How I turned 50 noisy AI proposals into a clean, actionable to-do queue

From Noisy Proposals to Actionable Insights

I've been running a self-hosted AI system, which I've dubbed Ollama, for a while now. With 50 agents and a single RTX 4050 6GB laptop, I've learned to navigate the complexities of managing a large number of agents. Recently, I faced a challenge where I had to turn 50 noisy AI proposals into a clean, actionable to-do queue. In this post, I'll walk you through my process and the trade-offs I made to achieve this goal.

Understanding the Noise

My system consists of various agents, each with a specific role. The overseer, Atlas, is responsible for managing the other agents and ensuring they're working together seamlessly. The content agent, Axiom, generates text based on input prompts, while the SEO agent, Sentinel, analyzes website data to identify areas for improvement. Other agents, such as Forge and Scout, handle tasks like data processing and research. With so many agents producing output, it's easy for the signal-to-noise ratio to get out of hand. I had to develop a strategy to filter out the noise and focus on the most important tasks.

Prioritization and Filtering

To tackle the 50 noisy proposals, I first established a set of clear priorities. I identified the most critical tasks that required attention and assigned a score to each proposal based on its relevance and urgency. I then used this scoring system to filter out proposals that were below a certain threshold. This process helped me narrow down the list to about 20 proposals that were worthy of further evaluation. Next, I applied additional filters to remove any proposals that were duplicates or didn't align with my current goals. This brought the list down to 10 proposals that I could realistically action.

Agent Calibration and Refining

Once I had my shortened list, I worked on refining the proposals by calibrating the agents responsible for generating them. For example, I adjusted the overseer's parameters to ensure it was allocating resources effectively and that the content agent was producing high-quality text. I also fine-tuned the SEO agent to focus on the most critical website optimization tasks. By calibrating the agents, I was able to refine the proposals and make them more actionable. This process involved a significant amount of trial and error, but it ultimately paid off.

Implementation and Feedback Loop

With my refined proposals in hand, I began implementing the tasks. I created a to-do queue and assigned each task to the relevant agent. As the agents worked on their assigned tasks, I monitored their progress and provided feedback. This feedback loop was crucial in ensuring that the agents were on track and that the tasks were being completed efficiently. I also used this opportunity to gather data on the agents' performance, which helped me identify areas for further refinement. Some of the key metrics I tracked included:

• Agent utilization: ensuring that each agent was working at optimal capacity
• Task completion rate: monitoring the number of tasks completed within a given timeframe
• Quality of output: evaluating the quality of the work produced by each agent

Lessons Learned and Trade-Offs

Throughout this process, I learned several valuable lessons. First, prioritization is key when dealing with a large number of proposals. It's essential to focus on the most critical tasks and filter out the noise. Second, agent calibration is crucial in refining proposals and making them actionable. Finally, implementing a feedback loop is vital in ensuring that the agents are working efficiently and effectively. In terms of trade-offs, I had to balance the need for accuracy with the need for speed. In some cases, I had to compromise on the quality of the output to meet tight deadlines. However, by carefully evaluating the proposals and calibrating the agents, I was able to achieve a good balance between these competing demands.

Conclusion and Future Work

In conclusion, turning 50 noisy AI proposals into a clean, actionable to-do queue required careful prioritization, agent calibration, and a feedback loop. By following this process, I was able to refine the proposals and create a manageable to-do queue. Going forward, I plan to continue refining my system and exploring new ways to optimize agent performance. With a self-hosted AI system like Ollama, the possibilities are endless, and I'm excited to see what the future holds. As I continue to work on my system, I'll be sharing more insights and lessons learned, so stay tuned for future updates.