Artificial Intelligence (AI) agents are designed to interact with their environment in a structured manner. A foundational concept in building intelligent agents is the Thought-Action-Observation (TAO) cycle, which enables agents to reason, act, and learn from their experiences. This blog delves into each component of the TAO cycle, providing detailed explanations and real-world examples to illustrate how AI agents operate effectively.
The TAO cycle consists of three iterative steps:
Thought: The agent processes the current state, evaluates available tools, and decides on the best course of action based on the given input.
Action: The agent executes the chosen action, such as searching the web, generating an image, or querying a model.
Observation: The agent reviews the output of its action, updates its state, and determines if additional steps are required.
This cycle allows AI agents to dynamically adjust their responses, refine their understanding, and improve the accuracy of their outputs.
The “Thought” phase represents the agent’s internal deliberation, where it processes current observations and decides on the next action(s) to take. This involves:
Analyzing Information: Interpreting the input data or user queries.
Planning: Breaking down complex tasks into manageable steps.
Decision Making: Selecting the most appropriate action based on the analysis.
This internal dialogue leverages the agent’s Large Language Model (LLM) capabilities to simulate human-like reasoning
Here are some illustrative examples of the types of thoughts an AI agent might have:
Planning: “I need to break this task into three steps: 1) gather data, 2) analyze trends, 3) generate report.”
Analysis: “Based on the error message, the issue appears to be with the database connection parameters.”
Decision Making: “Given the user’s budget constraints, I should recommend the mid-tier option.”
Problem Solving: “To optimize this code, I should first profile it to identify bottlenecks.”
Memory Integration: “The user mentioned their preference for Python earlier, so I’ll provide examples in Python.”
Self-Reflection: “My last approach didn’t work well; I should try a different strategy.”
Goal Setting: “To complete this task, I need to first establish the acceptance criteria.”
Prioritization: “The security vulnerability should be addressed before adding new features.”
These thoughts enable the agent to handle tasks methodically and adaptively.
The ReAct (Reasoning and Acting) approach is a prompting technique that encourages the agent to think step by step before acting. By appending prompts like “Let’s think step by step,” the agent is guided to:
Decompose Problems: Breaking down complex queries into simpler sub-tasks.
Plan Sequentially: Formulating a sequence of actions to achieve the goal.
Reduce Errors: By reasoning through each step, the likelihood of mistakes decreases.
This method enhances the agent’s ability to handle complex tasks by promoting a structured reasoning process.
Consider an AI agent tasked with providing weather information:
User Query: “What’s the current weather in New York?”
Agent’s Thought: “I need to find the current weather for New York. I’ll use the weather API to retrieve this information.”
Action: The agent calls the weather API with “New York” as the parameter.
Observation: The API returns: “It’s 15°C and partly cloudy in New York.”
Final Response: “The current weather in New York is 15°C and partly cloudy.”
This sequence demonstrates how the agent’s internal reasoning guides its actions and responses.
In the context of AI agents, an Action is a deliberate operation executed by the agent to interact with its environment. These actions can range from retrieving information to controlling devices. For instance, a customer service agent might:
Retrieve customer data
Offer support articles
Transfer issues to a human representative
Agents can perform various types of actions, each serving different purposes:
Actions aimed at collecting data, such as:
Performing web searches
Querying databases
Retrieving documents
Example: An agent searches online to find the latest news articles on a specific topic.
Utilizing external tools or APIs to perform tasks:
Making API calls
Running calculations
Executing code
Example: An agent uses a weather API to fetch the current temperature in New York.
Interacting with digital interfaces or physical devices:
Manipulating digital interfaces
Controlling physical devices
Example: An agent adjusts the thermostat settings in a smart home system.
Engaging in interactions:
Chatting with users
Collaborating with other agents
Example: An agent responds to a user’s query in a customer support chat.
Agents can specify actions in different formats:
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Example:
"action": "get_weather",
"action_input": {"location": "New York"}
This structured format allows for easy parsing and execution by external tools.
The agent generates executable code blocks, typically in a high-level language like Python.
Example:
result = get_weather("New York")
final_answer = f"The current weather in New York is: {result}"
print(final_answer)
This approach offers greater flexibility and expressiveness, allowing for complex logic and operations.
A specialized form of JSON Agent, fine-tuned to generate a new message for each action, invoking specific functions with defined arguments.
Example:
"name": "get_weather",
"arguments": {
"location": "New York"
This method ensures precise function invocation and is particularly useful in structured environments.
To ensure actions are executed correctly, agents employ the Stop and Parse approach:
Structured Output: The agent outputs the intended action in a clear, predetermined format (JSON or code).
Halting Generation: Once the action is fully defined, the agent stops generating additional tokens. This prevents extra or erroneous output.
Parsing and Execution: An external parser reads the formatted action, determines which tool or function to call, and extracts the required parameters.
Example:
"action": "get_weather",
"action_input": {"location": "New York"}
This output can be easily parsed to call the appropriate function with the specified arguments.
An Observation is the information an AI agent receives after performing an action. This feedback can include:
Data from APIs: Such as weather information or stock prices.
Error Messages: Indicating issues encountered during execution.
System Logs: Providing detailed records of operations.
Sensor Readings: For agents interacting with physical environments.
These observations inform the agent’s subsequent thoughts and actions, allowing it to adapt to new information and changing circumstances.
In the Thought-Action-Observation cycle:
Thought: The agent analyzes the input and plans the next action.
Action: The agent performs the planned action, such as calling an API.
Observation: The agent observes the result of the action and integrates this feedback into its knowledge base.
This cycle repeats as necessary, allowing the agent to adapt and refine its actions based on new information.
Observations can be categorized into several types:
System Feedback: Messages indicating the success or failure of an action.
Data Changes: Alterations in data sources or environments.
Environmental Data: Information from sensors or external systems.
Response Analysis: Results returned from actions like API calls or computations.
Time-based Events: Occurrences triggered by time constraints or schedules.
After an action is performed, the agent processes the resulting observation through the following steps:
Parsing the Action: Identifying the function called and its arguments.
Executing the Action: Performing the action using the specified parameters.
Appending the Result: Integrating the outcome into the agent’s context for future reference.
This process ensures that the agent maintains an up-to-date understanding of its environment and can make informed decisions moving forward.
Consider an AI agent tasked with providing weather information:
User Query: “What’s the current weather in New York?”
Thought: “I need to find the current weather for New York. I’ll use the weather API to retrieve this information.”
Action: The agent calls the weather API with “New York” as the parameter.
Observation: The API returns: “It’s 15°C and partly cloudy in New York.”
Final Response: “The current weather in New York is 15°C and partly cloudy.”
This sequence demonstrates how the agent’s internal reasoning guides its actions and responses.
Let’s consider an AI agent tasked with providing weather information:
User Query: “What’s the current weather in New York?”
Thought: “I need to find the current weather for New York. I’ll use the weather API to retrieve this information.”
Action: The agent calls the weather API with “New York” as the parameter.
Observation: The API returns: “It’s 15°C and partly cloudy in New York.”
Final Response: “The current weather in New York is 15°C and partly cloudy.”
This sequence demonstrates how the agent’s internal reasoning guides its actions and responses, adapting based on the feedback received.
The Thought-Action-Observation cycle is fundamental to the operation of AI agents, enabling them to reason, act, and learn from their environment. By understanding and implementing each component — Thought, Action, and Observation — developers can create intelligent agents capable of complex decision-making and adaptive behavior.
Ecosystems, libraries, and foundations to build on. Orchestration frameworks, agent platforms, and development foundations.