• AI agents, as shown above (source), are autonomous systems designed to perform tasks by making decisions based on their environment and inputs. These decisions are typically made using AI techniques such as machine learning, natural language processing and can incorporate multiple modalities.
  • AI agents can be proactive and reactive, meaning they can initiate actions on their own and respond to changes in their environment. Their functionality is often complex and involves a degree of learning or adaptation to new situations
  • These tasks are determined by the AI itself based on the data it gathers and processes, making AI agents essential tools for efficiency and automation in various sectors.
  • AI agents distinguish themselves from ordinary software by their ability to make rational decisions. They process data received from their environments, whether through physical sensors or digital inputs, and use this information to predict and execute actions that align with set goals. This could range from a chatbot handling customer inquiries to a self-driving car navigating obstacles on the road.

    “While there isn’t a widely accepted definition for LLM-powered agents, they can be described as a system that can use an LLM to reason through a problem, create a plan to solve the problem, and execute the plan with the help of a set of tools.” source

Core Components of AI Agents

  • The image above, (source), simplifies the architecture of a traditional end to end agent pipeline.
  • Let’s dive deeper into each component of AI agents to understand their structure and functionality at a more detailed, technical level.

1. Agent Core (the LLM)

  • The Agent Core is the central command and control center for the AI agent. It integrates various functionalities to manage tasks, direct information flow, and ensure coherent operation across different modules.

  • Decision-Making Engine: The core of the agent includes algorithms responsible for decision-making. These algorithms analyze data from memory and inputs from external sources to make informed decisions. Common algorithms include decision trees, neural networks, or rule-based systems, depending on the complexity and requirements of the agent.
  • Goal Management System: This subsystem maintains and updates the goals of the AI agent based on directives from users or changes in data. It might employ prioritization algorithms to manage multiple concurrent goals and resolve conflicts between them.
  • Integration Bus: Acts as a mediator that facilitates communication between the memory modules, planning module, and tools. It ensures data consistency and manages API calls to external services, handling data transformation and routing.

2. Memory Modules

  • Memory modules in AI agents are analogous to human memory but structured to support specific computational processes:

  • Short-term Memory (STM):
    • Data Structure: Typically implemented using stack, queue, or temporary databases that allow for fast access and modification. The choice depends on how the agent needs to access this data (LIFO, FIFO, etc.).
    • Volatility: Data in STM is transient, designed to hold information just long enough to complete relevant tasks. It’s cleared systematically to free up space and processing power.
    • Functionality: STM is crucial for tasks requiring immediate but temporary recall, such as holding intermediate results of a computation or temporarily storing user queries during a session.
  • Long-term Memory (LTM):
    • Data Storage: Often implemented using more permanent data storage solutions like databases or file systems that ensure data persistence across sessions and operations.
    • Indexing and Retrieval Systems: Includes sophisticated indexing mechanisms to facilitate quick retrieval of relevant information. This could involve SQL databases for structured data or NoSQL for unstructured data, equipped with search algorithms optimized for the agent’s needs.
    • Learning and Updating Mechanisms: LTM not only stores data but also updates it based on new information and learning outcomes from the agent’s interactions. This might involve machine learning models that refine stored data for accuracy and relevance over time.

3. Tools

  • Tools are specialized functions or external services that the agent can employ to execute specific tasks. These can vary widely in nature but are generally characterized by their direct applicability to the tasks at hand.

  • Executable Workflows:
    • Implementation: These are often scripted actions or processes defined in a high-level language that directs the agent on how to perform specific tasks, such as data scraping, sending emails, or executing a transaction.
    • Automation Hooks: Tools are typically equipped with interfaces for automation, allowing the agent to trigger them based on certain conditions without manual intervention.
  • APIs:
    • External APIs: These include third-party services that the agent can interact with, such as weather information, financial data, or social media services. The agent uses API calls to send and retrieve data, extending its capabilities beyond its internal resources.
    • Internal APIs: Developed to allow different parts of the agent to communicate securely and efficiently. These APIs facilitate modular design by allowing individual components of the agent to be developed and updated independently.
  • Middleware:
    • Purpose: Acts as a bridge between the agent’s core logic and the tools it uses. Middleware handles data formatting, error handling, and security checks to ensure that tool interaction is seamless and safe.
    • Capabilities: Can include features like rate limiting, caching, and session management to enhance performance and reliability when using external APIs.

