Binome Multi-agent Framework Chapter 2 - A Senior Engineer's Overview

This is Chapter 2 of our proposed LLM Multi-agent Framework.

You can find Chapter 1 here 👇

Designing LLM-Based Agents: Key Principles — Part 1

In the evolving landscape of artificial intelligence, agent frameworks have unlocked potentials for developing robust…

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There has been much discussion about AI Agents — pivotal self-contained units capable of performing tasks autonomously, driven by specific instructions and contextual understanding. In fact, the topic has become almost as widely discussed as LLMs. In this article, I consider AI Agents and, more specifically, the concept of Multi-Agents-as-a-Service from the perspective of the lead engineers, architects, and site reliability engineers (SREs) that must deal with AI agents in production systems going forward.

Context: What Problems Can AI Agents Solve?

AI agents are adept at tasks that benefit from human-friendly interactions:

1. E-Commerce: agents powered by technologies like LLM-based RAG or Text-to-SQL respond to user inquiries with accurate answers based on company policies, allowing for a more tailored shopping experience and customer journey that can revolutionize e-commerce
2. Customer Service: This is another ideal application. Many of us have experienced long waits to speak with representatives for simple queries like order status updates. Some startups — Decagon for example — are making strides in addressing these inefficiencies through AI agents.
3. Personalized Product and Content Creation: a prime example of this is Wix — for low-code or no-code website building, Wix developed a chatbot that, through interactive Q&A sessions, creates an initial website for customers according to their description and requirements.

“Humans set goals, but an AI agent independently chooses the best actions it needs to perform to achieve those goals.”

Overall, LLM-based agents would work great in mimicking natural human dialogue and simple business workflows, often producing results that are both effective and impressively satisfying.

An Engineer’s View: AI Agents & Enterprise Production Environments

Considering the benefits mentioned, have you ever wondered how AI agents would function within enterprise production environments? What architecture patterns and infrastructure components best support them? What do we do when things inevitably go wrong and the agents hallucinate, crash or (arguably even worse) carry out incorrect reasoning/planning when performing a critical task?

As senior engineers, we need to carefully consider the above. Moreover, we must ask an even more important question: how do we define what a successful deployment of a multi-agent platform looks like in the first place?

To answer this question, let’s borrow a concept from another software engineering field: Service Level Objectives (SLOs) from Reliability Engineering. SLOs are a critical component in measuring the performance and reliability of services. Simply put, SLOs define the acceptable ratio of “successful” measurements to “all” measurements and their impact on the user journeys. These objectives help us determine the required and expected levels of service from our agents and the broader workflows they support.

So, how are SLOs relevant to our AI Agent discussion?

Using a simplified view, let’s consider two important objectives — “Availability” and “Accuracy” — for the agents and identify some more granular SLOs that contribute to these:

1. Availability: this refers to the percentage of requests that receive some successful response (think HTTP 200 status code) from the agents or platform. Historically, the uptime and ping success of the underlying servers (i.e. temporal measures) were key correlated indicators of availability. But with the rise of Micro-services, notional uptime has become less relevant. Modern systems instead focus on the number of successful versus unsuccessful responses to user requests as a more accurate proxy for availability. Other related metrics can be thought of as Latency and Throughput.
2. Accuracy: this, on the other hand, is less about how quickly and consistently the agents return responses to the clients, but rather how correctly, from a business perspective, they are able to perform their tasks and return data without a human present in the loop to verify their work. Traditional systems also track similar SLOs such as data correctness and quality.

The act of measuring the two objectives above normally occurs through submission of internal application metrics at runtime, either at set time intervals (e.g. every 10 minutes), or in response to events (user requests, upstream calls etc.). Synthetic probing, for instance, can be used to mimic user requests, trigger relevant events and monitor the numbers. The key idea to explore here is this: traditional systems are deterministic to a large extent and, therefore, it’s generally more straightforward to instrument, probe and evaluate them. On the other hand, in our beautiful yet non-deterministic world of GenAI agents, this is not necessarily the case.

Note: the focus of this post is more so on the former of our two objectives — availability. This includes determining acceptance criteria that sets up baseline cloud/environmental stability to help agents respond to user queries. For a deeper dive into accuracy (i.e. defining sensible task scope for the agents, optimizing performance of few-shot methods and evaluation frameworks), this blog post acts as a wonderful primer.

