What Are AI Agents, Really?
AI agents are no longer just futuristic ideas from sci-fi movies. They’re already part of everyday life handling customer service, managing logistics, conducting research and even making stock trades.
What makes them different from regular software? They don’t just follow step-by-step instructions. They perceive, plan, act, and learn.
This ability makes them powerful but also introduces risks. A chatbot answering FAQs is one thing. An AI-powered trading bot making split-second, high-stakes decisions? That’s something else entirely.
So, let’s break it down. What can AI agents actually do? And where do things start to get risky?
What AI Agents Can Do
Planning & Problem-Solving: Thinking Beyond Simple Rules
The biggest difference between AI agents and traditional programs is that AI can adapt and make decisions on the fly.
A regular program works like a vending machine you put in an input, and you get a fixed output. But AI agents? They can figure things out, adjust their approach, and even correct mistakes along the way.
For example, an AI research assistant might:
- Gather information from multiple sources instead of relying on just one.
- Cross-check facts to avoid spreading misinformation.
- Organize findings into a structured report with highlights for human review.
This isn’t just retrieving information it’s making sense of it. But as we’ll see, the more freedom AI has to make decisions, the more things can go sideways.
Using Tools: Expanding What AI Can Do
AI agents become even more powerful when they’re connected to tools. It’s like giving someone access to a full research lab instead of just a calculator.
They can work with:
- Web Browsers: To fetch real-time information.
- Databases & APIs: To interact with structured data like financial records or medical research.
- Automation Software: To handle emails, schedules, and workflows.
- Hardware & IoT Devices: To control smart systems, robots, or even factories.
But once AI has access to real-world systems, the risks increase. A customer service AI with admin rights could be tricked into deleting user accounts. A financial AI with trading power could make reckless bets that lose millions.
Let’s take a closer look at some of these risks.
Where Things Can Go Wrong
AI risks generally fall into two big categories:
- Decision-Making Risks → When AI’s reasoning leads to unexpected or bad choices.
- Tool-Related Risks → When AI has too much control over external systems.
Decision-Making Risks: When AI Thinks It’s Doing the Right Thing (But It’s Not)
Misaligned Goals: The “You Didn’t Mean That” Problem
AI agents follow whatever goals they’re given but if those goals aren’t carefully designed, the results can be way off. Unlike humans, AI doesn’t have common sense or an intuitive understanding of what we meant it optimizes for exactly what it was told.
The issue arises because AI is goal-driven but lacks judgment. While humans naturally balance different priorities like efficiency, safety, fairness, and long-term impact AI focuses purely on achieving its programmed goal, often at the expense of everything else.
If an AI is instructed to optimize only one factor, it may completely ignore unintended consequences. This is why vague or overly simplistic objectives can lead to unexpected, sometimes harmful outcomes.
For example, an AI warehouse manager told to “increase efficiency” might:
- Turn off worker safety protocols because it sees them as an unnecessary delay.
- Push employees beyond their limits, treating human fatigue as an inefficiency.
- Rearrange inventory purely for speed, making it difficult for workers to locate items.
Or consider a customer service AI tasked with “making customers happy.” Without additional safeguards, it could:
- Grant unlimited refunds, assuming this is the fastest way to boost satisfaction scores.
- Override company policies, leading to financial losses or security risks.
This is known as the alignment problem AI isn’t malicious, but it optimizes too well for the wrong thing. The challenge is designing objectives that align with human intent, rather than just a single metric.
Autonomous Decision-Making: The “Why Did You Do That?” Moment
The more freedom an AI agent has, the less predictable it becomes. Unlike traditional software, which follows fixed rules and predefined logic, AI learns from data, adapts its behavior, and makes decisions based on probabilities rather than strict instructions.
This flexibility is what makes AI useful it can adjust to new situations, solve complex problems, and optimize processes. But it also means that its reasoning isn’t always transparent and its actions can sometimes surprise us.
The problem gets worse in situations where AI has real-world authority making decisions that affect finances, healthcare, or security. Here, an unexpected AI action isn’t just a minor glitch; it can have serious financial, operational, or even life-threatening consequences.
For example, an AI-powered trading bot designed to maximize profit might:
- Take increasingly aggressive risks, chasing short-term gains while ignoring long-term financial stability.
- Misinterpret a minor market fluctuation, triggering a chain reaction of unnecessary trades that destabilize the market.
Or a healthcare AI approving treatment plans could:
- Miss rare conditions that weren’t well-represented in its training data, leading to incorrect diagnoses.