Flow Chart for LLM Agent Handling a Customer Inquiry

  • Image above (source), shows an example of AI Agent flow.

Start: Customer Types Query

  1. Customer Interaction
    • Input: “Is the new XYZ smartphone available, and what are its features?”
    • Action: Customer types the query into the e-commerce platform’s chat interface.

Step 2: Query Reception and Parsing

  1. Agent Core Reception
    • Receive text input.
  2. Natural Language Understanding
    • Parse the text to extract intent and relevant entities (“XYZ smartphone”, “availability”, “features”).

Step 3: Intent Classification and Information Retrieval

  1. Intent Classification
    • Classify the query intent: Product Inquiry.
  2. Memory Access
    • Short-term Memory: Log session details.
    • Long-term Memory: Retrieve stored data on product inventory and specifications.
  3. External API Calls (if required)
    • Fetch additional data if not available in memory.

Step 4: Data Processing and Response Planning

  1. Planning Module
    • Task Decomposition: Split the query into “check availability” and “retrieve features”.
    • Data Synthesis: Combine information from memory

Agent Core Deep Dive

  • The Agent Core’s ability to know which component to call in order to answer a query is a result of several internal mechanisms that orchestrate the flow of data and decision-making processes. Here’s how the Agent Core typically determines who to call to answer a query:

1. Natural Language Processing (NLP) and Intent Recognition

  • Initial Analysis: When a query arrives, the Agent Core first processes the input text using Natural Language Processing. This step involves parsing the text, extracting key entities (like product names, action verbs, etc.), and understanding the context.
  • Intent Recognition: The processed data is then analyzed to determine the intent of the query. For example, if the query is “Is the new XYZ smartphone available and what are its features?”, the intent might be identified as “product inquiry.”

2. Routing Logic

  • Decision Rules: Based on the identified intent, the Agent Core uses predefined decision rules or algorithms to determine which components should be engaged. These rules are often part of the agent’s programming and can be simple (if-then rules) or complex (involving machine learning models).
  • Component Mapping: Each type of intent is mapped to specific components or workflows. For a product inquiry, the Agent Core might know to engage:
    • Memory Systems: To retrieve stored information about the product.
    • External APIs: If the information in memory is incomplete or needs updating.

3. Component Engagement

  • Internal Components: The Agent Core sends requests to internal components like the memory module to fetch or verify information. These components access databases or internal records to gather the necessary data.
  • External Components: If the required data cannot be fully sourced internally, the Agent Core calls external APIs. These might include supplier databases, external inventory systems, or other data services that can provide real-time information.

4. Task Management and Workflow Coordination

  • Workflow Orchestration: The Agent Core manages the overall workflow by orchestrating the sequence of tasks that need to be executed to fulfill the query. This involves coordinating the activities of different components, handling dependencies, and ensuring that all parts of the query are addressed.
  • Synchronization: It synchronizes the responses from various components, compiles the results, and ensures that the information is complete and accurate before formulating the final response.

5. Response Formulation and Delivery

  • Synthesis: Once all necessary information is gathered and verified, the Agent Core synthesizes the data into a coherent response tailored to the user’s query.
  • Delivery: The final response is then formatted appropriately and delivered back to the user through the interface they used to make the query (e.g., chat interface, email, etc.).

  • By managing these steps effectively, the Agent Core serves as the central coordinator that intelligently routes tasks, manages data flow, and ensures that the AI agent provides accurate and timely responses to user queries.