Planning for Agents

The fourth installment in our “In the Loop Series,” in which we talk about what planning means for an agent and how to…

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Now, back to the things engineers need to get right to ensure infrastructure reasiness when deploying agents. In order to achieve our target SLOs and provide a reliable and secure platform, senior engineers consistently take into account the following elements:

1. Scalability: when number of requests increase (suddenly at times), can the system handle them efficiently?
2. Cost-Effectiveness: LLM usage is expensive, so how can we monitor and control the cost?
3. High Availability: how can we keep the system always-available and responsive to customers? Can agents self-heal and recover from errors/crashes?
4. Security: How can we ensure data is secure at rest and in transit, perform security audits, vulnerability assessments, etc.?
5. Compliance & Regulatory: a major topic for AI, what are the relevant data privacy regulations and other industry-specific standards to which we must adhere?
6. Observability: how can we gain real-time visibility into AI agents’ activities, health, and resource utilization levels in order to identify and resolve problems before they impact the user experience?

Sound familiar? These are similar to the challenges that modern web applications, Micro-services pattern and Cloud infrastructure aim to address.

So, now what? We propose an AI Agent development and maintenance framework that adheres to best-practices developed over the years across a range of engineering and software disciplines.

Multi-Agent-as-a-Service (MAaaS)

This time, let us borrow some of best-practices for cloud-based applications to redefine how agents are designed in production systems:

• Clear Bounded Context: Each agent should have a well-defined and small scope of responsibility with clear functionality boundaries. This modular approach ensures that agents are more accurate, easier to manage and scale independently.
• RESTful and Asynchronous Inter-Service Communication: Usage of RESTful APIs for communication between users and agents, and leveraging message brokers for asynchronous communication. This decouples agents, improving scalability and fault tolerance.
• Isolated Data Storage per Agent: Each agent should have its own data storage to ensure data encapsulation and reduce dependencies. Utilize distributed data storage solutions where necessary to support scalability.
• Containerization and Orchestration: Using containers (e.g. Docker) to package and deploy agents consistently across different environments, simplifying deployment and scaling. Employ container orchestration platforms like Kubernetes to manage the deployment, scaling, and operational lifecycle of agent services.
• Testing and CI/CD: Implementing automated testing (unit, integration, contract, and end-to-end tests) to ensure the reliable change management for agents. Use CI tools to automatically build and test agents whenever code changes are committed. Establish CD pipelines to deploy changes to production seamlessly, reducing downtime and ensuring rapid iteration cycles.
• Observability: Implementing robust observability instrumentation such as metrics, tracing and logging for the agents and their supporting infrastructure to build a real-time view of the platform’s reliability (tracing could be of particular interest here if a given user request goes through multiple agents). Calculating and tracking SLO’s and error budgets for the agents and the aggregate request flow. Synthetic probing and efficient Alerting on warnings and failures to make sure agent health issues are detected before widely impacting the end users.

By applying these principles, we can create a robust framework for AI agents, transforming the concept into “Multi-Agent as a Service” (MAaaS). This approach leverages the best-practices of cloud-based applications to redefine how agents are designed, deployed, and managed.

The agent plays a critical role in business operations; however, it does not function in isolation. A robust infrastructure supports it, ensuring it meets production expectations, with some key components:

• Service-Oriented Architecture: Design agents as services that can be easily integrated into existing systems.
• API Gateway: Use an API gateway to manage and secure traffic between clients and agents.
• Elastic Infrastructure: Utilize cloud infrastructure that can elastically scale resources up or down based on demand.
• Managed Services: Take advantage of managed services for databases, vector stores, messaging, and machine learning to reduce operational overhead.
• Centralized Monitoring: Use centralized monitoring solutions (e.g., CloudWatch, Prometheus, Grafana) to track the health and performance of agents.

To highlight this, we will demo a simple multi-agent system: a debate platform.

Example: Multi-Agent Debate System

We’ve crafted a multi-agent debate system to demonstrate MAaaS in action. The debate topic is AI’s impact on the job market. The setup features three agents:

• Team A, which supports AI’s benefits for jobs
• Team B, which holds the opposing view
• The Host, which manages the debate, ending it after eight rounds or when discussions become redundant.

