentially teaches the AI how to “code” without relying on human developers for every function.

A2A, or Agent-to-Agent communication, is equally crucial. It allows different AI agents to communicate with each other across networks, enabling more distributed and scalable operations. This capability is especially relevant in large organizations where AI agents may be managing separate departments such as HR, finance, and customer service but need to work collaboratively on shared goals.

Together, these frameworks position Agentic AI not just as a tool but as a new layer of the digital workforce capable of independently accessing resources, executing tasks, and collaborating within complex systems.

Ethical Challenges and the Importance of Governance

While the benefits of Agentic AI are considerable, Sandman did not shy away from discussing the associated risks. One major concern is AI poisoning, a process where AI models are trained on manipulated or misleading data. 

For example, an AI responsible for interpreting traffic signs in self-driving cars might be misled into ignoring stop signs if it is trained on faulty images. Prompt injection, a different but equally dangerous tactic, involves feeding malicious prompts into an AI to alter its behavior during inference.

To mitigate these risks, Sandman advocated for robust quality assurance practices, including AI-driven validation systems. One AI checks another, ensuring accuracy, fairness, and compliance. This “AI-on-AI” model of monitoring is vital as humans may not be able to keep up with the speed and complexity of Agentic AI's operations. 

He also emphasized the importance of adhering to international AI governance frameworks, such as those being implemented in the US, EU, and Asia, to ensure secure and responsible use.

Agentic AI is already reshaping several core domains:

1. Software Development

• Developers can use AI agents for “vibe coding” to turn simple prompts into full applications
• The entire UI/UX, backend, and integration logic can be generated from specifications
• Reduces development time from weeks to minutes

2. Quality Assurance and Testing

• AI not only writes tests but also visually inspects UI for design flaws
• Uses vision models to identify overlapping text, poor contrast, or hidden elements
• Automated regression and UI testing ensure software remains stable with rapid updates

3. Workflow Automation

• Repetitive tasks like booking flights, generating reports, or syncing databases can be managed by agents
• AI agents now handle customer queries, HR assistance, and internal approvals

Evaluating Success: Metrics and Measurement

To determine the effectiveness of Agentic AI, organizations must rely on a mix of quantitative and qualitative metrics. Quantitative success might be measured by reduced development time, improved code coverage, or faster turnaround on service tickets. Qualitative metrics include user satisfaction, ethical compliance, and alignment with organizational values.

Sandman emphasized that true quality is not just about whether software “works,” but whether it fulfills its intended purpose. This purpose-driven perspective on quality is increasingly essential as AI-generated applications proliferate in customer-facing environments.

AWS, Bedrock, and Other Ecosystems

Adam also touched on practical tools available today:

• AWS Bedrock: Hosts models from Anthropic, DeepSeek, and others
• Operator by OpenAI: Allows for building autonomous agents using their models
• Open-source and Commercial Models: Developers can choose based on cost, speed, and customizability

A New Paradigm in Development and Deployment

The most exciting implication of Agentic AI is how it reduces barriers to entry for businesses. Sandman demonstrated how an entire enterprise-grade application could be built and deployed in under 30 minutes using Agentic AI. With the ability to revise applications in real-time based on user feedback, businesses can now respond to market demands faster than ever before.

This speed also intensifies competition. Startups can challenge established firms not because they have more capital, but because they can innovate faster and cheaper. However, with this power comes the responsibility to ensure safety, fairness, and accountability in AI deployment.

The Agentic AI Professional Certification from GSDC validates your expertise in advanced AI systems that autonomously make decisions, solve problems, and achieve goals. 

This globally recognized credential from GSDC covers agent architectures, memory models, task planning, ethics, and real-world deployment. Designed for AI developers and innovation leaders, it equips you to build and manage intelligent agents across various industries with confidence.

Adam Sandman’s message was ultimately one of optimism, tempered with caution. Agentic AI is not just another innovation; it is a paradigm shift. Businesses that embrace it thoughtfully, integrating quality control and ethical frameworks, stand to benefit immensely. Those who ignore the implications risk being left behind or, worse, causing harm through poorly governed AI systems.

The takeaway is clear: the future belongs to those who not only use AI but use it wisely. The right combination of human oversight and intelligent automation can unlock levels of efficiency, creativity, and responsiveness never before possible. As Sandman concluded, it's not about resisting change; it’s about ensuring that the change leads to progress, not chaos.