Agent Application

Model Selection

For optimal multi-step planning, we recommend selecting a model with strong tool-calling capabilities, such as the Qwen-Max series.

1. Select a model from the model selector dropdown. Click More Models to see additional models.

2. Click the parameter configurator on the right side of the model selector. The following parameters can be modified:

   • Maximum Response Length: The maximum length of model-generated content, excluding the prompt.

   • Temperature: Controls the randomness and diversity of the output. Higher values increase randomness.

   • enable_thinking: Whether to enable thinking mode. Enabling thinking mode helps improve the agent's reflection capability. Models that do not support thinking mode cannot configure this parameter.

Prompts

The system prompt defines the agent's role, behavioral instructions, and capability boundaries, ensuring consistency, controllability, and task compliance during interactions.

1. Configure the System Prompt

   Set the system prompt to Please answer my questions in the style of "One Hundred Years of Solitude". The following is an effect comparison:

   • Without a system prompt: The model responds with a standard self-introduction.

   • With the system prompt configured: The AI responds in a literary style, demonstrating that the system prompt has taken effect.

2. Using Custom Variables in the System Prompt (Optional)

   In addition to static text, the system prompt also supports embedding custom variables.

   1. Click Custom variables in the upper right corner of Prompts, configure the custom variable, and click OK to save.

   2. Type / to use a configured variable.

File Pre-parsing

The file pre-parsing feature controls how uploaded files are processed.

• Pre-parsing disabled: When disabled, the system does not proactively parse files. The file URL is passed as context information to the agent, which can then decide whether to invoke tools and pass the URL as a parameter in subsequent steps.

• Pre-parsing enabled: When enabled, the system uses built-in parsers to process uploaded documents, images, videos, audio files, and other formats, returning parsed text content to the model as reference.

Qwen-VL series models have multimodal capabilities and can directly parse images and video files even when file pre-parsing is disabled.

In all other cases (for example, when using a text-only model without multimodal capabilities, or when using a Qwen-VL series model to process non-image/video files), the agent's file processing capability strictly follows the enabled or disabled logic described above.

Built-in Tools

Built-in tools run in an isolated sandbox environment, providing code execution and file operation capabilities. All tools are disabled by default and can be enabled as needed.

Knowledge Base

The knowledge base enables the agent to query external information and use the retrieved content as the basis for generating answers. In the new agent, the knowledge base is treated as a skill of the agent and invoked autonomously as a tool. This proactive approach to knowledge retrieval improves answer accuracy and reduces hallucinations when dealing with private knowledge or domain-specific Q&A. For more information, see Knowledge Base.

Enable Show Source in the Response section to display knowledge sources and source file URLs.

The new agent supports using tags to narrow the query scope of knowledge bases. By assigning tags to knowledge base files and defining usage rules in the system prompt, you can guide the agent to search within a smaller, more precise set of files based on user intent, significantly improving answer accuracy and relevance. For more information, see Knowledge Base Tag Filtering for New Agent.

MCP

In the new agent, external tools are integrated via the MCP protocol and incorporated into the scheduling system, including official MCP services from the MCP Market and custom MCP services. The agent can dynamically invoke MCP in a non-fixed order during multi-step reasoning to handle more complex tasks. Additionally, plugins can be converted to MCP services with one click.

Data Connectors

Data connectors serve as bridges for agents, workflows, and knowledge bases on the Model Studio platform to access external data. By configuring data connectors, agents can directly query and operate on data from external data sources.

Application Components

Integrate existing agents or workflows as tools. The agent or workflow application must first be published as a component.

Skills

Skills are capability packages that can be added to agents, enabling the agent to automatically handle specific types of tasks during conversations. After adding skills, the agent automatically identifies matching tasks and invokes the corresponding skill for processing, without requiring additional code. For more information, see Skill.

Memory

• Short-term Memory: The new agent supports short-term memory, which provides context information across multi-turn conversations. You can set 0 to 30 turns of context (0 means no multi-turn conversation history is passed). More turns increase conversational relevance but also increase input length.

• Long-term Memory: This feature is planned for future iterations.

Environment

The environment section is used to configure authentication credentials and environment variables required by skills. Once configured, the agent automatically injects the corresponding credentials and parameters when invoking skills, eliminating the need to hardcode sensitive information in skill code.

Response

The response section supports displaying answer sources. When enabled, knowledge sources and source file/web page URLs are displayed as footnote markers. This feature is recommended for use in combination with knowledge bases and the web search MCP.

Publish Application

In the upper-right corner of the application configuration page, click Publish. A pop-up window displays the configuration changes since the last publication. After confirming the release information, click Confirm Publish to complete the publication.

API Invocation

In the Publish Channel tab of the agent application, click View API on the right side of API Call to view the API invocation method for the new agent application. For more information, see New agent application API.

Version Management

The version management feature allows you to edit the description of historical versions or roll back to a previously published version.

1. On the application configuration page, click Version Management on the right side of the top navigation bar.

2. Select the historical version you want to roll back to, hover over the card, and click the edit icon in the upper-right corner. In the Edit Version Description dialog box, make the necessary modifications and click OK to save. Click Overwrite Current Draft to roll back to that version.

Can I upgrade a legacy agent to the new version?

No. The legacy agent and the new agent are built on different technical architectures and are not compatible. Direct version switching, upgrading, or downgrading is not possible.

If you are currently using a legacy agent and want to experience the features of the new agent, go to the console and create a new agent application.

Why does the agent not invoke the configured tools as expected?

You can troubleshoot from the following four aspects:

• Skill configuration and binding: Verify that the skill has been successfully created and correctly bound to the current agent application.

• System prompt guidance: Check whether the system prompt clearly describes the skill's functionality, parameters, and applicable scenarios. The model relies on these descriptions to decide when to invoke a skill.

• Intent-skill relevance: Evaluate whether the question is clearly formulated and whether its intent clearly points to a specific skill. If the intent is vague or unrelated to the skill's functionality, the model may choose not to invoke it.

• Execution round limit: Check whether the ReAct round limit has been reached. The agent may have planned to invoke the skill but was forced to terminate before reaching that step because the round limit was exhausted.

Does the agent application support context cache?

It supports implicit cache, but configuring explicit cache within an application is not yet supported.

• Implicit cache: It takes effect automatically when the agent invokes a model that supports implicit cache, requiring no configuration and cannot be disabled. The system automatically identifies and caches common prefixes of requests (such as identical system prompts, multi-turn conversation history, and text chunks retrieved from the knowledge base). Input tokens that hit the cache are billed at 20% of the standard input price, which can reduce model invocation costs accordingly.

• Explicit cache: It requires actively creating cache markers for the specified content in the model invocation request. Because the agent application constructs model requests uniformly on the platform side, configuring explicit cache is not yet supported.

For the working modes, supported models, and billing details of context cache, see Context Cache.