AI is the new intern—don’t give it master keys!

We have spent the last few years marveling at what artificial intelligence can say. Now, we are entering the era of what AI can do. This is the shift from Generative AI to Agentic AI. While Generative AI has transformed how we create content and code, Agentic AI promises to revolutionize how we execute tasks. 

Imagine a software system that doesn’t just draft a travel itinerary but actually books the flight, reserves the hotel, and adds the confirmation numbers to your calendar. That is the promise of Agentic AI. These systems serve as digital teammates, capable of reasoning, planning, and using tools to achieve goals with limited supervision. 

However, giving software the “agency” to act independently introduces a new layer of complexity for business leaders. It requires a shift from simply managing outputs to managing actions. To truly benefit from this technology, organizations must understand not just the potential rewards, but the specific risks involved in deployment. 

What is Agentic AI? 

Agentic AI refers to autonomous systems designed to pursue specific goals with minimal supervision. Unlike standard chatbots that simply respond to prompts, these agents can break down complex objectives, reason through problems, and adapt based on feedback. 

In a professional setting, these function like specialized digital teammates. For example, a multi-agent system might coordinate one agent for data analysis and another for stakeholder communication. Organizations are already utilizing this technology to streamline customer support and accelerate software development cycles. 

How it differs from Generative AI 

It is helpful to distinguish Agentic AI from the Generative AI tools many of us use daily. 

  • Generative AI (GenAI): Focuses on creating content. It predicts the next word in a sentence or generates an image based on a description. It is excellent at drafting, summarizing, and brainstorming. However, it generally stops at the output. 
  • Agentic AI: Focuses on executing tasks. It uses Large Language Models (LLMs) as a “brain” to understand instructions, but it also has “hands”—integrations with APIs, databases, and other software. It takes the output and does something with it. 

For example, GenAI can write a SQL query for you. Agentic AI can write the query, run it against your database, analyze the results, and alert you if the numbers look wrong. 

The Challenges for Agentic AI Systems 

Transitioning to Agentic AI is not as simple as flipping a switch. Because these systems operate with a degree of autonomy, they present unique operational challenges. 

The primary challenge is predictability. When an AI follows a rigid script, you know exactly what it will do. When an AI is allowed to “reason” and choose its own path to a goal, the outcome can be harder to predict. An agent might find a technically correct way to solve a problem that violates company policy or breaks a downstream process. 

There is also the challenge of complexity. Agentic systems often involve multiple models talking to each other and interacting with external tools. If one agent fails or hallucinates, it can create a domino effect, causing errors that cascade through the entire workflow. 

Major Risks of Agentic AI in Business Settings 

As organizations move these agents from the lab to production, several specific risks emerge. Understanding these is the first step toward secure adoption. 

Identity and Access Control 

In traditional IT, we assign permissions to humans. With Agentic AI, we are dealing with “non-human identities.” An agent needs access to your CRM, your email server, or your financial database to do its job. If an agent is compromised, or if it is granted overly broad permissions, it could access or alter sensitive data at machine speed. Managing the lifecycle of these non-human identities is a critical security task. 

Observability Gaps 

When a standard software program crashes, engineers look at the logs to see what broke. With AI agents, the “logic” isn’t always written in code; it is embedded in the model’s reasoning. It can be difficult to understand why an agent made a specific decision. Did it hallucinate? Did it misinterpret a tool’s output? Without deep observability into the agent’s “thought process,” troubleshooting becomes difficult. 

Tool Misuse 

Agents use tools (APIs) to interact with the world. A significant risk is that an agent might use a tool incorrectly. For example, a customer service agent might accidentally trigger a refund when it only meant to check a balance. If the guardrails aren’t tight enough, an agent could write data to a system of record that corrupts the database. 

Trust and Adoption 

Trust varies depending on the task. Research suggests that while business leaders are comfortable using AI for data analysis or performance improvement, trust drops sharply for high-stakes use cases like financial transactions. If employees or customers do not trust the agent to act correctly, adoption will stall. 

How to Implement the Right Safeguards 

The risks are real, but they are manageable. By adopting a “Responsible AI” approach, organizations can harness the power of agents while keeping their operations secure. 

1. Implement “Human-in-the-Loop” Oversight 

For high-impact actions, full autonomy should not be the default. Establish checkpoints where a human must approve the agent’s plan before it executes. For example, an agent might draft a refund and prepare the transaction, but a human manager must click “approve” to release the funds. 

2. Follow the Principle of Least Privilege 

Do not give an agent the keys to the castle. Assign only the minimum privileges necessary for the agent to perform its specific task. If an agent only needs to read customer records, ensure it does not have write access to delete them. This limits the “blast radius” if something goes wrong. 

3. Run Pilots and Red-Team Testing 

Before a full rollout, test your agents in a controlled environment. “Red-teaming” involves simulating attacks or trying to trick the agent into misbehaving. This helps uncover vulnerabilities—such as an agent being manipulated to reveal sensitive data—before the system faces real customers. 

4. Establish Robust Monitoring 

Monitoring must go beyond simple uptime checks. You need to track the agent’s decisions, tool usage, and outcomes. Look for anomalies, such as a sudden spike in API calls or an unusual pattern of data access. Continuous feedback loops allow you to refine the system and correct drift before it becomes a major issue. 

5. Define Clear Governance 

Create a centralized inventory of your AI agents. You should know exactly which agents are running, who owns them, what data they access, and what their business purpose is. Clear governance ensures that no “shadow AI” agents are operating without oversight. 

Moving Forward with Confidence 

Agentic AI can free your team from repetitive tasks and accelerate innovation, but these digital teammates require strong oversight. By treating AI agents with the same rigor you apply to human employees, you can build a system that is both powerful and trustworthy. 

Navigating the complexities of AI adoption can be daunting. Our team has the expertise to help you integrate these technologies safely. If you’re ready to explore how Agentic AI can work for your organization, Volta’s ready to start exploring with you.