From Answering to Executing: Enterprise AI Agent Frameworks and Managed Services

1. Background: Shifting from “Answering” to “Executing”

As Large Language Models (LLMs) continue to evolve, industry focus is shifting from simple natural language Q&A to whether models can complete actual business tasks. In agentic scenarios, models must not only understand user intent but also call tools, execute commands, read files, access the web, and continuously adjust strategies across multi-turn interactions.

Traditional evaluation metrics like answer accuracy and reasoning scores are insufficient to reflect an agent’s performance in long-horizon tasks. A model might excel at generating code but frequently fail when executing commands in a real Shell environment; it might converse fluently but be unable to correct its execution path based on user feedback. Therefore, agent evaluation is incorporating dimensions closer to engineering practice, such as execution reliability, task confirmation success rate, user satisfaction, and controllability.

In this context, open-weight models and managed agent frameworks have emerged as two critical choices for enterprise implementation. The former allows developers to own model weights and deploy them privately, while the latter encapsulates the engineering components supporting agent execution into cloud services, reducing construction and maintenance costs.

2. Key Metrics for Open-Weight Models in Agent Evaluation

Agent tasks typically feature long execution chains, heavy environment dependencies, complex feedback cycles, and diverse failure causes. Relying solely on manual spot-checks is insufficient to determine if a model possesses stable execution capabilities. The industry typically uses real user session data for evaluation, focusing on several core metrics.

2.1 Execution Reliability: Shell Command Execution

In actual agent operations, models frequently need to generate Shell commands, such as creating directories, viewing files, installing dependencies, or running scripts. If a command has a syntax error, missing parameters, or makes incorrect assumptions about the environment state, the task will abort.

The execution reliability metric measures the rate at which model-generated commands are successfully executed by the environment. Unlike traditional code generation evaluations, this metric emphasizes “actually runnable” rather than “appears reasonable.” For instance, a command might be syntactically correct but fail in the target environment due to non-existent paths, insufficient permissions, or missing dependencies.

From an engineering perspective, improving execution reliability requires the model to have the following capabilities:

• Checking the environment state before execution, rather than assuming it matches training data;
• Providing fallback strategies for operations that might fail;
• Avoiding implicit assumptions when outputting commands, such as hardcoded usernames or fixed path separators;
• Reading error messages and making corrections when failures occur.

These capabilities depend not only on the model itself but also on whether the runtime framework provides clear environmental feedback. A robust agent framework should return structured information to the model, such as command exit codes, standard error output, and timeout statuses, to form a closed loop.

2.2 Controllability: Adjusting Execution Direction Based on Feedback

In real business tasks, users often introduce new requirements during execution, such as “Do not delete the original files,” “Switch to a different data source,” or “Process only the first 100 records for now.” The agent needs to quickly understand the new instruction and adjust subsequent steps without interrupting the overall task.

The controllability metric measures the model’s performance in such scenarios. High-scoring models typically exhibit strong instruction-tracking capabilities, distinguishing between “the user is just providing additional information” and “the user is explicitly overriding the previous step.” Additionally, the model should avoid repeating completed steps and refrain from executing irreversible operations without confirmation.

At the framework design level, controllability can be enhanced through engineering means, such as:

• Inserting user feedback into the context as structured messages;
• Setting confirmation checkpoints before critical operations;
• Allowing users to pause, modify, or roll back tasks at any time;
• Adding secondary confirmation mechanisms for irreversible operations.

Therefore, controllability is not solely determined by the model but is the combined result of model capability and the runtime framework.

2.3 User Satisfaction and Confirmation Success

User satisfaction usually stems from subjective evaluations after actual use. In agentic scenarios, user satisfaction depends not only on whether the final result is correct but also on factors like execution efficiency, interaction frequency, error handling, and explainability.

The confirmation success metric is more objective: whether the task was completed as expected and the user explicitly confirmed the completion state. It is worth noting that some tasks might “appear complete” but are not accepted by the user. For example, the model might generate a CSV file with the wrong column order, or return a report using data sources outside the user’s specified scope.

To improve the confirmation success rate, the framework should support visual presentation of task results, log retrieval, and output validation. Before completing a task, the model should also proactively explain what it executed, where the results are saved, and whether any anomalies or degradations occurred.

3. Engineering Architecture of Managed Agent Services

Building a functional agent requires more than just a model; it requires a complete set of engineering components. Common components include:

• Task Scheduler: Parses tasks, dispatches steps, and controls loops;
• Tool Executor: Executes code, commands, and API calls;
• Context Manager: Maintains multi-turn sessions and historical states;
• Permission Controller: Restricts resources accessible to the model;
• Logging and Audit System: Records every operation;
• Billing and Usage Statistics Module.

If developers build these components from scratch, they must handle numerous engineering challenges like environment isolation, concurrent scheduling, error recovery, and security auditing. Managed agent services provide these capabilities as cloud services. Developers only need to define the agent’s behavioral logic and tool scope to integrate it into their business systems.

3.1 Long-Period Task Execution Capabilities

In real-world business, many agent tasks cannot be completed in a single request. Examples include batch processing tens of thousands of records, continuously monitoring data changes, or gathering information across multiple systems. These tasks might take minutes or even hours.