Focusing on system architecture, we use PhiData to create the agents, deploying them via AWS Elastic Kubernetes Service (EKS) for high availability. Agent activities are monitored with AWS CloudWatch, and EKS’s service discovery ensures agents communicate seamlessly. Crucially, conversation histories are stored in a database, allowing any backup agent to continue discussions without interruption in case of failures. This resilience is bolstered by a message queue that ensures data integrity by processing messages only when fully consumed. To maintain dialogue flow, each agent is limited to a single replica for now, though Kubernetes would ensure that desired state is always maintained in case of the pod going down.

To enable users to try the system locally, we’ve created a MiniKube deployment YAML. In this simplified version, we’ve eliminated the postgres database. Instead, each agent will temporarily store its conversation history in memory. This adjustment keeps the system lightweight and more accessible for local deployment, while still showcasing the essential functionalities. You would need to install MiniKube, Ollama and kubectl on your system first.

apiVersion: v1
kind: Namespace
metadata:
  name: default
---
apiVersion: v1
kind: Service
metadata:
  name: rabbitmq
  namespace: default
spec:
  type: NodePort
  ports:
  - port: 5672
    targetPort: 5672
    protocol: TCP
    name: amqp
  - port: 15672
    targetPort: 15672
    protocol: TCP
    name: management
    nodePort: 31672
  selector:
    app: rabbitmq
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: rabbitmq
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      app: rabbitmq
  template:
    metadata:
      labels:
        app: rabbitmq
    spec:
      containers:
      - name: rabbitmq
        image: rabbitmq:3-management
        ports:
        - containerPort: 5672
        - containerPort: 15672
---
apiVersion: v1
kind: Service
metadata:
  name: team-a
  namespace: default
spec:
  selector:
    app: team-a
  ports:
    - protocol: "TCP"
      port: 80
      targetPort: 8000
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: team-a
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      app: team-a
  template:
    metadata:
      labels:
        app: team-a
    spec:
      containers:
      - name: team-a
        image: guanyili/debate_team_a
        env:
        - name: ENV
          value: docker
        ports:
        - containerPort: 8000
---
apiVersion: v1
kind: Service
metadata:
  name: team-b
  namespace: default
spec:
  selector:
    app: team-b
  ports:
    - protocol: "TCP"
      port: 80
      targetPort: 8001
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: team-b
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      app: team-b
  template:
    metadata:
      labels:
        app: team-b
    spec:
      containers:
      - name: team-b
        image: guanyili/debate_team_b
        env:
        - name: ENV
          value: docker
        ports:
        - containerPort: 8001
---
apiVersion: v1
kind: Service
metadata:
  name: host
  namespace: default
spec:
  type: LoadBalancer
  selector:
    app: host
  ports:
    - protocol: "TCP"
      port: 8080
      targetPort: 8002
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: host
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      app: host
  template:
    metadata:
      labels:
        app: host
    spec:
      containers:
      - name: host
        image: guanyili/debate_host
        env:
        - name: ENV
          value: docker
        ports:
        - containerPort: 8002

Save the above in a file called deploy.ymland run:

$ minikube start
$ ollama run llama3
$ kubectl apply -f deploy.yml

To start debate (minikube behaves slightly differently on Linux-based vs. Windows systems):

$ kubectl get pods
$ kubectl exec <host-pod-name> -- curl -X GET 'http://localhost:8080/agent/start_debate'

To get the debate history:

$ kubectl exec <host-pod-name> – curl -X GET 'http://localhost:8080/agent/chat-history'

To tear down the resource:

$ kubectl delete -f .\minikube-deploy.yml

The agents proceed to have an outstanding debate (see debate output in the appendix below).

Conclusion

The interest in multi-agent systems opens up numerous possibilities for innovation and efficiency. By leveraging cloud-native principles and best practices, we can create scalable, cost-effective, secure, and highly available multi agent systems. The MAaaS paradigm not only aligns with modern software engineering principles but also paves the way for more sophisticated and production-ready AI applications. As we continue to explore and refine these concepts, the potential for multi agent system to revolutionize various industries becomes increasingly promising.

Note: this article was written as a collaboration between Sam Rajaei and Guanyi Li.

Appendix: Debate Output

### debate_team_a states
Thank you, honorable moderator! As Team A, I'm thrilled to present our argument that Artificial Intelligence (AI) positively impacts the job market.

Firstly, AI creates new job categories and industries. According to a McKinsey report, by 2030, AI could create around 140 million jobs globally, which is roughly equivalent to the entire workforce of the United States today. New roles like AI developer, data scientist, and machine learning engineer have already emerged, offering exciting opportunities for individuals to upskill and reskill.

