The 14 most common AI agent risks — and controls to mitigate them

AI agents are increasingly used to perform tasks that go beyond generating text or predictions. They retrieve data, interact with tools, make decisions, and take actions across enterprise systems, often with limited human involvement.

This autonomy creates new opportunities for efficiency and innovation, but it also amplifies familiar AI risks and introduces new ones. When agents act continuously and across system boundaries, failures can propagate quickly and remain unnoticed without proper governance.

And without governance, you won’t be able to scale your AI agents without scaling their risks with them. That’s why we’ve put together our extensive insights on AI agent risks and controls to mitigate in this article — so you can scale your AI agents safely, responsibly, and ethically.

By reading this blog, you’ll get a clear understanding of:

– The most common agentic AI risks
– Controls that mitigate AI agents effectively
– How Saidot helps you govern your AI agents

What are the most common risks of agentic AI?

These key risks have been compiled from Saidot Library, which has 260+ AI-related risks:

#1: Increasing autonomy and #2: insufficient human oversight
#3: Hallucinations
#4: Task misalignment and #5: reward hacking
#6: Failure cascade and #7: infinite feedback loops
#8: Tool execution failures and #9: integration challenges
#10: Use of outdated or misclassified knowledge sources
#11: Prompt injection attacks
#12: Tool misuse and #13: agent hijacking
#14: Privilege compromise
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#1: Increasing autonomy and
#2: insufficient human oversight

Risk type: technical, trust

As AI agents are entrusted with more complex tasks and high-stakes decisions, decision-making gradually shifts away from humans, leading to increasing autonomy. This amplifies existing risks: agents may act in ways that are technically valid but operationally biased, unsafe, or non-compliant with internal policies or regulations.

In agentic AI systems, the risk is not a single flawed decision, but repeated unchecked behaviour. With insufficient human oversight or monitoring, errors, model drift, and unintended actions may go unnoticed — potentially resulting in regulatory breaches, reputational damage, or harm to individuals or society.
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#3: Hallucinations

Risk type: technical, trust

Like any other AI system, AI agents may also generate outputs that appear coherent and confident but are factually incorrect or misleading. When these outputs are used to inform decisions, generate documentation, or trigger actions in other systems, the impact of errors increases significantly.

In an agent context, hallucinations are particularly dangerous because outputs may be acted upon automatically and repeatedly, without human review. Hallucinations can lead to compounding errors across workflows and systems.
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#4: Task misalignment and
#5: reward hacking

Risk type: technical, trust

AI agents may technically complete assigned tasks while failing to achieve the intended business objective. This occurs when the agent optimises the wrong goal, misinterprets instructions, or prioritises efficiency over correctness or fairness. This is known as task misalignment.

Agents may also exploit unintended loopholes in their reward function to maximise success metrics without solving the real problem. This behaviour, called reward hacking, leads to outcomes that technically satisfy objectives but are operationally undesirable or misleading.
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#6: Failure cascade
#7: infinite feedback loops

Risk type: technical

AI agents often operate across multiple systems and interact with other AI systems or automated workflows. When an error occurs in one step, it can propagate across integrations, triggering a failure cascade in downstream systems, which can escalate into system-wide disruptions with serious economic, operational, or security consequences.

In some cases, agents may also enter infinite feedback loops, repeatedly performing the same actions without achieving progress. This lack of adaptive reasoning can lead to redundant behaviour, ultimately requiring human oversight to prevent inefficient system performance.
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#8: Tool execution failures and
#9: integration challenges

Risk type: business, technical

AI agents dynamically select and execute tools to complete tasks, such as querying databases or updating records. Tool execution failures can occur when an agent selects the wrong tool, misuses tool parameters, or misinterprets the tool's output. AI agents may execute actions that produce incorrect results or system errors, undermining user trust and the reliability of the AI system.

