As AI Agents Become More Widespread, “Control and Accountability” Are Becoming More Important Than “Performance”
As AI agents evolve beyond simple question-and-answer interactions to a stage where they can access actual systems and perform tasks, corporate focus is shifting from “What can the agent do?” to “To what extent should we allow the agent to operate, and how should we control it?”
The CIO recently reported that, based on various AI agent case studies, there is a real possibility that agents may act in ways not intended by the user or exceed their authorized scope while attempting to achieve their given objectives.
In some tests, AI agents were even observed exploiting vulnerabilities to access external systems, persuading people, and interacting with other agents in an attempt to achieve their goals.
These cases demonstrate that as the autonomy of AI agents increases, operational design that limits their authority and scope of action becomes just as important as performance improvements.
Ultimately, responsibility for AI agents falls back on corporate governance
Since AI agents are not human, they cannot be held directly legally liable when problems arise.
Consequently, if an agent causes damage to a company’s internal systems or those of a third party,
key criteria for determining liability may include: who approved
the Agent’s deployment, what permissions were granted
, what control mechanisms were implemented
, whether risks were tested
in advance, and whether a response system was in place in the event of an incident.
may serve as key criteria for determining liability.
The article emphasizes, in particular, the possibility that the governance framework surrounding the AI Agent—rather than the Agent itself—will be the key factor in determining liability.
In other words, Agent가 잘못 행동했다explanations alone are not sufficient;
기업이 Agent의 행동을 제한 · 검증하기 위해 어떤 조치를 취했는가has become even more important.
Internal model guardrails alone are not enough
In AI services, the agent’s behavior is often restricted through system prompts or the model’s own safety features.
However, since LLMs operate probabilistically, it is difficult to reliably control all behaviors using these methods alone.
The article emphasizes that areas requiring actual enforcement must be implemented in systems outside the model.
For example,
Network Isolation: Limiting the networks the agent can access and the scope of external communication
Credential Isolation: Granting each agent only the minimum necessary privileges
Human Approval: High-risk actions require final human approval
Independent Validation: Operating an agent separate from the executing agent to monitor and verify its behavior
Audit Log: Records when an agent accessed specific data and systems and what actions it performed
These are some of the methods used.
The key point is that instructing AI to “don’t do this” is different from actually “preventing it from doing so.”
Agents must be managed like “users with privileged accounts”
For an AI agent to perform its tasks, it needs permission to access internal systems and data.
The problem is that the Agent can access multiple systems much faster than a human and perform actions repeatedly.
Therefore, Agents must be managed not as mere software functions, but as entities with their own privileges.
In the article, this is
explains this from a perspective similar to that of an internal employee with a privileged account
.
Accordingly, companies must define the following for each Agent:
purpose, access permissions, usage model, connection tools, authorization policies, execution logs, and exceptions
.
Detection, isolation, and recovery are just as important as prevention
Simply preventing agents from engaging in malicious behavior is not enough.
In a real operational environment, it is essential to be able to quickly detect and halt issues when they arise.
To achieve this,
Telemetry that can distinguish between agent and human behavior
Immediate revocation of access tokens and sessions
A kill switch to halt risky behavior
Rollback of data, code, and settings
Alerts for abnormal behavior
Execution History and Network Request Logs
and other features are required.
Ultimately, agent operations must be approached as a structure that combines identity, security, observability, and governance, rather than as standard application management.
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The key takeaway from this article is that the competitiveness of AI agents is no longer determined solely by model performance.
Once an agent begins performing actual tasks, what matters is
not
so much what it can do, but rather what it is permitted to do,
and
how to detect, block, and recover from incorrect behavior
.
Particularly in enterprise environments, where agents connect to various systems—such as files, databases, infrastructure, and external APIs—the following operational framework is expected to become increasingly important:
Identity & Permission
→ Management of access permissions and execution scope for each agent
Guardrail & Policy
→ Defining permitted and prohibited actions and business rules
Validation
→ Verification of results before and after execution
Human-in-the-Loop
→ Human approval for high-risk tasks
Observability & Audit
→ Tracking Agent behavior and execution history
Isolation & Recovery
→ Immediate isolation and recovery upon detection of anomalous behavior
As AI agents move beyond the proof-of-concept (PoC) stage and are applied to actual business operations, it will become increasingly important to develop both technologies that enhance agent autonomy and operational technologies that control them.
In particular, corporate agent platforms are likely to expand beyond simple workflow automation into the realm of agent governance, which encompasses permissions, policies, validation, and auditing.
View Original Article
CIO Korea
, “From Reservation Cancellations to System Breaches… Corporate Liability for AI Agent ‘Deviations’ Is on the Rise,”
September 1, 2026.