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Introduction: With the development of artificial intelligence technology, AI Agents have gradually become the primary means for humans to interact with large models. AI Agents can perform tasks, solve problems, and provide personalized services. Their key components include planning, memory, and tool use, making interactions more efficient and natural. The applications of AI Agents cover scenarios such as professional domain Q&A, information organization, and role-playing, greatly enhancing user experience and work efficiency. With the help of AI Agent development platforms, users can easily create customized AI applications, promoting the widespread application and deep integration of AI technology across various fields.

As artificial intelligence technology rapidly advances, AI Agents are gradually becoming the primary means for humans to interact with large models (such as large language models). AI Agents are AI systems capable of performing tasks, solving problems, and providing services. They simulate human behavior and decision-making processes, making interactions with large models more natural, efficient, and personalized.

AI Agents will become the primary means for humans to interact with large models

As a bridge for interaction between humans and large models, AI Agents not only improve the efficiency and quality of interactions but also expand the application scope of large models. With continuous technological advancements, AI Agents will become more deeply integrated into our daily lives, becoming indispensable intelligent partners.

What is an AI Agent

AI Agents (English name: AI Agent or AI Bot) refer to entities that can perceive their environment and take actions to achieve certain goals. AI Agents can be software programs, robots, or other forms of systems. In commercial and technical applications, the concept of AI Agents (AI Bots) is also used to describe automated systems capable of performing specific tasks. They are cloud-based and AI-centric, building a multi-dimensional perception, global collaboration, precise judgment, continuous evolution, and open intelligent system. AI Agents (AI Bots) have wide applications in various fields such as enterprise services, game development, robot control, smart homes, autonomous vehicles, financial analysis, and medical diagnosis. AI Agents (AI Bots) consist of four key components: planning, memory, tool use, and action. AI Agents (AI Bots) possess basic characteristics such as autonomy, reactivity, proactivity, sociality, and evolution.

Figure 1: AI Agent System Driven by Large Models

As shown in the figure above, in AI Agents based on large models, the large model acts as the “brain” of the AI Agent, along with three key components:

1. Planning: AI Agents break down large tasks into sub-tasks and plan the execution process; they reflect on the task execution process to decide whether to continue executing the task or determine if the task is complete and terminate the operation.
2. Memory: Short-term memory refers to the context generated and temporarily stored during the execution of tasks, which is cleared after the task is completed. Long-term memory retains information for an extended period, generally referring to external knowledge bases, usually stored and retrieved using vector databases.
3. Tool use: Equipping AI Agents with tool APIs, such as calculators, search tools, code executors, database query tools, etc. With these tool APIs, AI Agents can interact with the physical world and solve real problems.

The characteristics of AI Agents mainly include:

1. Natural Language Understanding and Dialogue Management: AI Agents can understand user instructions and needs through advanced natural language processing technology, communicating with users in natural language. This communication mode includes not only simple Q&A but also complex dialogues, understanding context and user intentions.
2. Personalized Services: AI Agents can provide personalized services and suggestions based on users’ historical interaction data and preferences. This personalization is reflected not only in content recommendations but also in interaction methods and language styles, adapting to different user needs.
3. Task Automation: AI Agents can automate a series of tasks, from simple data retrieval to complex decision support. They can handle emails, schedule appointments, manage projects, and even perform creative work such as design and programming in some cases.
4. Learning and Adaptation: AI Agents possess the ability to learn and adapt, continuously optimizing their performance through machine learning algorithms to better meet user needs. This learning capability allows AI Agents to become smarter and more efficient over time.
5. Multimodal Interaction: In addition to text interaction, AI Agents can also process various types of data such as images and sounds, achieving multimodal interaction. This enables AI Agents to understand and respond to user needs more comprehensively, providing a richer interaction experience.

What Can AI Agents Do

By now, we should have a basic understanding of AI Agents. If you still find the concept of AI Agents a bit abstract, don’t worry. Let’s look at specific scenarios to see what practical problems AI Agents can solve for us.