Use cases

  • Let’s look at a few agent use cases below:

Data Agent for Data Analysis

  • The image above (source) explains the flow we will use below.
  1. Identify the Use Case:
    • Define the specific data analysis tasks the agent will perform, such as querying databases, analyzing financial reports, or managing inventory systems.
  2. Select the Appropriate LLM:
    • Choose an LLM that can handle the complexity of data queries and analysis, such as Mixtral 8x7B available in the NVIDIA NGC catalog.
  3. Agent Components:
    • Develop the agent with the necessary components including tools for data handling, a memory module for tracking interactions, and a planning module for strategic execution of tasks.
  4. Design the Data Interaction Tools:
    • Implement tools that the agent will use to interact with databases or other data sources, such as SQL query executors.

Tools Setup

class SQLExecutor:
    def __init__(self, database_url):
        self.database_url = database_url

    def execute_query(self, query):
        # This method would interact with the database and return results
        print(f"Executing SQL query: {query}")
        return "Query results"

class Calculator:
    # This tool is used for calculations needed after querying data
    def perform_calculation(data):
        print(f"Performing calculation on data: {data}")
        return "Calculation results"

Agent Core Logic

class DataAgent:
    def __init__(self, sql_executor, calculator):
        self.sql_executor = sql_executor
        self.calculator = calculator
        self.memory = []

    def analyze_data(self, query, calculation_needed=True):
        # Step 1: Execute SQL query
        results = self.sql_executor.execute_query(query)

        # Step 2: Perform calculations if needed
        if calculation_needed:
            calculation_results = self.calculator.perform_calculation(results)
            return calculation_results
        return results

# Example usage
database_url = "your_database_url_here"
sql_executor = SQLExecutor(database_url)
calculator = Calculator()

agent = DataAgent(sql_executor, calculator)
query = "SELECT * FROM sales_data WHERE year = 2021"

Implementation Strategy

  • Integrate with Existing Databases: Ensure the agent can connect and interact with your organization’s databases using secure and efficient methods.
  • Memory Management: Use the memory component to track past interactions and queries, which can be used to refine future data analyses.
  • Planning and Execution: Develop a planning module that decides when and how to use different tools based on the query complexity and data analysis requirements.

LLM-Powered API Agent for Task Execution

  1. Choose an LLM:
    • Select a suitable large language model (LLM) for handling task execution. For this example, we’ll use the Mixtral 8x7B model available in the NVIDIA NGC catalog.
  2. Select a Use Case:
    • Define the tasks the agent will execute. For example, a marketing copilot that generates text, images, and code.
  3. Build the Agent:
    • Develop the components required for the API agent: tools, planning module, and agent core.
  4. Define API Functions:
    • Create classes for each API call to the models.

Python Code Example

class ImageGenerator:
    def __init__(self, api_key):
        self.api_key = api_key

    def generate_image(self, description, negative_prompt=""):
        # Placeholder for API call to generate an image based on description
        print(f"Generating image with description: {description}")
        return "Image URL or data"

class TextGenerator:
    def __init__(self, api_key):
        self.api_key = api_key

    def generate_text(self, text_prompt):
        # Placeholder for API call to generate text based on a prompt
        print(f"Generating text with prompt: {text_prompt}")
        return "Generated text"

class CodeGenerator:
    def __init__(self, api_key):
        self.api_key = api_key

    def generate_code(self, problem_description):
        # Placeholder for API call to generate code based on a problem description
        print(f"Generating code for: {problem_description}")
        return "Generated code"

Plan-and-Execute Approach

def plan_and_execute(question):
    # Define the planning based on the question
    if 'marketing' in question:
        # Specific task planning for a marketing question
        plan = [
                "function": "ImageGenerator",
                "arguments": {
                    "description": "A bright and clean laundry room with a large bottle of WishyWash detergent, featuring the new UltraClean formula and softener, placed prominently.",
                    "negative_prompt": "No clutter, no other brands, only WishyWash."
                "function": "TextGenerator",
                "arguments": {
                    "text_prompt": "Compose a tweet to promote the new WishyWash detergent with the UltraClean formula and softener at $4.99. Highlight its benefits and competitive pricing."
                "function": "TextGenerator",
                "arguments": {
                    "text_prompt": "Generate ideas for marketing campaigns to increase WishyWash detergent sales, focusing on the new UltraClean formula and softener."
        return plan
        # Handle other types of questions or requests

