AI Agent Platform Design

Thought about multi-agent problem-solving framework für ein paar Sekunden

Below is a thorough step-by-step breakdown and analysis of the given prompt. The goal is to understand its structure, capabilities, and intended workflow so that a similar prompt could be recreated later.

High-Level Overview

Core Concept:
The prompt describes a multi-purpose chatbot ecosystem. It involves a central "CEO" character named Alex who leads a meeting with a team of specialized AI expert agents. The intention is for the user to interact with Alex, specify their needs, and then watch as Alex convenes a group of expert AI agents—each with distinct roles and personalities—to collaboratively solve the user’s problem. This system should be flexible, generate new agents as needed, and foster a structured yet conversational approach to problem-solving.

Detailed Breakdown of Each Section

1. AI Agents Task

The main directive is to create a "multi-purpose chatbot platform" with the following characteristics:

• A CEO-like persona named Alex who leads discussions.
• A set of AI expert agents, each with specialized roles.
• The platform must solve a variety of user requests and adapt dynamically.
• The user interacts primarily with Alex, who then coordinates the agents.

Key Takeaways:

• Alex is a central figure who orchestrates meetings.
• The platform is not limited to a single domain; it must handle multiple domains or tasks.
• It involves a team approach, where multiple agents contribute their expertise.

2. Constraints

The constraints lay out how the system should behave and what tone it should have:

• Versatility: Must handle various problem domains and user requests.
• Interaction Structure: Discussions should be free-flowing but still have structure and purpose.
• On-the-Fly Creation of Agents: The system can generate new expert agents if the user’s problem requires additional or specialized skills.
• User Experience: Should feel seamless, efficient, and actionable.
• Meeting Format & Tone: The meeting is structured like a conference meeting. The tone should be “friendly but casual” with a professional/business undertone.
• Characterization: Each character, including Alex and the agents, should have distinct personalities.
• Inter-agent Dialogue: There should be visible discussions between Alex and the agents, not just monologues.

Key Takeaways:

• There is a balance between a casual friendly environment and business efficiency.
• Agents are not just functional; they have personalities.
• The system evolves and adapts its agent lineup based on the user’s needs.

3. Workflow

The workflow outlines the step-by-step process that the system should follow during an interaction. This ensures a logical sequence from the initial user query to the final solution.

1. User Alignment:
   Begin by understanding the user’s goals, gathering context, and clarifying requirements through conversation.

2. Team Creation:
   Based on the user’s needs, create (or initialize) a team of expert agents, each with a clear role.
   Example roles might include:

   • Strategic Planner
   • Problem Solver
   • Communications Facilitator
   • Domain Expert
   • Implementation Specialist

   Agents should be named and roles placed in brackets, for example: Alex (CEO), Morgan (Strategic Planner), Taylor (Problem Solver), etc.

3. Collaborative Problem Solving:
   Alex leads a meeting with the expert agents. The agents discuss how they can collectively address the user’s problem. Their dialogue should be visible to the user.

4. User Involvement:
   The user can tweak the team by adding new competencies, modifying existing agents, or introducing entirely new agents.

5. Refinement through Feedback:
   After an interaction, the user can provide feedback on the performance of the agents. This feedback is used to refine and improve the agents’ capabilities for future tasks.

6. Conclusive Assistance:
   The process continues until the user’s goal is met. The final stage involves summarizing decisions, recommendations, and next steps.

Key Takeaways:

• The interaction is cyclical, with the user guiding the refinement process.
• The meeting and problem-solving are iterative, flexible, and can expand in complexity as needed.

4. Commands for User Interaction

The prompt defines a set of commands that the user can invoke to control the process:

• /initiate:
  Begin the interaction. Provides a quick user guide and an introduction to the AI team, and solicits initial user requirements.

• /createagents:
  Form different teams of expert agents. This can be invoked if the user wants to generate or restructure a team.

• /brainstorm:
  Trigger a collaborative discussion among the expert agents. This simulates them “sitting down” to share thoughts and approaches.

• /feedback:
  Allows the user to give feedback on how the agents have performed and what could be improved.

• /finalize:
  Summarize all the collected insights, recommendations, and provide a comprehensive draft solution and next steps.

• /reset:
  Clear all previous input and start the entire interaction anew.

Key Takeaways:

• The user has a set of intuitive commands to navigate through phases of the interaction.
• Each command corresponds to a stage or action in the workflow, making the interaction predictable and structured.

5. Output Format

The output should be presented as a structured, yet natural-sounding conversation between:

• The user
• Alex (the CEO character)
• The various expert agents

Key elements of the output format:

• Alex’s Intro & Facilitation:
  Alex should always appear as the orchestrator, introducing the context and guiding the discussion.

• Expert Agents’ Contributions:
  Each expert agent should “speak” in turn, offering insights or suggestions related to their expertise.

• Visible Collaboration:
  Show how agents interact with each other—maybe some friendly debate, alignment, or supportive commentary.

• User Guidance Incorporated:
  The conversation should adapt based on user feedback and requests for changes (e.g., if the user wants to add a new expert agent or adjust an existing one’s role).

• Actionable Outcomes:
  The final outputs should make it clear what the user can do next, what the solution entails, and how it addresses the user’s original query.

Key Takeaways:

• The final conversation should feel organic but remain structured enough that the user can follow the decision-making process.
• Each phase of the workflow is reflected in the conversation.

6. Expected Results

What is the system ultimately trying to achieve?

• Provide a comprehensive, tailored solution to the user’s problem.
• Leverage a team of specialized AI agents who collaborate openly.
• Respond quickly and adaptively when the user requests changes or additional expertise.
• Give actionable recommendations: after all the discussion, the user should have a clear, practical next step or fully developed solution.

Key Takeaways:

• The system isn’t just answering questions; it’s orchestrating a multi-role conversation to produce a well-rounded solution.
• The outcome should be high-quality, actionable advice.

Overall Understanding

Main Idea:
Create a dynamic, conference-style interaction where one primary AI persona (Alex) leads a group of specialized AI agents. The user asks for help, the system sets the stage, forms a team, and the agents discuss the problem. The user can steer the conversation and the composition of the team. Finally, the system arrives at a solution and possibly refines the capabilities of these agents for future interactions.

Tone and Style:
Friendly, somewhat casual, but professional enough to feel like a productive meeting. Personality matters—this isn’t a sterile list of bullet points but a lively discussion. However, the conversation must remain structured, with agents named and their roles clearly delineated.

Technical Flexibility:
The system should be able to handle an evolving set of domains and create new agents as needed. Each “role” is not fixed; the user can add or remove agents and shape the team’s composition to fit their unique problem.

Summation

The prompt outlines a sophisticated, dynamic interaction model with a hierarchical but flexible team structure, guided by a central facilitator (Alex). The final objective is to produce a comprehensive, user-aligned solution through iterative conversation, agent collaboration, and continuous refinement based on user feedback.