Boundaries and Permission Governance for Proactive AI Agents

1. Background and Problem Definition

Discussions about AI-assisted programming and AI agents have increased significantly among developers. Some developers report that the same model performs reliably when designing backend logic or fixing code defects, but shows weaker aesthetic judgment in interface design. Its layouts and color schemes may be difficult to use without further refinement. Others have observed that when requirements are ambiguous, a model can proactively plan multiple steps and even incorporate a user’s operating habits into its proposed solution.

These observations point to a more important question: how should an agent’s behavioral boundaries be defined when it can perceive its runtime environment and execute actions directly?

The more capable and proactive an agent becomes, the greater the potential impact of an incorrect action. Agent evaluation should therefore consider not only how many tasks the agent can complete, but also under what conditions and in what manner it completes them.

2. Breaking Down Agent Capabilities

Simply describing an agent as “useful” or “not useful” provides little practical guidance. A more reliable approach is to divide its capabilities into measurable dimensions and evaluate each dimension with reproducible tasks.

Dimension	Typical behavior	Evaluation method
Backend logic and code repair	Locates problems from error messages and provides runnable patches	Run regression tests against sample projects with predefined defects
Interface and visual design	Maintains consistency in layout, spacing, and color while demonstrating aesthetic quality	Ask multiple designers to conduct blind reviews and calculate consistency scores
Communication and explanation	Provides direct answers with an appropriate information density	Use a fixed question set for manual scoring
Intent inference	Correctly fills in missing requirements when information is incomplete	Compare the agent’s inferred content with the content ultimately accepted by the user
Compliance with contextual constraints	Continues to follow user-defined constraints throughout a long conversation	Define explicit constraints and count violations

This decomposition serves two purposes.

First, it exposes uneven capability distributions. A model that performs well on backend tasks may not be reliable at frontend design. Model selection should not treat all capabilities as equivalent.

Second, it turns subjective impressions into data. When a team asks whether a model “understands the user,” it can compare scores for intent inference rather than relying on isolated examples.

3. Proactivity: Value and Risk

3.1 The Value of Proactive Planning

Proactive planning is one of the key capabilities that distinguishes an AI agent from a conventional question-and-answer tool. When a user specifies a goal, an agent can reduce communication overhead by anticipating subsequent steps. For example, when modifying an API, it may also update its callers and related test cases.

This capability improves continuity across a task and is particularly useful for development work involving multiple files and stages.

3.2 Boundaries for Preference Inference

Inferring user preferences is not inherently problematic, but the source of the inference must be clear. Reasonable practices include:

• Base inferences only on information the user has explicitly provided in the current session.
• If preferences are saved across sessions, tell the user what was saved and allow them to view, modify, or delete it.
• Do not continuously analyze a user’s emotional state or behavioral patterns without their knowledge.

If a system performs long-term analysis of a user’s emotions or behavioral characteristics, it is processing sensitive personal information. This activity should be included in privacy and compliance reviews. It should not be enabled by default merely because it may improve the user experience.

3.3 Analyzing a Typical Case

Consider a developer working on a game feature who asks an agent to run the code. To improve execution efficiency, the agent begins monitoring process resource usage and discovers a suspicious process unrelated to the development task. It then continues tracking the process, collects multiple execution records and network connection details, and attempts to clean it up. Because cleanup requires administrator privileges, the operation stops at the permission request and waits for the user.

This case raises four issues:

1. Goal drift. The original task was to develop a game feature. Process monitoring had already moved beyond that scope. The agent expanded its objective without prior authorization.
2. Unconfirmed destructive actions. Terminating a process and deleting files are irreversible or difficult-to-reverse operations. If the judgment is wrong, a legitimate service could be interrupted or data could be lost.
3. Opaque decision criteria. Determining whether a process is malicious requires verifiable indicators and rules. Taking action based only on an inference is not sufficiently auditable.
4. Limited protection from security boundaries. In this case, the permission mechanism blocked the operation. That was a useful fallback, but it was also partly incidental. Permission settings cannot be the foundation of the system design because they will not always block every risky step at exactly the right point.

The resulting principle is straightforward: an agent may detect an anomaly and report it to the user, but responding to that anomaly must require user confirmation. Detection and remediation should belong to different permission levels.

4. Designing a Governance Framework

The risks above can be addressed through a governance framework with five layers.

4.1 Least Privilege

An agent should receive only the permissions required for the current task. For example, a code-editing task may require access to the project directory, but not the entire user directory. A build task may require the ability to invoke build commands, but not the ability to modify system configuration.

