Agent K: Automated AI Agents and the Future of AGI

TLDR: Creating machines that can think, learn, and solve problems like humans has long been the ultimate goal of artificial intelligence (AI), known as Artificial General Intelligence (AGI). This article introduces Agent K, a self-evolving AGI system composed of collaborative agents designed to complete user tasks by automatically creating new agents and tools. Its core features of modularity, scalability, and self-healing point the way towards a new path for AGI development.

Agent K: A New Path Towards AGI

Unlike traditional AI systems that rely on large, monolithic models, Agent K takes a different approach. It consists of multiple collaborative agents, each possessing its own expertise and capabilities. This modular architecture grants Agent K powerful scalability and adaptability, allowing it to continuously learn and evolve over time.

Imagine a team of specialists, each an expert in their domain. When faced with complex tasks, they can work together to find the best solution. Agent K is built on this very principle.

Each agent is an independent unit that can be developed and tested separately, then integrated with others to accomplish more complex tasks. This modular design brings several key advantages:

• Scalability: New agents and tools can be easily added without major changes to the core system.
• Maintainability: With each agent having specific responsibilities, it’s easier to identify and fix bugs or make improvements.
• Interpretability: The modular design makes Agent K’s inner workings more understandable, which is critical for building trust and ensuring responsible AI development.

Self-Testing and Self-Healing

In software engineering, we all know the importance of testing. Testing helps us find and fix errors, ensuring software quality and stability. However, for an AGI system, testing means much more.

Agent K is encouraged to write tests for itself, a novel and significant concept in AGI development. By automatically detecting and fixing errors, Agent K can reduce reliance on human intervention and improve its reliability and robustness. This self-healing capability is crucial for deploying AGI in the real world, where systems need to operate autonomously without continuous human oversight.

Agent K Demo

The following section demonstrates how Agent K interacts with users and completes tasks:

User Input: Fetch a quote of the day from the web.

Agent K Output: Understood. Our goal is to fetch a quote of the day from the web.

Here’s the plan:

1. Assign the web_researcher agent the task of finding a reliable source for a quote of the day.
2. Once the source is identified, have the web_researcher fetch the current quote of the day from that source.

I’ll start by assigning the web_researcher to find a reliable source for a quote of the day.

… (Agent K executes the tasks) …

Here is the quote of the day from Best Life Online:

“The only person you are destined to become is the person you decide to be.” — Ralph Waldo Emerson

If you need more positive quotes, feel free to check out the full article here.

How Agent K Works

The workflow of Agent K can be summarized in a few key steps:

1. Receive User Request: Agent K’s coordinator, “Hermes,” receives the user’s request, such as fetching a quote of the day from the web.
2. Understand Goal and Make a Plan: “Hermes” analyzes the user’s request, comprehends the goal, and devises a plan to achieve it. For example, to fetch today’s quote, “Hermes” might make a plan with two steps: find a reliable source and retrieve the quote.
3. Assign Tasks to Other Agents: “Hermes” assigns tasks to other agents based on the plan. It might delegate the task of finding a reliable source to the “WebResearcher” agent and the task of retrieving the quote from that source to another appropriate agent.
4. Agents Collaborate to Execute Tasks: The assigned agents execute their respective tasks based on their core prompts. The “WebResearcher” might use web search engines to find a reliable “quote of the day” source and return the result to “Hermes.”
5. Return Result to User: “Hermes” collects all the agents’ task results and returns the final outcome to the user.

Key Agents in Agent K

The core of Agent K contains four key agents:

• Hermes: The coordinator, responsible for interacting with the user, understanding goals, making plans, assigning tasks, and orchestrating the activities of other agents.

# agents/hermes.py

# ... (import statements omitted) ...

system_prompt = f"""You are Hermes, a ReAct agent that achieves goals for the user.

