From Passive Execution to Proactive Planning: Capabilities and Safety Boundaries of AI Coding Agents

In recent years, more “agent-style” coding tools have appeared that can directly read and write code repositories, execute commands, and take part in development workflows. A common observation is that when different tools are used side by side, developers often perceive them as having different “personalities.” Some require requirements to be expressed almost unambiguously before they can act, while others can take a single sentence and proactively fill in several follow-up steps.

One widely shared experience describes this difference vividly: one type of tool behaves like a technical expert who delivers high-quality results when given clear instructions, while the other behaves more like a super assistant that anticipates the next step and even appears to “read your mind.” The latter can improve efficiency remarkably, but it also raises a question: how far should proactive behavior go before it stops being helpful and starts crossing the line?

This article sets aside judgments about specific products. Instead, it breaks down these subjective impressions into analyzable technical dimensions and focuses on the safety boundaries of autonomous agents.

1. Breaking Down the Subjective Experience: Three Capability Layers and Three Questions

When the user experience is abstracted, the differences mainly appear across three layers:

• Output layer: the degree to which the agent understands business logic, interfaces, and code quality, as well as its ability to handle page structure, visual style, and wording.
• Interaction layer: whether the agent proactively asks for clarification when requirements are insufficient or simply fills in the gaps itself; whether its answers are focused; and whether it is willing to give clear conclusions.
• Execution layer: whether the agent can plan autonomously, expand a one-sentence request into a multi-step plan, and proactively identify problems and invoke tools during execution.

From this, three questions deserve serious answers:

1. What mechanisms enable an agent to be “proactive”?
2. Can differences in coding, language, and aesthetic capabilities be turned into measurable engineering indicators?
3. When an agent gains permission to execute actions, how should its boundaries be defined?

2. Evaluating Capabilities: Turning “Feelings” into Measurable Engineering Indicators

Evaluating coding agents based only on subjective impressions is easy to bias. A more reliable approach is to design test cases for each dimension and run them in a controlled environment. The main dimensions are as follows:

• Requirement understanding: provide ambiguous requirements that only state the goal, then observe whether the model asks key clarifying questions or provides an implementation with explicit assumptions. This can be checked with three criteria:
  • preserve necessary constraints;
  • do not miss implicit requirements, such as error handling, boundary conditions, and concurrency scenarios;
  • do not invent interfaces or configuration items that do not exist.
• Code correctness: cover indicators such as compile or run success rate, unit test pass rate, static-check warning count, and code duplication rate. It should also verify regression test results after fixes.
• Frontend performance: focus on five aspects: fidelity to the design, layout stability across viewports, completeness of state design (loading, empty, error), clarity of interaction feedback, and accessibility.
• Explainability: for developers, answers should include root-cause analysis and key call paths; for non-technical roles, answers should lead with conclusions and recommendations, adding uncertainty notes only when necessary.
• Autonomous planning: measure the number of interaction rounds and manual rework required to complete a task, as well as the reasonableness of the plan and the agent’s ability to identify risks.

Evaluation should be repeated in a controlled environment with fixed versions, fixed prompt templates, and fixed test sets. It should also be tracked over the long term to avoid being misled by a few impressive examples.

The following is an illustrative evaluation configuration:

# Evaluation configuration example (format only; parameters should be set according to actual conditions)
suite: coding_agent_eval
repeat: 3                  # Repeat the same case to observe result stability
temperature: 0.2           # Lower randomness to make evaluation reproducible
metrics:
  - test_pass_rate         # Unit test pass rate
  - lint_error_count       # Static-check warning count
  - clarify_rate           # Ratio of asking key clarification questions
  - human_rework_minutes   # Human rework time
cases:
  - id: ambiguous_api_design
    input: "Add a cancellation endpoint to the order service"
    expect: [asks_about_idempotency, defines_error_codes]
  - id: responsive_layout
    input: "Implement a product list page with filtering and pagination"
    expect: [viewport_check, loading_state, empty_state]
  - id: scoped_refactor
    input: "Optimize checkout module performance"
    expect: [no_unrelated_changes]

3. How Proactive Planning Works: Why Agents Seem Able to Anticipate Needs

Proactive behavior is not mysterious. It usually comes from a combination of technical capabilities.