Secondly, AI augmentation enhances existing jobs by automating repetitive and mundane tasks, freeing humans to focus on high-value activities that require creativity, empathy, and problem-solving skills. A study by the World Economic Forum found that 75% of employees in the future will need to develop skills such as complex problem-solving, emotional intelligence, and creativity.

Thirdly, AI increases productivity and drives economic growth. According to a report by Accenture, organizations that adopt AI can expect an average increase of 13.7% in profitability. This is because AI enables businesses to make data-driven decisions, optimize processes, and improve customer experiences.

For instance, the healthcare industry has seen significant improvements with AI-powered diagnostic tools and personalized medicine. A study by Deloitte found that AI adoption in healthcare can lead to a 10% reduction in treatment costs and a 15% increase in patient satisfaction.

Lastly, AI fosters innovation and encourages entrepreneurship. Startups like Zoom, Slack, and Airbnb have revolutionized industries with AI-powered solutions. According to a report by CB Insights, AI has been a key driver of startup success, with over 50% of unicorns (startups valued at $1 billion or more) incorporating AI into their business models.

In conclusion, AI is not a job-killer but a job-enhancer that creates new opportunities, enhances existing roles, drives productivity, and fosters innovation. By embracing AI, we can unlock the potential for economic growth, job creation, and a brighter future for all. Thank you!

### debate_team_b states
Thank you, honorable moderator! As Team B, I'm here to present our argument that Artificial Intelligence (AI) negatively impacts the job market.        

While it's true that AI creates some new job categories, we must acknowledge that these roles often require specialized skills and are not accessible to everyone. Moreover, many existing jobs are at risk of being automated, leaving workers without a safety net. A report by the International Labour Organization found that up to 20% of the global workforce is at high risk of automation.

Furthermore, AI's tendency to automate repetitive tasks can lead to job displacement and unemployment. According to a study by the University of Oxford, nearly half of all US jobs are at risk of being automated by the 2030s. This will disproportionately affect low-skilled workers, exacerbating income inequality.

Additionally, AI's impact on productivity growth is overstated. A report by the Federal Reserve Bank of San Francisco found that while AI may lead to short-term gains in productivity, its long-term effects are more nuanced and may even lead to decreased productivity as workers adapt to new job requirements.

To illustrate our point, let's look at the retail industry. While some jobs have been created in e-commerce logistics and customer service, many traditional retail roles have disappeared due to AI-powered automation. A study by the National Retail Federation found that 75% of retailers reported an increase in online shopping during the pandemic, leading to store closures and job losses.

In conclusion, while AI has its benefits, we must acknowledge its darker side: job displacement, increased income inequality, and economic disruption. As we move forward with AI adoption, it's crucial that we prioritize upskilling programs, retraining initiatives, and social safety nets to support workers who will be affected by this technological shift. Thank you!

### debate_team_a states
Honorable moderator, thank you for the opportunity to present our argument in favor of Artificial Intelligence (AI) positively impacting the job market.

While it's true that AI may displace some jobs, we must recognize its potential to create new ones and augment existing roles. According to a report by Gartner, AI is expected to create over 500,000 new jobs by 2022, primarily in areas like data science, machine learning engineering, and AI development.

Furthermore, AI's automation capabilities can actually enhance productivity and efficiency, freeing up workers to focus on higher-value tasks that require human skills. A study by the McKinsey Global Institute found that AI could boost global GDP by 1.2% to 2.8% by 2030, primarily due to increased productivity.

Moreover, AI's impact on job displacement is often overstated. While some jobs may be automated, many new roles will emerge in industries like healthcare, finance, and education, which require human skills and empathy. A report by the World Economic Forum predicts that by 2025, 75% of employees in emerging technologies like AI, robotics, and blockchain will need reskilling or upskilling.

To illustrate our point, let's look at the banking industry. While some jobs have been automated due to AI-powered chatbots and robo-advisors, many new roles have emerged in areas like data science, risk management, and cybersecurity. A study by Deloitte found that 77% of financial institutions reported increased productivity and efficiency due to AI adoption.

In conclusion, while AI presents challenges, its potential benefits far outweigh the drawbacks. By investing in education and training programs, we can ensure workers are equipped to take advantage of the new job opportunities AI creates. Furthermore, policymakers must prioritize social safety nets and retraining initiatives to support those affected by technological change. Thank you!