Professional Domain Intelligent Q&A Expert

We can utilize knowledge bases and workflow orchestration tools to make AI Agents experts in a specific field, maximizing the value of users’ private knowledge bases to provide detailed and accurate answers. For example, we create a knowledge base that includes publicly available information about Jin Yong’s novels, processing a novel of tens of thousands of words into segments and vectorizing it before inputting it into the AI Agent. Let’s see what happens:

First, we create a “Jin Yong Martial Arts Novel” knowledge base by directly uploading the novel’s txt text or PDF document, as shown in the figure below.

Figure 2: Creating the Martial Arts Novel Knowledge Base

After a few minutes, the file is processed into segments and vectorized. This process converts the text into a language that computers can understand, facilitating the recognition and retrieval of the previously uploaded text information. We click on “Jin Yong – The Smiling, Proud Wanderer.txt” to see that the novel has been divided into 4572 paragraphs.

Figure 3: Details After Knowledge Base Processing

After creating the knowledge base, we proceed to create the AI Agent Bot, naming it “Linghu Chong”, and add the previously created “Jin Yong Martial Arts Novel” knowledge base.

Figure 4: Creating the AI Agent Bot

Next, let’s run the AI Agent to see how it responds. We input the question, “What specific moves does Linghu Chong’s Dugu Nine Swords have?”

Figure 5: Debugging AI Agent Dialogue Effect

From the response, the AI Agent first called the knowledge base tool to retrieve relevant information and then had the large model summarize and output the answer. This resulted in more precise and detailed content compared to directly asking the large model.

This demonstrated the process and effect of an AI Agent handling professional domain knowledge. Isn’t it super simple and interesting? Now let’s explore the application scenarios of AI Agents in intelligent organization and acquisition of industry information.

Intelligent Organization and Acquisition of Industry Information

AI Agents can orchestrate capabilities such as news retrieval and web scraping into workflows, organizing and refining content into specific formats to efficiently acquire the latest information in the industry.

First, we create a workflow for “searching news”, as shown in the figure below:

Figure 6: Creating the AI Agent Workflow

Next, we directly debug the workflow. We can see that not only do we obtain the latest news, but we can also organize the data format to provide a better reading experience.

Figure 7: Debugging AI Agent Workflow

Role-Playing and Style Creation

AI Agents can also incorporate excellent copywriting methodologies into prompt templates, allowing AI Agents to create content according to users’ writing styles for scenarios such as character creation, marketing copy, and speech scripts. For example, creating marketing copy in the style of Xiaohongshu or speech scripts. Below, we will look at the dialogue effect after adding role-playing style prompts to the AI Agent.

After adding the above prompt settings to the AI Agent, let’s test the dialogue effect:

Figure 8: Debugging AI Agent Workflow

Isn’t it amazing? After adding role settings, the AI Agent’s responses are no longer as stiff as typical large model responses; it feels like a real friend chatting with us, providing human-like responses, even with expressions and narrations. Don’t you also want to have your own AI companion?

By now, I believe you have a more concrete understanding of the capabilities of AI Agents. Let’s summarize the key components of AI Agents.

Key Components of AI Agents

In AI Agents based on large models, the large model acts as the “brain” of the AI Agent, along with three key components: planning, memory, and tool use.

Figure 9: Key Components of AI Agents

Planning

Planning allows for understanding observation and thought. If we compare it to humans, when we receive a task, our thought process might look like this:

1. We first think about how to complete the task.
2. Then we examine the tools we have at hand and how to use them efficiently to achieve our goals.
3. We break the task down into sub-tasks (just like we would use project management to break down tasks).
4. During task execution, we reflect and improve the execution process, learning lessons to enhance future steps.
5. We think about when the task can be terminated during execution.

This is the planning ability of humans, and we hope that AI Agents can possess such a thought process. Therefore, we can empower AI Agents with this thought process through LLM prompt engineering. In AI Agents, the most important thing is to enable LLMs to have the following two capabilities: sub-task decomposition and reflection and improvement.

1. Sub-task Decomposition

By using LLMs, AI Agents can break down large tasks into smaller, more manageable sub-tasks, effectively completing complex tasks.

