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From: New-World-2019 <37373361+New-World-2019@users.noreply.github.com>
Date: Mon, 26 Aug 2024 11:53:33 +0800
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This pull request includes a minor update to the agent_chat.md file to correct the link to the Enhanced Inference.
---
 website/docs/Use-Cases/agent_chat.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/Use-Cases/agent_chat.md b/website/docs/Use-Cases/agent_chat.md
index 46b555b3d7ca..76f9959841b8 100644
--- a/website/docs/Use-Cases/agent_chat.md
+++ b/website/docs/Use-Cases/agent_chat.md
@@ -21,7 +21,7 @@ The figure below shows the built-in agents in AutoGen.
 We have designed a generic [`ConversableAgent`](../reference/agentchat/conversable_agent.md#conversableagent-objects)
  class for Agents that are capable of conversing with each other through the exchange of messages to jointly finish a task. An agent can communicate with other agents and perform actions. Different agents can differ in what actions they perform after receiving messages. Two representative subclasses are [`AssistantAgent`](../reference/agentchat/assistant_agent.md#assistantagent-objects) and [`UserProxyAgent`](../reference/agentchat/user_proxy_agent.md#userproxyagent-objects)
 
-- The [`AssistantAgent`](../reference/agentchat/assistant_agent.md#assistantagent-objects) is designed to act as an AI assistant, using LLMs by default but not requiring human input or code execution. It could write Python code (in a Python coding block) for a user to execute when a message (typically a description of a task that needs to be solved) is received. Under the hood, the Python code is written by LLM (e.g., GPT-4). It can also receive the execution results and suggest corrections or bug fixes. Its behavior can be altered by passing a new system message. The LLM [inference](#enhanced-inference) configuration can be configured via [`llm_config`].
+- The [`AssistantAgent`](../reference/agentchat/assistant_agent.md#assistantagent-objects) is designed to act as an AI assistant, using LLMs by default but not requiring human input or code execution. It could write Python code (in a Python coding block) for a user to execute when a message (typically a description of a task that needs to be solved) is received. Under the hood, the Python code is written by LLM (e.g., GPT-4). It can also receive the execution results and suggest corrections or bug fixes. Its behavior can be altered by passing a new system message. The LLM [inference](/docs/Use-Cases/enhanced_inference) configuration can be configured via [`llm_config`].
 
 - The [`UserProxyAgent`](../reference/agentchat/user_proxy_agent.md#userproxyagent-objects) is conceptually a proxy agent for humans, soliciting human input as the agent's reply at each interaction turn by default and also having the capability to execute code and call functions or tools. The [`UserProxyAgent`](../reference/agentchat/user_proxy_agent.md#userproxyagent-objects) triggers code execution automatically when it detects an executable code block in the received message and no human user input is provided. Code execution can be disabled by setting the `code_execution_config` parameter to False. LLM-based response is disabled by default. It can be enabled by setting `llm_config` to a dict corresponding to the [inference](/docs/Use-Cases/enhanced_inference) configuration. When `llm_config` is set as a dictionary, [`UserProxyAgent`](../reference/agentchat/user_proxy_agent.md#userproxyagent-objects) can generate replies using an LLM when code execution is not performed.