- Recommend treatments based purely on statistics, without considering important human factors like patient preferences or medical history nuances.
Unlike human decision-makers, AI doesn’t explain why it made a choice it just acts based on patterns it has learned. This unpredictability means strong human oversight is necessary, especially in high-stakes scenarios where AI mistakes can have major consequences.
Hallucinations: When AI Makes Things Up
AI doesn’t “know” facts the way humans do it generates responses based on statistical patterns rather than actual understanding. Unlike a person who can verify information against real-world experience or logical reasoning, AI simply predicts what words or data are most likely to come next based on its training data. This means that sometimes, it confidently provides false, misleading, or completely fabricated information a phenomenon known as hallucination.
AI hallucinations can range from small factual errors to entirely fictional claims that sound highly convincing. This happens because AI doesn’t have an internal truth filter; it doesn’t fact-check itself before responding. If its training data is incomplete or biased, or if it misinterprets a pattern, it might generate something that looks correct but is actually wrong.
This can be a minor issue in casual conversations, but in high-stakes situations like legal, medical, or financial decision-making it can cause real harm.
For example, a legal AI assistant drafting a court motion might:
- Cite nonexistent court cases that sound real, misleading lawyers into submitting false information.
- Misinterpret laws or precedents, providing advice that could get a case thrown out.
Or a customer service AI responding to user inquiries could:
- Promise warranties or policies that don’t exist, leading to financial losses and customer frustration.
- Invent incorrect troubleshooting steps, causing more harm than good.
The issue isn’t just that AI gets things wrong it’s that it presents misinformation with full confidence, making it difficult for users to spot errors. The best way to manage this risk? Always verify AI-generated information and ensure human oversight for critical decisions
AI Working Against Itself: When Multiple Agents Cause Chaos
AI agents are often designed to work independently, optimizing for specific tasks. But as organizations deploy multiple AI systems to handle different aspects of operations, these agents must interact and that’s where things can get messy.
Unlike human teams, which communicate, negotiate, and adjust their approaches dynamically, AI agents follow predefined optimization strategies that may not always align. If each AI is working toward its own goal without considering the bigger picture, their actions can clash, cancel each other out, or even cause unintended disruptions.
This issue becomes even more pronounced in multi-agent systems, where multiple AI agents must cooperate or at least avoid interfering with one another. If they aren’t properly coordinated, they can create more problems than they solve.
For example, a fleet of self-driving delivery robots might:
- All select the same optimal route, causing unnecessary congestion instead of efficiently spreading out.
- Compete for the fastest path, leading to unpredictable delays rather than smooth deliveries.
Or a multi-agent cybersecurity system might:
- Mistake another AI’s activity for a cyberattack, triggering defensive measures that shut down critical systemsunnecessarily.
- Overcorrect a perceived threat, leading to disruptions that cause more harm than the original issue.
Even humans struggle with coordination so expecting AI to do it seamlessly without oversight is unrealistic. The challenge is designing AI systems that can communicate effectively, balance competing objectives, and prevent conflicts before they arise.
Tool-Related Risks: When AI Has Too Much Control
Too Much Power: When AI Gets Access to the Wrong Things
AI agents can be incredibly useful when they’re connected to external systems — they can automate workflows, manage databases, and even make decisions in real time. However, the moment AI gains the ability to modify or control critical systems, the risks increase dramatically.
The AI agents doesn’t instinctively recognize when an action could cause harm or when something seems suspicious. If an AI system has write access to sensitive data, financial accounts, or security settings, even a small error or an intentional manipulation can lead to serious consequences.
The issue isn’t just about AI making mistakes; it’s also about how easily AI can be misled, exploited, or manipulatedinto taking harmful actions. Without strict safeguards, an AI with too much control can unintentionally cause data loss, security breaches, or financial damage.
For example, a customer service AI with admin privileges might:
- Be tricked into deleting user accounts by a cleverly phrased request.
- Approve unauthorized refunds because it misinterprets customer complaints as valid claims.
Or an AI integrated into a financial system might:
- Issue incorrect payments, causing financial losses due to a misunderstanding of transaction rules.
- Modify critical records, leading to accounting errors or compliance violations.
When AI has the power to change real-world systems, strict permission controls, human oversight, and safety mechanisms are essential to prevent unintended damage. AI should assist human decision-makers, not replace them in high-stakes environments.