Chain of Thought (CoT)

Chain of Thought (CoT) is a technique used in the field of natural language processing (NLP) to enhance the reasoning capabilities of models. It allows models to output a series of intermediate thought steps before generating the final answer, making the decision-making process of the model more transparent and interpretable. This technique is particularly effective in solving problems that require multi-step reasoning, such as mathematical problems and logical reasoning tasks.

Application Cases of Chain of Thought Technology

1. Mathematical Problem Solving: When solving mathematical problems, the model can first output the steps to solve the problem, such as listing equations and calculation processes, and finally provide the answer. This helps improve the accuracy and interpretability of the model.
2. Logical Reasoning: In logical reasoning tasks, the model can first output the reasoning process, such as premises and intermediate conclusions, and finally arrive at the final conclusion. This helps the model perform better on complex logical problems.
3. Text Understanding: In text understanding tasks, the model can first output a preliminary understanding of the text, such as keyword extraction and sentence structure analysis, and finally provide a complete understanding of the text. This helps improve the model’s accuracy and depth in text understanding tasks.

Here are some examples of Chain of Thought prompts:

In this way, Chain of Thought prompts can help models analyze and solve problems more systematically rather than directly providing answers.

Tree of Thought (ToT)

Tree of Thought (ToT) is a technique used in the field of artificial intelligence, particularly in reinforcement learning and planning problems. It is a model-based decision-making method where the AI Agent constructs a tree structure of possible actions and outcomes to evaluate and select the best course of action.

Current large models still make token-level decisions sequentially from left to right. Is such a simple mechanism sufficient for LMs to develop into general problem solvers?

Research shows that humans have two modes of decision-making: a fast, automatic, unconscious mode (“System 1”) and a slow, deliberate, conscious mode (“System 2”). The second decision-making mode involves maintaining and exploring different alternatives to the current choice, rather than just picking one; evaluating the current state and actively looking ahead or backtracking to make more global decisions. This may provide insights into the current decision-making methods of models.

The existing large models have two main shortcomings in problem-solving:

1. Locally, there is no exploration of different continuations in the thought process—similar to branches of a tree.2. Globally, there is no incorporation of any type of planning, foresight, or backtracking to help evaluate these different choices—heuristic-guided search is a characteristic of human problem-solving.

Tree of Thought (ToT) allows models to explore multiple reasoning paths, treating all problems as a search on a tree, where each node on the tree represents a state (a partial solution to the input and the sequence of thoughts so far).

Tree of Thought (ToT)

Tree of Thought (ToT) is an extension of Chain of Thought (CoT), where multiple branches are reasoned out at each step of the Chain of Thought, topologically expanding into a tree of thought. Heuristic methods are used to evaluate the contribution of each reasoning branch to problem-solving. Search algorithms such as breadth-first search (BFS) or depth-first search (DFS) can be used to explore the tree of thought and perform foresight and backtracking.

2. Reflection and Improvement

During task execution, AI Agents reflect on completed sub-tasks through LLMs, learning from mistakes and improving future steps to enhance the quality of task completion. They also reflect on whether the task is complete and terminate it.

ReAct

ReAct (Yao et al. 2023), “ReAct: Synergizing Reasoning and Acting in Language Models” proposes a method to enhance large language models by combining reasoning and acting to improve reasoning and decision-making effectiveness.

• Reasoning: LLMs deduce conclusions based on “existing knowledge” or “knowledge acquired after acting”.
• Acting: LLMs use tools to acquire knowledge or complete sub-tasks to obtain interim information based on the actual situation.

Why does combining reasoning and acting effectively enhance LLMs’ ability to complete tasks? The ReAct paper’s examples demonstrate that by interacting with a simple encyclopedia API, it overcomes common hallucination and error propagation issues in Chain of Thought reasoning, generating human-like task-solving trajectories that are more interpretable than baselines without reasoning traces.

Reflection and Improvement

As shown in the figure: (1) Comparing four prompting methods, (a) standard method, (b) Chain of Thought (CoT, reasoning only), (c) acting only, and (d) ReAct (reasoning + acting) to solve HotpotQA (Yang2018) problems; (2) Comparing two prompting methods, (a) acting only, and (b) ReAct, to solve an AlfWorld game (Shridhar 2020b). In these two methods, context examples in the prompts are omitted, showing only the task-solving trajectories generated by the model (Act, Thought) and the environment (Obs).

