Agents with specialized LLMs.

 Your proposed framework for combining agent models with specialized LLMs sounds promising and well-thought-out. By breaking down complex tasks into smaller, more manageable components and assigning each component to an agent paired with a fine-tuned LLM, you could potentially achieve a higher level of reliability and quality in AI-generated outputs.

Here's a step-by-step breakdown of your proposed framework as applied to the task of generating a research paper:

Agent 1 (Researcher LLM): The first agent is paired with an LLM specialized in conducting research and gathering information on a given topic. This agent generates a comprehensive set of information and data related to the research question.

Agent 2 (Fact-Checker LLM): The second agent is paired with an LLM designed specifically for fact-checking. This agent analyzes the information provided by Agent 1 and verifies the accuracy and reliability of the data, ensuring that any incorrect or misleading information is flagged and corrected.

Agent 3 (Paper Generator LLM): The third agent works with an LLM that excels in structuring and organizing information into a well-written research paper. This agent takes the verified information from Agent 2 and generates a draft of the research paper, ensuring that the content is clear, coherent, and logically organized.

Agent 4 (Final Fact-Checker LLM): The fourth agent is another fact-checking specialist that performs a final review of the research paper draft. This agent identifies any remaining inaccuracies or inconsistencies and makes any necessary corrections to ensure the highest level of accuracy in the final product.

Agent 5 (Citation and Referencing LLM): The fifth agent is paired with an LLM that excels in generating proper citations and referencing in the required markup style. This agent adds relevant sources and references to the research paper, ensuring that the work is well-supported and academically rigorous.

Agent 6 (Final Polish LLM): The final agent works with an LLM specialized in refining and polishing written content. This agent reviews the research paper, making any necessary adjustments to the language, structure, and style to ensure that the final product is of the highest quality and ready for submission or publication.

By combining the specialized skills of each LLM-paired agent, this framework could potentially address many of the current limitations of AI-generated content, ensuring that the final output is accurate, well-organized, and well-supported.

and depending on the task, a different workflow would be used.  Maybe an llm could be speciallized in generating proper workflows depending on the task and topic.

You're absolutely right! The workflow should be adaptable and tailored to the specific task at hand. Having an additional LLM specialized in generating workflows based on the given task and topic would make the entire process more efficient and effective.

This "Workflow Generator LLM" could analyze the task requirements and objectives, consider the available LLM-paired agents and their respective specializations, and then create an optimized workflow that ensures the best utilization of resources and expertise. This approach would add another layer of intelligence and adaptability to the overall framework, making it more versatile and capable of handling a wide range of tasks and topics.

To incorporate the Workflow Generator LLM into the existing framework, you could follow these steps:

Task Analysis: The Workflow Generator LLM analyzes the given task, taking into account its complexity, scope, and objectives.

Resource Assessment: The Workflow Generator LLM evaluates the available LLM-paired agents and their specializations, as well as any other relevant resources.

Workflow Creation: Based on the task analysis and resource assessment, the Workflow Generator LLM creates an optimized workflow that outlines the sequence of tasks and the specific agents responsible for each step.

Workflow Execution: The LLM-paired agents follow the workflow generated by the Workflow Generator LLM, working collaboratively to complete the task efficiently and effectively.

With this modification, your agent framework would be capable of dynamically adapting to various tasks and topics, ensuring that the most appropriate resources and workflows are utilized for each situation.

if it didn't know how to handle a task it should ask and remember those instructions

Absolutely! The ability to handle unknown tasks and learn from new information is a crucial aspect of a flexible and effective agent framework. To incorporate this capability, you could implement a "Learning Module" in the Workflow Generator LLM. Here's how it could work:

Identify Unknown Tasks: When the Workflow Generator LLM encounters a task it doesn't know how to handle, it should flag it as an "unknown task."

Request User Input: The Workflow Generator LLM should then request input from the user, asking for guidance or instructions on how to handle the unknown task.

Update Knowledge Base: After receiving the user's input, the Workflow Generator LLM should update its knowledge base with the new information, ensuring that it can handle similar tasks in the future without requiring user intervention.

Generate New Workflow: With the updated knowledge base, the Workflow Generator LLM can now create a new workflow that includes the previously unknown task, along with the user-provided instructions for handling it.

when a specialized model is needed and has not been constructed yet, could the models be automatically trained by automatically constructing a training set specialized for that topic?  Could a job description automatically be written for the agent?

Yes, that's a possibility. To handle situations where a specialized model doesn't exist, you could implement an "Auto-Trainer" module within the framework. This module would be responsible for automatically generating training data and creating new LLMs as needed. Here's how it might work:

Identify Need for Specialization: The Workflow Generator LLM detects that a task requires a specialized LLM that doesn't currently exist in the framework.

Generate Training Data: The Auto-Trainer module generates a training dataset by automatically collecting relevant information from various sources, such as online databases, publications, or other available resources. The dataset should be specific to the task's topic and cover a wide range of examples and contexts.

Train New LLM: Using the generated training data, the Auto-Trainer module trains a new LLM specialized in the required task or topic. This process may involve fine-tuning an existing LLM or training a new one from scratch.

could these specialized llms just be lora that are added to a more general purpose llm?

Yes, that's a great idea! Instead of training entirely new LLMs from scratch, you could leverage Large Language Model Fine-tuning (LoRA) to create specialized models. This approach would be more efficient and less resource-intensive. Here's how it would work:

Prepare Specialized Training Data: The Auto-Trainer module generates a dataset specific to the task or topic, as described earlier.

Create LoRA Model: The Auto-Trainer uses the specialized training data to create a LoRA model, which is essentially a distilled version of the task-specific knowledge.

Add LoRA to General Purpose LLM: Instead of creating a standalone specialized LLM, the LoRA model is added to a general-purpose LLM, effectively "injecting" the specialized knowledge into the existing model.

Use Fine-tuned LLM: The Workflow Generator LLM then assigns tasks to the general-purpose LLM with the appropriate LoRA model, enabling it to handle specialized tasks without the need for a completely separate LLM.

And if users marked a particular answer as bad, they could be asked specifically why, trying to identify which agent/model was bad so that pair can be improved.

Yes, incorporating user feedback is an excellent way to continuously improve the performance of the agent framework and the associated LLMs. You could implement a "Feedback Loop" that allows users to provide feedback on the quality of the generated outputs. Here's how it might work:

Request User Feedback: When a user receives a generated output from the agent framework, they are prompted to rate its quality (e.g., good, average, or bad) and provide additional comments or suggestions for improvement.

Identify Problematic Components: If a user marks an output as bad, the Feedback Loop analyzes the user's comments and tries to identify the specific components (e.g., agent, LLM, or LoRA) that led to the poor result.

Incorporate Improvements: Based on the user feedback, the Auto-Trainer module updates the training data or LoRA models, or adjusts the workflow as necessary. This could involve adding more examples, refining the LLM parameters, or making changes to the agent's decision-making process.