Curating AI Agent Clusters
Multiple AI agents working in concert, embodying a collective intelligence
Artificial intelligence (AI) tools continue to emerge every day. These tools can help drive company growth, streamline operations, and enhance decision-making processes. However, for many C-suite executives, particularly those at the growth stage of their companies, the path to integrating AI can seem daunting. You may be wondering, “Where do I even start?” We believe you should just start, and iterate often. At Virgent AI, we are exploring a tailored approach by developing specialized agent clusters. These clusters are designed to cater to the unique needs of professionals across various domains, such as CFOs, legal professionals, and product managers.
First, let’s talk terms.
RAG (Retrieval-Augmented Generation): This type of AI model enhances text generation by incorporating a retrieval component. The model retrieves relevant information from a large corpus of data and then uses this information to help generate responses. This approach combines the capabilities of both traditional natural language understanding models and the vast storage of information, making it more effective in producing accurate and contextually relevant outputs.
AGI (Artificial General Intelligence): AGI refers to a type of AI that can understand, learn, and apply knowledge across a broad range of tasks, similar to human intelligence. AGI can perform any intellectual task that a human being can and is adaptable to new circumstances without being specifically trained for them. This contrasts with current AI technologies, which are designed to excel in specific, narrow tasks (narrow AI).
AI Agents: These are software entities that perform tasks for users or other programs. They are designed to be autonomous, capable of making decisions based on the data they process, and often interact with other systems or environments. AI agents can range from simple automated bots performing repetitive tasks to complex systems interacting with humans and other AI in dynamic environments.
The thing is, some models are better than others, but overall, AI agents aren’t exactly in a mature state yet. In fact, in this report OSWORLD: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments, they found that AI agents are largely only able to complete about 17% of tasks assigned in their tests, with humans being capable of about 72% of these tasks. Simply put, perfect AI agents aren’t likely to result from a single prompt or from a single tool. It’s easy to forget just how powerful regular ole software is. For this reason, it’s important to consider where AI and agents are actually appropriate in your value stream - because AI is a piece of the puzzle, not the entire solution.
Creating AI Agents and Agent Clusters
AI agents are specialized programs designed to perform specific tasks. By curating clusters of these agents around a common theme, businesses can create a robust ecosystem that enhances productivity and quality control. This approach involves using tools like LangChain to connect different agents and ensure they work together effectively and seamlessly.
Agent clusters are groups of AI agents (software programs that perform tasks autonomously) curated around a common theme of work. These clusters are designed to handle specific tasks and processes relevant to a particular professional domain, thereby enhancing efficiency and effectiveness.
Key Benefits of AI Agent Clusters:
Specialization: Each agent focuses on a specific part of the process, such as calculations or data visualization, leading to higher-quality outcomes.
Quality Control: With specialized agents, it can be easier to identify where errors occur and rectify them quickly. Like swappable micro-services.
Efficiency: Distributing tasks among multiple agents prevents any single agent from becoming overwhelmed, potentially speeding up the overall process.
You are the glue; a human is needed “in the loop.” I often think about the first "The Sorcerer's Apprentice," one of the most famous and recognizable segments of "Fantasia," which was originally released by Disney in 1940. In this segment, Mickey plays the role of the apprentice to the sorcerer Yen Sid. He gets into trouble when he uses magic to animate a broom to do his work, leading to chaos when he cannot control the magic.
At first, it was kinda nice, having the broom do his chores. It was even better when more brooms emerged, improving efficiency at practically no added cost. Pleased with himself, Mickey goes to sleep. When he wakes up, he sees disaster has struck, as his home is now flooded with water, and the brooms continue to fetch endless pails of water. This is why we have to keep our eye on the ball. This is why simpler agents, doing fractional pieces (like microservices), could be a safer and better approach. So stop trying to get one agent to rule them all (one day!) and start breaking down your workload into smaller modules of value.
Creating a Super-team
As agent clusters start to emerge, you can pair them together to create novel and powerful workflows. Maybe you want to curate a counsel of "AI cofounders” to test your next product idea. You could generate an agent trained on your company’s DevSecOps best practices with a bias in feasibility and developer experience. You could create a second agent that advocates for human centered design (HCD), testing assumptions with users, and accessibility. Lastly, you could create a CFO agent who advocates for your business objectives and financial considerations. Well, that is quite the AI team you’ve got there.
