The Multi-Agent Architecture for Problem Solving is a sophisticated system that employs a systematic approach to tackle complex problems by integrating specialized modules called agents. This architecture ensures that every aspect of a given problem is addressed through the collaboration of these agents, offering comprehensive and effective solutions.
- Modular Design: The architecture consists of multiple specialized agents, each designed to handle specific tasks, allowing for a divide-and-conquer approach to problem-solving.
- Iterative Process: The agents work together in an iterative manner, refining and improving the solution until it meets the original objectives, ensuring high-quality results.
- Specialization: Each agent is trained to excel in its designated task, whether it's problem definition, decomposition, generation, execution, testing, or editing, enabling efficient and accurate problem-solving.
- Collaborative Approach: The agents communicate and collaborate with each other seamlessly, sharing information and building upon each other's work to arrive at the best possible solution.
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Problem Definer Agent:
- Clarifies the user's objectives through iterative questioning.
- Ensures a clear understanding of the problem before passing it to the Decomposer Agent.
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Decomposer Agent:
- Breaks down the problem into manageable subtasks.
- Explores alternative approaches for each subtask.
- Passes individual subtasks to the Generator Agent.
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Generator Agent:
- Determines the number of Worker Agents needed based on the subtasks.
- Generates custom prompts specifying individual tasks for each Worker Agent.
- Sends the output to the respective Worker Agents.
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Worker Agents:
- Specialized in solving specific subtasks, such as coding, analysis, data manipulation, etc.
- Plan and explain their reasoning step by step.
- Iterate on individual problems until correct solutions are found.
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Compiler Agent:
- Combines the solutions from Worker Agents into a unified final solution.
- Evaluates and selects the most promising combined solution.
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Tester Agent:
- Executes and evaluates the final solution against the original problem objectives.
- Provides detailed feedback on the strengths and weaknesses of the solution.
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Error Identifier Agent:
- Identifies issues or flaws in the solution based on the Tester's feedback.
- Categorizes errors or flaws and prioritizes them based on their impact.
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Editor Agent:
- Suggests edits or improvements based on the identified errors.
- Iterates the editing process based on the Tester's feedback.
- Enhances workflow efficiency by refining the solution.
The Multi-Agent Architecture for Problem Solving can be applied to a wide range of domains, including:
- Software Development: Automating code generation, testing, and debugging processes.
- Data Analysis: Extracting insights from complex datasets and generating meaningful reports.
- Research and Development: Conducting comprehensive literature reviews and generating innovative ideas.
- Business Strategy: Analyzing market trends, competitor analysis, and generating strategic recommendations.
This project is licensed under the MIT License.
We are continuously working on enhancing the Multi-Agent Architecture for Problem Solving. Some planned future enhancements include:
- Developing a user-friendly interface for easier interaction with the system and visualization of the problem-solving process.
- Optimizing the communication protocols between agents to further improve efficiency and reduce latency.
- Expanding the library of specialized agents to cover a wider range of problem domains and tasks.
- Integrating machine learning capabilities to enable agents to learn and adapt based on past problem-solving experiences.