What Executives Need to Know About Agentic AI

What is Agentic AI? 

Agentic AI refers to systems that don’t just analyze or predict; they act. These are models designed to make decisions, carry out tasks, and influence environments with minimal human input. They're not just smart tools. They're autonomous agents with goals, capabilities, and potential impacts that go far beyond traditional automation. 

Why Should Business Leaders Care? 

Executives are increasingly asked to greenlight AI adoption across workflows from operations to strategy. But there’s a blind spot: most decision-makers aren’t aware they’re dealing with agentic systems. They think they’re deploying smarter tools when they’re actually onboarding autonomous decision-makers. 

This matters because agency introduces risk: 

• Agents can act in unpredictable or misaligned ways. 

• They may carry out objectives in ways you didn’t anticipate—or approve. 

• They’re often integrated without safeguards, governance structures, or performance audits. 

If you’re responsible for AI initiatives, cybersecurity, or digital transformation, you need to pause and define the boundaries of agency before it defines your risk exposure. 

How Agentic Systems Work in Practice 

Agentic systems are different from traditional AI models (like simple classifiers or chatbots). They: 

• Set intermediate goals without being told. 

• Use external tools, APIs, or databases to get results. 

• Make autonomous decisions based on perceived progress toward a goal. 

Think about tools like AutoGPT, OpenAI’s Code Interpreter, or open-source autonomous agents. These systems can read instructions like “Create a report,” and then break that down into subtasks, gather data, write, and export a file—without step-by-step human intervention. 

The architecture behind this autonomy often includes: 

• Goal-setting modules 

• Memory or context retrieval systems 

• Multi-step planning or recursive loops 

This kind of recursive, tool-using, self-directed action is what separates agentic AI from your everyday AI assistant. 

Key Business Use Cases (and Why They’re Risky) 

Executives are deploying agentic AI to: 

• Draft contracts 

• Generate technical documentation 

• Write code and deploy it 

• Conduct market research 

• Act as automated cybersecurity monitors 

But the moment your AI can take action without you reviewing every step, you’re operating in agentic territory often without realizing it. 

Risks include: 

• Security vulnerabilities if AI executes commands through connected systems. 

• Legal exposure if AI generates or modifies content with downstream consequences. 
• Brand damage if outputs go live without oversight or quality control. 

The Governance Gap 

Most AI governance frameworks are built for predictive models, not agentic systems. This means: 

• Risk assessments don’t account for autonomy. 

• Procurement reviews skip over intent, agency, or tool use. 

• Audits fail to monitor multi-step workflows. 

• Security teams don’t have visibility into what tools agentic models access or how they use them. 

At Galson, we’re seeing a massive gap in executive understanding, and that gap is a threat vector. 

What Leaders Should Do Now 

If you're a CIO, CISO, CTO, or operational executive, here’s your checklist: 

1. Audit AI use cases for autonomy. 
   Ask: Is this tool taking multiple actions on its own? Is it using external tools? 
2. Define policy boundaries. 
   Set rules for what agentic systems can and cannot do without human review. 
3. Pressure-test deployments. 
   Simulate worst-case scenarios to expose agent failure modes or misaligned outcomes. 
4. Build oversight infrastructure. 
   Set up sandbox environments, use output logging, and enforce checkpoints. 
5. Educate leadership. 
   Make sure everyone understands the difference between predictive AI and agentic AI. 

Agentic AI Isn’t Just a Technical Concern. It’s a Strategic One. 

As AI continues to evolve, businesses that understand the difference between automation and agency will have a massive competitive—and ethical—advantage. 

At Galson, we help executive teams bridge the gap between technical innovation and operational clarity. If you’re ready to lead AI confidently and securely, this is your moment to take the conversation deeper. 

Key Takeaways for Leaders 

What is Agentic AI?

Agentic AI systems are autonomous models that perform multi-step tasks, make decisions, and use tools with minimal human intervention. 

Why it matters:

Executives are unknowingly deploying systems with goal-setting behavior and unintended consequences. 

Next steps:

Audit current use, set guardrails, pressure-test implementations, and build a clear oversight strategy. 

Sources: 

• Open Worldwide Application Security Project. (2024). OWASP Top 10 for Agentic AI. Retrieved from https://owasp.org/www-project-agentic-ai/ 

• Masterman, T., Besen, S., Sawtell, M., & Chao, A. (2024). The landscape of emerging AI agent architectures for reasoning, planning, and tool calling: A survey. arXiv. https://arxiv.org/abs/2404.11584 

• Shavit, Y., Agarwal, S., Brundage, M., O’Keefe, C., Campbell, R., Lee, T., Mishkin, P., Eloundou, T., Hickey, A., Slama, K., Ahmad, L., McMillan, P., Beutel, A., Passos, A., & Robinson, D. G. (2024). Practices for governing agentic AI systems. OpenAI. https://openai.com/research/practices-for-governing-agentic-ai-systems 

• Putta, P., Mills, E., Garg, N., Motwani, S. R., Finn, C., Garg, D., & Rafailov, R. (2024). Agent Q: Advanced reasoning and learning for autonomous AI agents. arXiv. https://arxiv.org/abs/2408.07199 

Originally authored by Susanna Cox. Adapted for Galson Research by our editorial team.