Memory

The memory of AI Agents is their ability to store and recall information, which is crucial for learning, decision-making, and adapting to the environment. The memory of AI Agents can be divided into different types, each playing a different role in the operation of the AI Agent.

1. Types of Memory

1. Short-term Memory

• Short-term memory, also known as working memory, can temporarily store information needed by the AI Agent during the current task processing. For example, when the AI Agent is solving a mathematical problem, it may store intermediate calculation results in short-term memory for use in subsequent steps. The capacity of short-term memory is usually limited, and information may be forgotten after a period.

2. Long-term Memory

• Long-term memory can store information obtained from past experiences, knowledge, and learning. This includes learned patterns, rules, concepts, etc. The capacity of long-term memory is relatively large, and information can be retained for a long time. AI Agents can recall and retrieve information from long-term memory to solve new problems or respond to new situations.

2. Memory Storage Methods

1. Distributed Storage

• Information is stored in a distributed manner within the AI Agent’s neural network or other data structures. This storage method allows information to be represented through multiple nodes or connections, enhancing the robustness and scalability of memory. For example, in deep learning, the weights and connections of neural networks can be seen as a form of distributed memory, storing knowledge learned from training data.

2. Associative Storage

• Information is stored in an associative manner, establishing connections between different pieces of information. When the AI Agent recalls a piece of information, it can retrieve related information through associative cues. For example, when you recall a person’s name, you might use cues related to that person, such as appearance, profession, or shared experiences, to help you remember their name.

3. Hierarchical Storage

• Information is stored hierarchically, gradually building from specific instances to abstract concepts. This storage method helps the AI Agent organize and classify information, improving retrieval efficiency. For example, in an image recognition AI Agent, images can be stored according to different categories and hierarchies, from specific objects to abstract concepts such as animals, plants, vehicles, etc.

3. Memory Updating and Forgetting

1. Learning and Updating

• AI Agents can update their memory through continuous learning and experience accumulation. When AI Agents encounter new situations or tasks, they can integrate new information into existing memory or form new memories. For example, in reinforcement learning, AI Agents continuously adjust their strategies and memories through interaction with the environment to obtain better rewards.

2. Forgetting Mechanism

• To avoid memory overload and maintain the effectiveness of information, AI Agents need to have a certain forgetting mechanism. Forgetting can be active or passive. Active forgetting refers to the AI Agent actively deleting some unimportant or outdated information based on certain strategies. Passive forgetting occurs naturally due to the passage of time or lack of use of information. For example, AI Agents can decide whether to forget certain information based on the frequency of use or importance of the information.

4. Functions of Memory

1. Problem Solving

• AI Agents can utilize knowledge and experience stored in memory to solve new problems. By recalling past similar problems and solutions, AI Agents can quickly find ways to solve current problems. For example, an intelligent customer service system can use past dialogue records and solutions to answer user questions.

2. Learning and Adaptation

• Memory is the foundation for AI Agents to learn and adapt to new environments. By storing and recalling past experiences, AI Agents can continuously adjust their behaviors and strategies to better adapt to changing environments. For example, an autonomous vehicle can improve its driving safety and efficiency by recalling past road conditions and driving experiences.

3. Prediction and Planning

• AI Agents can use information in memory for prediction and planning. By analyzing past events and trends, AI Agents can predict future situations and develop corresponding plans. For example, a weather forecasting AI Agent can use past meteorological data and models to predict future weather conditions.

The memory of AI Agents is an important component of their intelligent behavior. Through reasonable storage, updating, and utilization of memory, AI Agents can better solve problems, learn and adapt to the environment, and make predictions and plans.

Tool Use

LLMs are programs in the digital world. To interact with the real world, acquire unknown knowledge, or compute complex formulas, they rely on tools. Therefore, we need to equip AI Agents with various tools and empower them with the ability to use these tools.

In AI Agents, tools are functions, and tool use is calling functions. Implementing function calls in LLMs utilizes the capability of LLMs: Function Calling.