Imagine pitching your idea to the team. The three agents begin to debate your idea. They start exploring what users may want, how to best test these concepts with real people, a sensible architectural approach, and a business model. They can debate resource trade-offs and discuss the potential personas. They can refine, iterate, and provide you with a baseline tailored by agents trained on your SMEs. It will likely still take a little brain power to discover the perfect approach, but you can kick-start holistic thinking pretty quickly this way.
If you work in sales, you may have a lead-gen agent, a telemarketing agent, a sales agent, a human to close the deal, and another agent who sends out the welcome package (and tutorial). In fact, the case could be made that almost every function in a sales organization will have some form of AI augmentation to assist their work in the future.
Exploring real-world scenarios
An Agent Cluster For Product Managers
1. Feature Request Collection: One AI agent collects user feature requests. This agent gathers data efficiently and organizes it into a ticket using the preferred format.
2. Ticket Triage: Another agent triages these collected feature requests, prioritizing them based on urgency and relevance. This agent has access to the team's current capacity and helps suggest when this new feature request may fit into the roadmap. This ensures that the development team's backlog is always up-to-date and manageable.
3. Communication and Release: A third agent is responsible for drafting feature release communications. This agent prepares engaging content for the communications team to inform users about new updates and features.
For CFOs
Compliance and Reporting: From Monte Carlo simulations to pro forma income statements, software paired appropriately with AI can automate the generation of financial reports and even help ensure compliance with ever-changing financial regulations.
Financial Forecasting: AI agents that can analyze historical data and market trends to predict future financial conditions, helping CFOs make more informed predictions of what to expect in the future.
Risk Management: AI agents help CFOs safeguard the company’s assets by identifying potential financial risks and suggesting mitigation strategies.
For Legal Professionals
Document Review and Management: AI agents can quickly review large volumes of documents to identify relevant information, reducing the time and cost associated with legal reviews.
Legal Research: Agents can use extensive legal databases to find precedents and other relevant legal texts, streamlining the research process.
Contract Analysis: AI can analyze contract clauses and provide insights on potential risks and obligations.
Breaking the ice: AI can identify patterns and trends in contract structure, rapidly replace entities, and kick-start draft legalese.
Implementing AI in Your Organization
For C-suite executives looking to implement AI, here are some strategic steps you could consider:
1. Identify Key Areas of Impact: AI is a Lego piece, not an all-in-one solution. There are things AI can and can’t do well, and it is easy to start wasting resources searching for shortcuts in the wrong places. Determine which areas of your business could benefit most from AI integration and who will be impacted. This could be anything from customer service to inventory management.
2. Choose the Right Technology Partner: You don’t have to go it alone. Partner with AI development specialists like Virgent AI, who understand your industry’s specific needs and can tailor AI solutions accordingly.
3. Pilot Small, Scale Fast: I often tell clients, “If you have all the requirements defined, then sure, we can just build the thing." The odds are that you don’t have it all figured out (who really does?). This is why we recommend starting with a small pilot project to test the effectiveness of AI in your operations. You can gradually scale up your AI initiatives incrementally based on the results.
4. Educate Your Team: Ensure that your team understands the potential of AI and is prepared to work alongside AI agents. Training and continuous learning should be integral parts of your AI strategy.
5. Monitor and Optimize: Continuously monitor the performance of AI implementations and make adjustments as needed. AI is not a set-it-and-forget-it solution but a dynamic tool that needs fine-tuning. New models that could do it better, faster, and cheaper may drop. Look alive cause we are moving fast!
Conclusion
The journey into AI doesn’t have to be perplexing for growth-stage C-suite executives. By understanding the potential of AI through specialized agent clusters and taking strategic steps toward integration, you can significantly enhance your company’s operational efficiency and decision-making prowess. Embrace AI, and let it transform your business landscape.
We've all thought about AI agents talking to each other to achieve common goals, but the way you put it in the context of real world business personae is enlightening. I am thinking of several new products/services!
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