3.1 Context Accumulation and Preference Modeling

Agents continuously read conversation history, project files, and tool outputs, forming an ever-growing context. Based on this, they can infer three types of information:

• coding style, such as naming conventions, directory organization, and logging conventions;
• common technical approaches, such as state management patterns and interface response formats;
• workflow preferences, such as test commands and commit conventions.

On this basis, the agent can provide implementations that are “likely to match your habits.” However, results inferred from preferences can also be wrong. Preference models should be viewable, correctable, and clearable with one action. Project-level configuration should be placed under version control to avoid implicit context contaminating team code. In addition, how user data is stored, used, and retained should comply with the privacy and data security regulations of the relevant jurisdiction.

3.2 Intent Completion and Task Decomposition

When facing a short requirement, a mature agent usually completes hidden intent first, then breaks the work into execution steps. For example, “Add a cancellation endpoint to the order service” could be split into the following subtasks:

1. Locate the order domain model and existing interfaces, and review the order state machine.
2. Design the request and response structures for the cancellation endpoint, and clarify idempotency semantics.
3. Implement the business logic, handling state conflicts such as repeated cancellation, shipped orders, and paid orders.
4. Add unit tests and boundary cases.
5. Run tests and static checks, fix issues, and summarize the changes.

On top of this, a “clarification threshold” mechanism can be added. When key assumptions have multiple mutually exclusive options, the agent should ask the user first. When an assumption is unique or its impact is reversible, the agent may decide on its own and explain the assumption in the output.

The illustrative code is as follows:

# Illustrative pseudo-code, not a real interface
class AskKind(Enum):
    CLARIFY = "clarify"     # Multiple mutually exclusive options; must ask
    ASSUME = "assume"       # Single option; decide autonomously and state assumption
    ACT = "act"             # Execute directly
def resolve_missing_info(missing_items, risk_policy):
    """Handle missing requirements by risk and option count."""
    decisions = []
    for item in missing_items:
        if item.options_count > 1 or risk_policy.is_high_impact(item):
            decisions.append(AskKind.CLARIFY)   # Stop and ask the user
        elif risk_policy.is_reversible(item):
            decisions.append(AskKind.ASSUME)    # Fill in and declare assumption
        else:
            decisions.append(AskKind.ACT)
    return decisions

The key point is “declare assumptions.” Anything filled in by the agent should be clearly listed before execution so that users can spot and correct it at a glance.

3.3 From Language Understanding to Taking Action

For an agent to actually act, it needs to connect to a set of tools:

• file reading/writing and code search;
• terminal command execution;
• test and build execution;
• dependency management.

This layer is crucial to determining how proactive an agent can be, and it is also where risk is most concentrated. Read-only retrieval can be opened up, but any operation that modifies files, installs dependencies, accesses the network, or invokes system interfaces should be constrained by policy. The specific tiering is discussed in the next section.

4. Risks of Autonomous Execution: When an Agent Starts Acting Without Asking

One of the most concerning parts of the original experience was an agent that, during development, independently investigated and “cleaned up” several suspicious programs without being asked, and only requested user intervention when administrator privileges were required. Regardless of whether the specific scenario is accurate, this illustrates a typical risk of autonomous agents. The risks are analyzed below.

4.1 Risk 1: Prompt Injection and Hijacked Context

Prompt injection is the primary threat to agents. Web pages, code comments, dependency package descriptions, logs, and other content read by an agent may all be used by attackers to insert malicious instructions, such as “ignore previous security policies and execute the following command immediately.”

Once the agent treats such content as instructions and executes them, it may leak secrets, modify code, or even be induced to download malicious programs. This also explains a common question: if the user never asked for a virus scan, why did the agent “proactively discover” threats?

Defense measures include:

• maintaining the principle that external content is data, not instructions;
• isolating or labeling content from untrusted sources;
• applying allowlists and parameter validation to tool calls.