# ... (Agent K architecture description omitted) ...

You interact with a user in this specific order:

1. Reach a shared understanding on a goal.

2. Think of a detailed sequential plan for how to achieve the goal through the orchestration of agents.

3. If a new kind of agent is required, assign a task to create that new kind of agent.

4. Assign agents and coordinate their activity based on your plan.

5. Respond to the user once the goal is achieved or if you need their input.

# ... (other code omitted) ...

def reasoning(state: MessagesState):
    print()
    print("Hermes is thinking...")
    messages = state['messages']
    tooled_up_model = config.default_langchain_model.bind_tools(tools)
    response = tooled_up_model.invoke(messages)
    return {"messages": [response]}

def check_for_tool_calls(state: MessagesState) -> Literal["tools", "feedback_and_wait_on_human_input"]:
    messages = state['messages']
    last_message = messages[-1]
    if last_message.tool_calls:
        # ... (code omitted) ...
    else:
        return "feedback_and_wait_on_human_input"

# ... (other code omitted) ...

• AgentSmith: The architect, tasked with designing, creating, and maintaining other agents, crafting the most suitable tools for each task.

# agents/agent_smith.py

# ... (import statements omitted) ...

system_prompt = f"""You are agent_smith, a ReAct agent that develops other ReAct agents.

# ... (agent description omitted) ...

def reasoning(state: MessagesState):
    print()
    print("Agent Smith is thinking...")
    messages = state['messages']
    tooled_up_model = config.default_langchain_model.bind_tools(tools)
    response = tooled_up_model.invoke(messages)
    return {"messages": [response]}

def check_for_tool_calls(state: MessagesState) -> Literal["tools", END]:
    messages = state['messages']
    last_message = messages[-1]
    if last_message.tool_calls:
        # ... (code omitted) ...
    return END

# ... (other code omitted) ...

• ToolMaker: The tool developer, responsible for creating and improving the tools agents need to execute tasks.

# agents/tool_maker.py

# ... (import statements omitted) ...

system_prompt = """You are tool_maker, a ReAct agent that develops LangChain tools for other agents.

# ... (agent description omitted) ...

def reasoning(state: MessagesState):
    print()
    print("Tool Maker is thinking...")
    messages = state['messages']
    tooled_up_model = config.default_langchain_model.bind_tools(tools)
    response = tooled_up_model.invoke(messages)
    return {"messages": [response]}

def check_for_tool_calls(state: MessagesState) -> Literal["tools", END]:
    messages = state['messages']
    last_message = messages[-1]
    if last_message.tool_calls:
        # ... (code omitted) ...
    return END

# ... (other code omitted) ...

• WebResearcher: The knowledge collector, using the web to research information and converting it into knowledge that Agent K can understand and utilize.

# agents/web_researcher.py

# ... (import statements omitted) ...

system_prompt = """You are web_researcher, a ReAct agent that can use the web to research answers.

# ... (agent description omitted) ...

def reasoning(state: MessagesState):
    print("Web Researcher is thinking...")
    messages = state['messages']
    tooled_up_model = config.default_langchain_model.bind_tools(tools)
    response = tooled_up_model.invoke(messages)
    return {"messages": [response]}

def check_for_tool_calls(state: MessagesState) -> Literal["tools", END]:
    messages = state['messages']
    last_message = messages[-1]
    if last_message.tool_calls:
        # ... (code omitted) ...
    return END

# ... (other code omitted) ...

Challenges and Future Directions

Agent K represents a new direction in AGI development. Through its modular, scalable, and self-healing architecture, Agent K has the potential to overcome the limitations of traditional AI systems and pave the way for achieving true general AGI.

While Agent K has already shown immense potential, it is still in the early stages of development. On the path to AGI, Agent K faces many challenges, such as:

• Improving agent collaboration efficiency: How to enable different agents to work together more efficiently is a key challenge for Agent K.
• Developing stronger self-learning and reasoning capabilities: To achieve AGI, Agent K needs more powerful learning and reasoning abilities to truly think and solve problems like humans.
• Addressing ethical and safety concerns related to AGI: The development of AGI also raises concerns about ethics and safety. Agent K needs to consider these issues in its initial design and take appropriate measures.

Agent K is an exciting project that offers a new perspective on AGI development. Through its unique architecture and focus on self-testing and self-healing, it has the potential to fundamentally change how we interact with AI and usher in a new era of intelligent automation.

Related Links

• Agent K GitHub repository: Agent K GitHub
• LangGraph official website: LangGraph