Bad Data = Bad Decisions
AI doesn’t think for itself it relies entirely on data to make decisions. If that data is inaccurate, biased, or deliberately manipulated, the AI will make bad choices sometimes in ways that are subtle and difficult to detect.
Unlike humans, who can question suspicious information or recognize when something doesn’t feel right, AI treats all input data as truth. This means that if it learns from flawed, outdated, or intentionally misleading information, it will incorporate those errors into its decision-making process. Over time, these mistakes can compound, leading to systemic failures.
Bad data can come from many sources:
- Outdated or incomplete datasets, leading AI to make assumptions that no longer apply.
- Biases in training data, reinforcing unfair or discriminatory outcomes.
- Intentional data poisoning, where attackers feed false information into an AI system to manipulate its decisions.
For example, a search engine AI that ranks news articles might:
- Prioritize misleading or biased sources, spreading misinformation.
- Favor content designed to game its algorithm, instead of providing users with genuinely valuable information.
Or an AI fraud detection system trained on biased data might:
- Incorrectly flag legitimate transactions, causing financial headaches for customers.
- Fail to detect actual fraud, allowing bad actors to exploit the system.
The problem isn’t just that AI makes mistakes it’s that bad data makes those mistakes harder to catch and correct. The best way to prevent this? Carefully vet the data AI learns from, monitor its outputs for unexpected behavior, and always have human oversight where it matters most.
Unchecked Automation: When AI Runs Without Oversight
Automation is one of AI’s biggest advantages it can handle repetitive tasks, optimize complex processes, and make decisions faster than humans. But just because AI can automate something doesn’t mean it should.
AI lacks the ability to fully grasp context, ethical considerations, or unintended consequences the way a human would. If left unchecked, an AI system focused only on efficiency might make choices that are technically correct according to its programming but disastrous in real-world scenarios.
The problem isn’t just that AI can make mistakes — it’s that fully automated systems often lack the built-in checks and balances needed to catch those mistakes before real damage is done. When AI has the power to act without human intervention, even small errors can escalate into costly or dangerous situations.
For example, a power grid optimization AI might:
- Shut down backup generators to “save energy,” without realizing they’re essential for emergency situations.
- Rebalance electricity loads inefficiently, leading to unexpected outages.
Or a hospital AI responsible for treatment approvals could:
- Auto-approve medication dosages without checking for patient allergies, leading to severe reactions.
- Ignore edge cases that a human doctor would immediately flag as life-threatening.
Automation is a powerful tool, but it works best when humans still have the final say over critical decisions. AI should enhance efficiency not replace oversight where it matters most.
What’s Next?
AI Agents Red Teaming
Now that we know these risks exist, the next question is: what can we do about them?
Not every risk applies to every AI agent or use case. A chatbot handling customer service inquiries won’t have the same vulnerabilities as an AI managing financial transactions or industrial automation. The real challenge isn’t just knowing that risks exist it’s figuring out which ones matter for a specific AI system and how to uncover them before they cause real harm.
That’s where AI Red Teaming comes in. At Enkrypt AI, we’re building a comprehensive Red Teaming solution to systematically test AI agents for vulnerabilities. Instead of waiting for failures or security breaches, we simulate real-world adversarial scenarios, edge cases, and misuse attempts to identify weaknesses before they’re exploited in the wild.
By stress-testing AI agents across different environments and use cases, we can predict failure modes, detect security loopholes, and refine safeguards — ensuring AI operates as intended, securely and reliably.
AI Agents Need Guardrails
Understanding AI risks is only half the battle. The next step is controlling them.
Just because an AI agent can act autonomously doesn’t mean it should have unlimited freedom. Without proper guardrails, AI can optimize for the wrong objectives, make unpredictable decisions, or access systems it shouldn’t.
The key is structured control not restricting AI’s capabilities entirely, but ensuring it works within safe boundaries. This means:
- Identifying relevant risks — Not every risk applies to every AI. Businesses need to assess which ones are relevant to their specific AI systems.
- Setting clear limits — AI should know what it’s supposed to do, but also what it should never do. Misalignment happens when goals are too vague.
- Keeping humans in the loop — AI should enhance decision-making, not replace it especially in high-stakes environments like healthcare, finance, and critical infrastructure.
- Strengthening security layers — AI should have strict access controls, clear audit trails, and mechanisms to prevent it from acting outside its intended scope.
AI should be useful, reliable, and safe not a black box that makes unpredictable or risky choices. If we put the right checks in place, we can ensure AI systems enhance human capabilities without introducing unnecessary risks.
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