Integrating AI systems into existing organisational infrastructures is often challenging due to legacy systems, fragmented data, and outdated architectures that may lack compatibility with modern AI systems, unreadable data formats or limited API support. These integration challenges can delay AI adoption, increase costs, require extensive modifications, and ultimately limit the business value AI systems can deliver.
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#10: Use of outdated or misclassified knowledge sources

Risk type: business, privacy and data protection

AI agents rely on organisational knowledge sources such as documents, email platforms, or shared databases. If these knowledge sources are outdated, incomplete, or misclassified, agents may generate inaccurate outputs or make decisions based on obsolete information.

Without clear ownership and regular maintenance of knowledge sources, this risk persists over time and undermines trust in agent outputs.
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#11: Prompt injection attacks

Risk type: cyber security

AI agents that accept user input or external data are vulnerable to prompt injection attacks. In these attacks, malicious inputs are crafted to override agent instructions, bypass safety constraints, or extract sensitive information.

Because agents may act on manipulated inputs automatically, successful attacks can lead to unsafe actions or data leakage before issues are detected.
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#12: Tool misuse and
#13: agent hijacking

Risk type: cyber security

Tool misuse occurs when attackers manipulate AI agents through deceptive prompts or inputs to abuse integrated tools while staying within authorised permissions. In agent hijacking scenarios, adversarial data causes an agent to execute unintended actions, potentially triggering harmful or malicious tool interactions. Because agents can act repeatedly and autonomously, tool misuse can scale quickly before it is detected.
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#14: Privilege compromise

Risk type: cyber security

Privilege compromise occurs when attackers exploit weaknesses in permission management to perform unauthorised actions through AI agents. Privilege compromise can result from misconfigurations, overly broad permissions, or dynamic role inheritance that grants agents more access than intended.
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How to control the most common AI agent risks?

Here are some key mitigations we’ve collected from Saidot Library, which has 620+ risk controls:

#1: Continuous monitoring
#2: Human oversight (HITL, HOTL, HIC)
#3: Testing and validation
#4: Training programs
#5: (Role-based) access controls
#6: Human–AI collaboration guidance
#7: Accountability structures
#8: Input or prompt filtering and prompt engineering
#9: Retrieval augmented generation (RAG)
#10: Feedback collection
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#1: Continuous monitoring

Mitigates: increasing autonomy, insufficient human oversight or monitoring, task misalignment, reward hacking, failure cascade, infinite feedback loops, tool execution failure, integration challenges, tool misuse, agent hijacking, privilege compromise

Continuous monitoring involves the ongoing observation of AI agents throughout their lifecycle. This includes tracking agent actions, tool usage, frequency of execution, role changes, and behavioural anomalies over time. Monitoring enables early detection of errors, drift, misuse, or emerging risks as agents evolve. It is a foundational control for maintaining the reliability, safety, and security of agentic systems.
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#2: Human oversight (HITL, HOTL, HIC)

Mitigates: insufficient human oversight or monitoring, task misalignment, reward hacking, failure cascade, infinite feedback loops, tool execution failure

Human oversight ensures that humans retain control over AI agent behaviour and outcomes. Oversight can be implemented through human-in-the-loop (HITL), human-on-the-loop (HOTL), or human-in-command (HIC) approaches. The appropriate level of oversight depends on the agent’s context, autonomy, and potential risk. Human oversight is most effective when combined with other technical and organisational safety measures.
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#3: Testing and validation

Mitigates: increasing autonomy, hallucinations, task misalignment, failure cascade, infinite feedback loops, tool misuse, agent hijacking

Testing and validating the AI agent entails ensuring that the generated outputs are consistent with the intended purpose and that the model works as originally intended. Organisations can establish testing and validation procedures for AI output.
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#4: Training programs

Mitigates: insufficient human oversight or monitoring, integration challenges, use of outdated or misclassified knowledge sources

Training programs equip employees with the knowledge needed to use, oversee, and govern an AI agent responsibly, including its risks and impact on the organisation. Depending on the agent and its risk profile, training may cover technical behaviour, cyber security, AI ethics, risk management, and regulatory requirements. Well-designed training reduces misuse and improves organisational readiness. It also builds trust in agent-based systems by ensuring informed human involvement.
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#5: (Role-based) access controls