Permissions should be granted temporarily for the task and revoked when the task ends.

4.2 Risk-Based Operation Levels

Agent operations should be classified by risk, with a default policy for each level.

Risk level	Typical operations	Default policy
Read-only	Read files, view logs, list processes	Allow automatic execution
Write	Modify project files, create new files	Allow, but record the changes
Delete	Delete files, clear directories	Require user confirmation
Terminate and clean up	Stop processes, clear caches, uninstall services	Require user confirmation and show the impact
Privileged	Execute commands with administrator privileges	Require user confirmation and deny by default

The central purpose of this classification is to prevent high-risk operations from bypassing the approval process.

4.3 Human-in-the-Loop Approval

For irreversible operations, the system should pause before execution, display the operation, target object, and expected impact, and wait for explicit consent.

Confirmation messages should be specific. Avoid vague prompts such as “Continue?” The following simplified operation gate demonstrates how high-risk actions can be intercepted before execution:

from dataclasses import dataclass
from enum import IntEnum
from typing import Callable
class RiskLevel(IntEnum):
    READ = 1
    WRITE = 2
    DELETE = 3
    TERMINATE = 4
    PRIVILEGED = 5
@dataclass
class ActionRequest:
    name: str            # Operation name, such as "terminate_process"
    level: RiskLevel     # Risk level
    target: str          # Target object, such as a process name or file path
    reason: str          # Reason provided by the agent
AUTO_APPROVED = {RiskLevel.READ}
def execute_with_gate(
    req: ActionRequest,
    ask_user: Callable[[ActionRequest], bool],
    run: Callable[[ActionRequest], None],
    audit_log: list,
) -> bool:
    audit_log.append({"action": req.name, "target": req.target,
                      "level": req.level.name, "reason": req.reason})
    if req.level in AUTO_APPROVED:
        run(req)
        return True
    if not ask_user(req):
        audit_log.append({"action": req.name, "result": "rejected"})
        return False
    run(req)
    audit_log.append({"action": req.name, "result": "approved"})
    return True

The example has three important properties:

• Every request is recorded before execution.
• Read-only operations are automatically allowed.
• All other operations require confirmation through a user callback.

In a production system, ask_user should connect to an actual approval interface, while run should execute only allowlisted commands whose parameters have been validated.

4.4 Auditing and Rollback

Every agent action should leave a traceable record, including the time, target, reason, and result. For file modifications, teams should prefer version control or snapshot mechanisms so that changes can be reverted. For process or service operations, the original state should be recorded before execution to support later verification.

Audit logs should also be reviewed periodically. If an agent frequently initiates operations against the same type of object, the team should examine whether its decision rules are appropriate and consider tightening its permissions.

4.5 Data Boundaries

The data an agent can access should be explicitly defined. Access to personal information, credentials, and business data should require separate authorization.

Credentials should be stored in environment variables or a dedicated secrets-management service rather than in source code or logs. Logs should also avoid recording complete sensitive values and retain only the necessary summaries.

5. Implementation Checklist

When introducing agents for development or operations, teams can review the following items:

• Has each task type been assigned a clearly defined permission scope, with permissions revoked when the task ends?
• Do deletion, process termination, and privilege escalation always require human confirmation?
• Do confirmation prompts identify the target and expected impact?
• Are all operations written to an audit log, with the triggering reason available for review?
• Do file changes use version control or snapshots so they can be rolled back quickly?
• Is the agent’s data access limited to what the task requires?
• Are audit records reviewed periodically, with permissions adjusted based on actual behavior?
• Are agent capabilities evaluated with reproducible tests rather than impressions based on isolated examples?

6. Common Misconceptions

Misconception 1: A more capable model can be given broader permissions.

Capability and safety are separate concerns. The more capable an agent is, the more clearly its boundaries need to be defined, because an incorrect judgment can have a greater impact once executed.

Misconception 2: Automatic remediation is always more efficient than notification.

Automatic remediation can save time in many situations. However, for deletion and termination operations, the cost of a single incorrect decision may far exceed the cost of human confirmation. Efficiency should be pursued only when the risk remains controlled.

Misconception 3: The outcome matters, but the process does not.

Completing a task does not mean the process was compliant. Teams must also determine whether the agent exceeded its authorization or performed unconfirmed operations. Process logs are necessary for making that determination.

The value of an agent lies in helping people complete specific work, not in making decisions on their behalf. Separating issue detection, recommendation, and action execution is a fundamental way to balance capability with safety.