In today’s rapidly evolving technological landscape, we’re witnessing the emergence of a fascinating new paradigm in software engineering: Multi-Agent AI Systems. Let’s explore a unique experiment that demonstrates how AI agents can work together to solve problems, and what this means for the future of software development.

Detailed Experiment Implementation

The Game Rules

The experiment is structured as a role-playing game with specific meta-rules:

- Organizations contain one or more agents
- Each agent has exactly one role
- Organizations can have multiple agents with the same role
- Agents cooperate via asynchronous message queues
- Each role has its own message queue
- An orchestrator manages message routing
- Messages can be grouped into jobs

The Workflow in Detail

Let’s break down how our number-summing organization (“NaiveSummers”) actually works:

  1. Initial Processing (Agent A)
    • Receives input list of numbers
    • For empty list: sends 0 to Agent D
    • For single item: sends that item to Agent D
    • For multiple items: initializes state and forwards to Agent B
  2. Distribution (Agent B)
    • Manages the list of numbers
    • Pops last number and sends to Agent C
    • When list is empty, sends final result to Agent D
  3. Calculation (Agent C)
    • Receives individual numbers
    • Adds to running total
    • Notifies Agent B when done
  4. Completion (Agent D)
    • Receives and displays final result

Role Specifications

Role A: The Initiator

Input format:
{
    "list_of_items": [numbers]
}

Possible responses:
// For empty list
{
    "instructions": [
        ["forward", "D", { "result": 0 }]
    ]
}

// For single item
{
    "instructions": [
        ["forward", "D", { "result": LAST_ITEM }]
    ]
}

// For multiple items
{
    "instructions": [
        ["open"],
        ["set", "list_of_items", LIST_OF_ITEMS],
        ["set", "intermediate_result", 0],
        ["forward", "B"]
    ]
}

Role B: The Distributor

Input format:
{
    "state": {
        "list_of_items": [numbers],
        "intermediate_result": number
    }
}

Possible responses:
// For empty list
{
    "instructions": [
        ["close"],
        ["forward", "D", { "result": INTERMEDIATE_RESULT }]
    ]
}

// For non-empty list
{
    "instructions": [
        ["pop", "list_of_items"],
        ["forward", "C", { "item": LAST_ITEM }]
    ]
}

Role C: The Calculator

Input format:
{
    "state": {
        "intermediate_result": number
    },
    "data": {
        "item": number
    }
}

Response:
{
    "instructions": [
        ["set", "intermediate_result", SUM],
        ["forward", "B"]
    ]
}

Role D: The Finalizer

Input format:
{
    "data": {
        "result": number
    }
}

Response:
{
    "instructions": [
        ["complete", RESULT]
    ]
}

Runtime Environment

The system operates within a virtual machine environment that provides several essential primitives:

  • open: Creates new state context
  • close: Finalizes state context
  • set: Updates state values
  • pop: Removes and returns last list item
  • forward: Routes messages between agents

These primitives allow the agents to maintain state and communicate effectively while performing their designated tasks.

Example Execution

For the input [1, 2, 3, 42], the workflow proceeds as follows:

  1. Agent A receives input and initializes state
  2. Agent B begins distributing numbers
  3. Agent C processes each number, updating running total
  4. Process continues until list is empty
  5. Agent D receives and displays final result (48)

The entire process is orchestrated through JSON messages, with each agent performing its specialized role while maintaining system state through the runtime environment.

Technical Implementation Notes

The implementation leverages several key technologies:

  • OpenAI’s GPT-3.5-turbo model with JSON mode
  • Azure.AI.OpenAI client library
  • Asynchronous message queues
  • State management system
  • Virtual machine for instruction execution

The system architecture is modular and extensible, allowing for potential expansion to handle more complex workflows and different types of computational tasks.

Code Equivalent

For comparison, here’s what this entire workflow accomplishes in simple pseudo-code:

def simple_sum(numbers):
    if len(numbers) == 0:
        return 0
    elif len(numbers) == 1:
        return numbers[0]
    else:
        return sum(numbers)

# The same operation that our entire agent system performs
result = simple_sum([1, 2, 3, 42])  # Returns 48

This stark contrast highlights how the multi-agent system, while vastly more complex, demonstrates important principles of distributed AI systems that could be applied to more sophisticated real-world problems.

Conclusion

While this experiment might seem like an unnecessarily complex way to perform a simple task, it demonstrates powerful concepts that could reshape software engineering. As we move toward more sophisticated AI systems, understanding these principles becomes increasingly important.

The future might not be about writing code line by line, but rather about orchestrating AI agents to collaborate effectively. This shift represents both an opportunity and a challenge for the software development community.

Remember, this isn’t just about summing numbers—it’s about understanding how future software systems might operate, and how we can prepare for this evolutionary leap in computing.

This blog post is based on an experimental project demonstrating multi-agent AI orchestration. The original experiment and documentation were created to explore the possibilities of AI agent collaboration in software systems.

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