Managed agent services typically support long-period task suspension and resumption. Even if the network connection drops or the client closes, the task can continue executing in the cloud. Developers can query task status, fetch intermediate results, or terminate tasks via APIs.

Furthermore, multi-agent collaboration requires support from the managed environment. Different agents might assume different roles, such as one for information retrieval, another for data analysis, and a third for report generation. The runtime framework needs to provide mechanisms for message passing, task dispatching, state synchronization, and conflict resolution.

3.2 Tool and Skill Integration

An agent’s capability boundary largely depends on its callable tools. General tool categories include:

• Web Search: Retrieving public information;
• Specialized Data Query: Accessing databases or structured data interfaces;
• Office Document Processing: Generating spreadsheets, presentations, or PDFs;
• Code Execution: Running scripts and returning results;
• Content Generation: Creating copy, summaries, translations, etc.

Enterprises often want to integrate their own tools and data sources, such as internal CRM systems, order databases, knowledge bases, or monitoring platforms. Managed agent services should feature an open plugin mechanism, allowing developers to register custom tools via standardized interfaces.

On cloud platforms like Baidu AI Cloud, agent applications can be combined with object storage, cloud databases, message queues, logging services, and monitoring alerts to build a complete pipeline. For instance, an agent might write processed files to object storage, trigger downstream tasks via a message queue, and record the execution process using a logging service.

4. Security Boundaries: From “Can Do” to “Allowed to Do”

When enterprises implement agents, security is the primary concern. Unlike chatbots, agents often have operational capabilities—they might delete files, call APIs, send messages, or modify configurations. Therefore, their operational boundaries must be strictly constrained.

4.1 Independent Sandbox and Network Isolation

Sensitive tasks should run in isolated environments to avoid impacting production systems. A sandbox can restrict file system access scopes, block specific system calls, and control network egress. For example, an agent might only be allowed to access specified directories and domains, with all other access denied.

Regarding network policies, enterprises may need to distinguish between intranet and public internet access. Some tasks only require public APIs, while others need access to internal databases. Managed services should allow different network access rules to be configured for different agents.

4.2 Execution Tracing and Audit Export

Every step an agent takes can have business impacts, making the execution process traceable. A complete audit log should include:

• Timestamps and operator identifiers;
• Raw input and structured instructions;
• Model-generated commands or tool calls;
• Tool return results and exit statuses;
• Permission check results and interception reasons.

Audit logs can be used for post-mortem analysis, compliance reviews, and issue localization. Enterprises typically also need to export logs to their own logging platforms for unified querying with other systems.

4.3 Pre-Inference Security Interception

In addition to controls at the execution layer, security checks can be performed before model inference. A common practice is adding checkpoints at the task input stage and the tool return stage. For example:

def pre_inference_check(user_input):
    if contains_sensitive_pattern(user_input):
        return reject("Input contains sensitive information, intercepted")
    if not within_scope(user_input):
        return reject("Task exceeds authorized scope")
    return allow()
def pre_tool_result_check(tool_output):
    if contains_private_data(tool_output):
        return redact(tool_output)
    return tool_output

This mechanism can be integrated with an enterprise’s self-built security services or use general rules provided by the platform. Interception rules typically include private data identification, sensitive field filtering, and unauthorized operation detection. By moving security capabilities forward to the inference stage, risky operations can be blocked earlier rather than remediated after the fact.

5. Enterprise Adoption Best Practices

When introducing agent capabilities, enterprises are advised to follow a phased approach.

First, define the business scenarios and boundaries. Instead of pursuing a general-purpose agent from the start, select scenarios with clear processes and well-defined inputs/outputs, such as investment research compilation, internal IT ops response, or customer service assistance. Clear boundaries help in designing tool scopes and permission policies.

Second, build observability. Agent behavior is inherently uncertain, necessitating robust task status querying, logging, metrics monitoring, and alerting mechanisms. For instance, you can monitor metrics like task success rate, average completion time, tool call failure rate, and user rejection rate.

Third, control costs and resource consumption. Agent operations involve multiple cost dimensions, including model inference, tool calls, and cloud resource usage. Enterprises should regularly analyze usage reports to identify high-consumption tasks and optimize strategies. Examples include avoiding unnecessary multi-turn retries, reusing tool results, and reducing redundant searches.

Finally, maintain human-in-the-loop capabilities. Regardless of the automation level, certain critical operations should retain manual confirmation steps. For example, processes involving financial operations, contract generation, or outbound messaging should have explicit approval workflows.

6. Conclusion and Outlook

AI agents are transitioning from experimental capabilities to enterprise-grade productivity tools. Open-weight models offer developers more autonomy, while managed agent services significantly lower the engineering barrier to entry. These two are not mutually exclusive but represent choices tailored to different deployment needs and capability requirements.

From an engineering practice perspective, model capability is just one component. The robustness of the execution framework, the maturity of security policies, the completeness of audit mechanisms, and the richness of the tool ecosystem collectively determine whether an agent can run stably in real business scenarios. As experience with long-period task execution accumulates, the industry will form more mature standards for agent design and operations, driving agents from “usable” to “trustworthy.”