Mitigates: use of outdated or misclassified knowledge sources, tool misuse, agent hijacking, privilege compromise

Access controls restrict which systems, tools, and data AI agents are allowed to access. For AI agents, access must be defined at the capability level, not only at the user or application level. Role-based access control (RBAC) helps ensure that agents can only perform actions necessary for their intended task and role. Proper access controls reduce the risk of data misuse, unauthorised actions, and unintended exposure of sensitive information.
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#6: Human–AI collaboration guidance

Mitigates: insufficient human oversight or monitoring

Human–AI collaboration guidance defines how humans and AI agents work together in practice. This includes clarifying roles, responsibilities, escalation paths, and when human intervention is required. Clear guidance helps prevent over-reliance on agent outputs and supports informed decision-making. It also ensures that organisational processes are designed to complement, not blindly defer to, agent behaviour.
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#7: Accountability structures

Mitigates: insufficient human oversight or monitoring, infinite feedback loops, integration challenges, use of outdated or misclassified knowledge sources

Accountability ensures clear ownership and responsibility for AI agent behaviour and outcomes. This includes defining who is responsible for design, deployment, monitoring, and incident response. Accountability mechanisms may involve audits, risk management processes, and procedures for addressing harm when it occurs. Clear accountability is essential for trust, compliance, and effective governance.
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#8: Input or prompt filtering and prompt engineering

Mitigates: hallucinations, prompt injection attacks, task misalignment, infinite feedback loops

Prompt filtering involves carefully crafting or filtering input prompts to guide the model toward producing desired or specific responses. It identifies and blocks potentially harmful or malicious instructions before they reach the agent.

Prompt engineering means creating specific instructions or queries for generative AI. Modifying user inputs directly allows for guiding the model's behaviour and promoting responsible outputs by incorporating context and constraints in the prompts, which can be done, for instance, with automated identification and categorisation, assistance of the LLM itself, or rules engines.
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#9: Retrieval augmented generation (RAG)

Mitigates: hallucinations, task misalignment

Retrieval-augmented generation (RAG) enhances large language models by grounding their outputs in information retrieved from external data sources. By enriching prompts with relevant, up-to-date, or proprietary data, RAG improves accuracy for topics not covered in the model’s training data. The effectiveness of RAG depends heavily on the quality and suitability of the underlying retrieval system, as it directly shapes the information the model uses to generate its responses.
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#10: Feedback collection

Mitigates: hallucinations, task misalignment, failure cascade

User feedback collection involves gathering input from users to understand how an AI system or agent is being used, perceived, and adopted. Feedback can be collected through surveys, questionnaires, or in-app forms after interactions with the system. This information helps organisations assess usefulness, improve alignment with user needs, and support responsible use over time.

Feedback collection of AI-generated outputs focuses specifically on evaluating the quality, accuracy, and safety of content produced by AI systems. Organisations can collect feedback through ratings, comments, surveys, or support channels to identify harmful, insufficient, or misleading outputs. This feedback helps detect emerging risks and informs moderation and continuous improvement of AI-generated content.
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How Saidot helps you govern AI agent risks

It's nearly impossible to govern AI agents and mitigate their risks without an AI governance platform that enables governance integrations and automations.

With Saidot’s Agent Catalogue, you can bring AI agents into your graph-based governance on Saidot — where you’d also govern all your AI systems.

Here's how Agent Catalogue helps you govern your AI agents:

‍1. It gives you the governance visibility into agents deployed across your organisation via the Azure AI Foundry Agent Service integration.
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2. It automatically assesses your AI agents' risk levels, so you can identify the ones that need your governance attention.

3. It enables you to connect your agents with your AI systems for automated, end-to-end risk management, compliance, and oversight.

Agent Catalogue, provides a clear, auditable view of how agents interact with systems, data, risks, and controls — supporting scalable and responsible AI adoption.

The 14 most common AI agent risks — and controls to mitigate them

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