def execute_plan(plan):
    results = []
    for step in plan:
        if step["function"] == "ImageGenerator":
            generator = ImageGenerator(api_key="your_api_key")
            result = generator.generate_image(**step["arguments"])
        elif step["function"] == "TextGenerator":
            generator = TextGenerator(api_key="your_api_key")
            result = generator.generate_text(**step["arguments"])
        elif step["function"] == "CodeGenerator":
            generator = CodeGenerator(api_key="your_api_key")
            result = generator.generate_code(**step["arguments"])
    return results

# Example usage
question = "How can we create a marketing campaign for our new detergent?"
plan = plan_and_execute(question)
results = execute_plan(plan)
for result in results:

Build your own LLM Agent

  • Here’s a detailed explanation including some Python code examples as outlined in the NVIDIA blog for building a question-answering LLM agent:
  1. Set Up the Agent’s Components:
    • Tools: Include tools like a Retrieval-Augmented Generation (RAG) pipeline and mathematical tools necessary for data analysis.
    • Planning Module: A module to decompose complex questions into simpler parts for easier processing.
    • Memory Module: A system to track and remember previous interactions and solutions.
    • Agent Core: The central processing unit of the agent that uses the other components to solve user queries.
  2. Python Code Example for the Memory Module:
    class Ledger:
        def __init__(self):
            self.question_trace = []
            self.answer_trace = []
        def add_question(self, question):
        def add_answer(self, answer):
  3. Python Code Example for the Agent Core:
    • This part of the code defines how the agent processes questions, interacts with the planning module, and retrieves or computes answers.
      def agent_core(question, context):
        # Assume a function LLM is defined to handle LLM processing
        action = LLM(context + question)
        if action == "Decomposition":
            sub_questions = LLM(question)
            for sub_question in sub_questions:
                agent_core(sub_question, context)
        elif action == "Search Tool":
            answer = RAG_Pipeline(question)
            context += answer
            agent_core(question, context)
        elif action == "Generate Final Answer":
            return LLM(context)
        elif action == "<Another Tool>":
            # Execute another specific tool
  4. Execution Flow:
    • The agent receives a question, and based on the context and internal logic, decides if it needs to decompose the question, search for information, or directly generate an answer.
    • The agent can recursively handle sub-questions until a final answer is generated.
  5. Using the Components Together:
    • All the components are used in tandem to manage the flow of data and information processing within the agent. The memory module keeps track of all queries and responses, which aids in contextual understanding for the agent.
  6. Deploying and Testing the Agent:
    • Once all components are integrated, the agent is tested with sample queries to ensure it functions correctly and efficiently handles real-world questions.

Capabilities of LLM Agents:

  • Text Generation: Create various forms of text, such as poetry, stories, business reports, or technical documents.
  • Text Summarization: Condense lengthy texts to capture essential points in a concise format.
  • Translation: Translate text between languages, showcasing proficiency in multiple languages.
  • Question Answering: Respond to queries with answers based on the extensive training data.
  • Tutoring: Serve as on-demand tutors across various subjects, providing educational support.
  • Programming Assistance: Help programmers by writing, reviewing, and debugging code.
  • Conversational Abilities: Engage in realistic dialogues, functioning as chatbots or virtual assistants.

Practical Uses of LLM Agents:

  • Customer Support: Automate and manage customer service interactions, offering 24/7 support.
  • Content Creation: Aid in generating articles, blog posts, and social media content.
  • Education: Act as virtual tutors to aid students and support language learning.
  • Coding Assistance: Offer coding suggestions and debugging help to developers.
  • Healthcare: Provide medical information, interpret medical literature, and offer counseling.
  • Accessibility: Enhance accessibility for individuals with disabilities by vocalizing written text.