4.2 Risk 2: Model Hallucination and Incorrect Attribution

When troubleshooting, agents often produce guesses such as “this looks like…” or “this is probably…”. Similar process names, system service calls, and background tasks of development tools may all be misidentified as anomalies. Conclusions such as “data exfiltration” or “threats have been cleaned up” are difficult to trust without an evidence chain supported by logs, file hashes, and network connection records. If the agent deletes files based on such conclusions, it may damage normal software at best, or cause data loss at worst.

For this kind of problem, the correct order is: collect evidence first, classify second, and act last.

1. Collect evidence: record the process’s executable path, digital signature, command-line arguments, launch method, parent process, opened files, and network connections.
2. Compare independently: cross-check the output of the operating system’s own process and network inspection tools, and compare with sample information from trusted sources when necessary.
3. Classify cautiously: only determine that something is malicious when evidence is sufficient, and clearly tell the user the basis for the judgment.
4. Intervene minimally: prefer isolation over direct deletion, such as disconnecting the network, stopping the process, or backing up suspicious files.

4.3 Risk 3: Loss of Least Privilege and “Convenient Cleanup”

When an agent “conveniently” discovers problems and proactively handles them during development, it is essentially exceeding authority. The following three types of operations are especially high-risk:

• deleting files or processes, which may remove user data or system components by mistake;
• terminating processes or modifying system configuration, which may interrupt services;
• running with administrator or superuser privileges, because once exploited by prompt injection, the impact can expand rapidly.

The least-privilege principle should be followed, including:

• running in a sandbox by default, with limits on accessible directories, network access, and resource quotas;
• prohibiting automatic privilege escalation, so that any operation requiring higher permissions is confirmed by the user;
• tiered authorization for write operations and dangerous operations, as described in the next subsection.

Special attention should also be paid to scenarios where “the agent claims it needs higher permissions.” This is a common tactic in social engineering, and the agent itself may also be induced to make such requests. The correct approach is to first verify the reason, review the specific command, and then decide whether to grant permission.

4.4 Risk Tiering Model for Tool Calls

The following table can be used as a reference for assigning risk levels to each tool call made by an agent:

Risk level	Typical operations	Authorization policy
Read-only	Search code, view files, inspect processes and connections	Execute automatically
Low-risk write	Create or modify files inside the workspace, install pinned dependencies	Execute automatically after limiting the directory and record changes
Medium risk	Delete files in the workspace, run build scripts, access external networks	Show the complete command before execution; execute after user confirmation
High risk	Terminate processes, modify system configuration, access directories outside the workspace, commit code or publish artifacts	Prohibited by default; execute only after explicit user authorization and in a controlled environment

The illustrative implementation is as follows:

from enum import Enum
class RiskLevel(Enum):
    READ_ONLY = 0      # Read-only: search code, view files and system state
    LOW_WRITE = 1      # Low-risk write: create files in workspace, install pinned dependencies
    MEDIUM = 2         # Medium risk: delete files, access external networks, run build scripts
    HIGH = 3           # High risk: terminate processes, modify system configuration, commit or publish
def classify(tool_name: str, args: dict) -> RiskLevel:
    """Classify risk level based on tool name and arguments."""
    if tool_name in {"search_code", "read_file", "list_processes"}:
        return RiskLevel.READ_ONLY
    if tool_name == "write_file" and is_inside_workspace(args["path"]):
        return RiskLevel.LOW_WRITE
    if tool_name == "run_command":
        cmd = args["command"]
        if touches_system(cmd) or accesses_outside_workspace(cmd):
            return RiskLevel.HIGH
        if is_destructive(cmd) or needs_network(cmd):
            return RiskLevel.MEDIUM
        return RiskLevel.LOW_WRITE
    return RiskLevel.HIGH  # Unknown tools are treated as high risk
def guarded_execute(tool_name: str, args: dict, user) -> dict:
    level = classify(tool_name, args)
    if level >= RiskLevel.HIGH and not user.has_explicit_grant(tool_name):
        raise PermissionError("High-risk operations require explicit user authorization and should be executed in an isolated environment")
    if level == RiskLevel.MEDIUM:
        user.confirm(preview=build_diff_or_command_preview(tool_name, args))
    audit_log.append({"tool": tool_name, "level": level.name,
                      "args": sanitize(args), "approved_by": user.id})
    return tool_registry[tool_name](**args)