The Function Calling mechanism in Large Language Models (LLMs) refers to the model’s ability to call external functions to perform specific tasks or obtain required information. When calling LLMs via API, the caller can describe the function, including its functionality description, request parameter specifications, and response parameter specifications, allowing the LLM to appropriately choose which function to call based on user input while understanding the user’s natural language and converting it into request parameters for the function call (returned in JSON format). The caller uses the function name and parameters returned by the LLM to call the function and obtain a response. Finally, if needed, the function’s response is passed to the LLM, allowing it to organize a natural language reply to the user.

Functions and Purposes

• Enhanced Functionality: By calling external functions, LLMs can perform tasks beyond their original training scope, such as querying databases, performing calculations, calling APIs, etc.
• Improved Accuracy: For tasks requiring real-time data or specialized knowledge, models can improve the accuracy of their outputs by calling the corresponding functions to obtain the latest information.
• Expanding Capability Boundaries: LLMs originally reasoned and generated text based solely on their training data, but through Function Calling, they can transcend these limitations and perform complex tasks.

How It Works

1. Function Registration: First, external functions need to be registered in the model’s environment. This usually involves defining the function’s signature (name, parameter types, and return types).2. Intent Recognition: When the model generates text, it attempts to understand the user’s request intent and decides whether to call a specific function.3. Parameter Extraction: If a function needs to be called, the model extracts the necessary parameters from the generated text.4. Function Call: The model calls the corresponding function and passes in the extracted parameters.5. Result Processing: After the function execution is complete, the result is returned to the model, which generates further responses based on the result.

Implementation Methods

1. API Interface: Obtaining information or performing tasks by calling RESTful APIs or gRPC services.2. Library Function Calls: Directly calling locally installed library functions.3. Custom Scripts: Executing custom scripts to perform specific operations.4. Database Queries: Querying databases to obtain stored data.

Application Scenarios

• Information Retrieval: Such as weather forecasts, news summaries, and other real-time information acquisition.
• Data Processing: Performing mathematical operations, statistical analyses, etc.
• External Service Integration: Interacting with third-party services such as payment systems and mapping services.
• Code Execution: Generating and executing simple code snippets to solve problems.

Through the Function Calling mechanism, LLMs can better serve practical application scenarios, enhancing their value in the real world. The specific workflow of function calling is shown in the figure below:

Function Calling Example

Function Calling Example

Suppose there is a language model, and the user requests to generate a simple Python program to calculate the sum of two numbers. The model not only generates the code but also uses the Function Calling mechanism to call a function to verify the correctness of the code.

In this example, the Function Calling mechanism can call a verification function to check the correctness of the add_numbers function and return the verification result.

Function Calling provides great flexibility and functionality for the application of large language models, enabling models to directly interact with external systems and perform complex tasks rather than just generating static text. This capability is especially valuable in building intelligent assistants, automation tools, and interactive applications.

AI Agent Development Platform

If you want to develop an AI Agent (AI application), it is now much more convenient than in the early days of large model explosions. With the continuous demand for AI applications, AI Agent development platforms are emerging one after another. For example, the Botnow AI Agent development platform abstracts and encapsulates frequently used modules such as memory capabilities, planning capabilities, RAG capabilities, and large model calls. In the Botnow AI Agent development platform, users can quickly and easily create high-quality AI Agents through plugins, knowledge bases, workflows, etc., and support publishing to third-party platforms, as well as API calls and Web SDK.

Botnow AI Agent Development Platform

Outlook

With the rapid development of large language models (LLMs), their supported context lengths are continuously increasing, parameter scales are becoming larger, and reasoning capabilities are significantly enhanced. This allows the capability boundaries of AI Agents (AI Agents) built on such advanced models to be continuously broken. With AI Agent technology, we have already been able to develop diverse AI applications such as Copilot and Botnow, which are gradually becoming indispensable parts of our daily lives and work. It is foreseeable that AI applications will rapidly and thoroughly reshape the software forms and interaction modes we are familiar with, significantly enhancing human work efficiency.

Source: Alibaba Cloud