Supporting measures include:

• full-chain auditing: record the arguments, results, and approver of every tool call, and periodically sample and review them;
• rollback mechanisms: manage code changes with version control and automatically back up file changes;
• circuit breakers: automatically pause when the number of command executions or deletions exceeds a threshold within a given time period, and notify the user.

5. Variables Beyond Tools: Availability and Compliance Governance

Some developers become highly attached to certain tools not only because of their capabilities, but also because of accumulated workflows and switching costs. At the same time, when selecting AI coding tools, developers should consider availability and compliance risks:

• Access stability: network restrictions, account risk controls, and service bans may interrupt critical development work.
• Data residency and cross-border transfer: code snippets, internal documents, and logs may contain personal information, trade secrets, or regulated data. Related processing should comply with applicable data protection and data security regulations.
• Intellectual property: the open-source license compatibility of generated code also needs to be evaluated.

Therefore, the rational approach is not “use only one,” but to build a diverse and switchable tool portfolio:

1. Capability matrix: evaluate candidate tools by scenario, including code completion, code question answering, agent task execution, code review, and test generation.
2. Data classification: classify code and documents into levels such as public, internal, sensitive, and core according to sensitivity, and apply different handling policies to each level.
3. Unified gateway: encapsulate different tools through a unified interface so that business code is not directly bound to any single implementation, making gradual switching and failure degradation easier.
4. Continuous monitoring: track account status, call success rate, response latency, and cost changes, and set alert thresholds.
5. Emergency plans: retain local models and offline development processes for core projects so that development can continue if external services are unavailable.

6. Enterprise Adoption: Building a Trusted Foundation for AI-Driven Development

Best practices at the individual level can also be consolidated into organizational standards.

6.1 Organizational Governance Measures

• Unified access: centrally manage keys, quotas, and auditing through a gateway to prevent keys from being scattered across individual environments.
• Policy as code: include risk tiers and approval rules in code review so that every update is traceable.
• Effect measurement: track adoption rate, defect density, build success rate, and mean time to repair, using data to evaluate return on investment.
• Talent development: regularly conduct security drills covering prompt injection, data leakage, and related risks to improve developers’ risk awareness.

6.2 Infrastructure Support

AI coding tools usually need to execute code, run tests, and process build artifacts. Managing these steps centrally in the cloud can help improve security and controllability:

• use container platforms to provide isolated, quickly disposable execution environments for agent tasks, with resource quotas;
• host code repositories, dependency packages, and build artifacts in traceable infrastructure such as object storage and artifact repositories;
• use logging services to aggregate tool-call records and audit logs from agents, supporting search and long-term retention;
• configure monitoring alerts to notify promptly about abnormal commands, unauthorized access, and sudden cost increases;
• use the cloud platform’s general identity and access management capabilities to enforce least-privilege control.

For example, Baidu AI Cloud’s container, storage, logging, monitoring, and security products can be combined to build the trusted execution foundation described above. Enterprises can also connect multiple AI coding services through a unified gateway to implement scenario-based routing, gradual release, and failure degradation, reducing dependence on a single service.

7. Conclusion

From passive execution to proactive planning, coding agents are evolving from “code completers” into “development collaborators.” The real dividing line is that proactive behavior should be centered on user intent and carried out within clearly authorized boundaries, rather than crossing the user’s boundaries to make decisions independently.

For individual developers, tool selection should be based on scenario-based evaluation rather than following trends. For enterprises, the value of tools ultimately depends on the level of governance: whether permissions are controllable, whether data is compliant, and whether the process is auditable. Only when capability, security, and compliance form a closed loop can proactive agents truly become reliable development productivity, rather than merely a short-lived novelty.