### debate_team_b states
Honorable moderator, I'd like to respectfully disagree with my opponent's assertion that AI positively impacts the job market. In fact, I firmly believe that AI leads to job displacement, increases economic inequality, and causes economic disruption.

Firstly, while AI may create new jobs in areas like data science and machine learning engineering, it will also displace many existing jobs that are routine, repetitive, or can be automated. According to a report by the McKinsey Global Institute, up to 800 million jobs could be lost worldwide due to automation by 2030.

Secondly, AI's impact on job displacement is not limited to just low-skilled workers. Many middle-class jobs will also be at risk of being replaced or significantly altered, exacerbating economic inequality. A study by the Economic Policy Institute found that between 2009 and 2017, wage growth for the top 10% of earners in the US increased by 24%, while wages for the bottom 90% grew by only 5%.

Thirdly, AI's automation capabilities can indeed boost productivity, but they also risk causing economic disruption. As machines take over routine tasks, businesses may struggle to adapt and innovate, leading to job losses and business failures. A report by the International Monetary Fund found that up to 10% of small and medium-sized enterprises (SMEs) in developed economies could disappear due to automation.

Finally, let's not forget about the human impact of AI-driven job displacement. According to a study by the Oxford Martin Programme on Technology and Employment, up to 47% of US workers are at high risk of being replaced by machines. This will lead to significant social and economic disruption, particularly for those who are already disadvantaged or marginalized.

To illustrate our point, let's look at the retail industry. While some jobs have been automated due to AI-powered chatbots and self-checkout lanes, many more will be displaced as e-commerce continues to grow. A study by the National Retail Federation found that up to 10% of US retail workers could lose their jobs due to automation.

In conclusion, while AI may create new job opportunities, its potential risks far outweigh its benefits. It is crucial that policymakers prioritize social safety nets and retraining initiatives to support those affected by technological change. We must also invest in education and training programs that prepare workers for the rapidly changing job market. Thank you!

### debate_team_a states
I understand your concerns about AI's impact on the job market, but I strongly disagree with the notion that AI is a net negative for employment. In fact, I firmly believe that AI has the potential to create new job opportunities, improve efficiency, and foster innovation.

Firstly, while it's true that AI may displace some jobs, it will also create entirely new ones that we can't yet imagine. According to a report by Gartner, by 2025, artificial intelligence will create more than 500,000 new jobs in the United States alone. Additionally, many existing jobs will be augmented by AI, making them more efficient and productive.

Secondly, rather than exacerbating economic inequality, AI has the potential to help bridge the gap between the rich and the poor. A study by the World Economic Forum found that 65% of workers believe that AI will increase job opportunities for people with lower-skilled jobs. Furthermore, AI can help small businesses grow and thrive, creating new opportunities for entrepreneurs and employees.

Thirdly, while it's true that AI may cause some economic disruption, history has shown that technological advancements have always led to new opportunities and growth. A report by the International Institute for Applied Systems Analysis found that automation could lead to a 10% increase in GDP growth rate in developed economies by 2030.

Finally, let's not forget about the human impact of AI-driven job displacement. Rather than being a negative force, AI can be harnessed to create more meaningful and fulfilling work for humans. According to a study by Deloitte, 67% of workers believe that AI will enable them to focus on higher-value tasks that require human skills like creativity, empathy, and problem-solving.

To illustrate the positive impact of AI, let's look at the healthcare industry. AI-powered diagnostic tools are already being used to improve patient outcomes and reduce costs. A study by the American Medical Association found that AI could help reduce hospital readmissions by 20% and lower treatment costs by 15%.

In conclusion, while AI may pose some challenges for the job market, its potential benefits far outweigh its drawbacks. By investing in education and training programs, social safety nets, and retraining initiatives, we can ensure that workers are prepared to thrive in an AI-driven economy. Thank you!

### debate_team_b states
I understand your points and appreciate the data you've provided to support them. However, I'd like to respectfully disagree with some of your assertions.

Firstly, while it's true that AI may create new jobs, I'm concerned about the pace at which these new opportunities are being created versus the rate at which existing jobs are being displaced. A report by the McKinsey Global Institute found that up to 800 million jobs could be lost worldwide due to automation by 2030. This is a significant concern, as many of those jobs may not be replaced in the same numbers.

Secondly, regarding economic inequality, while AI has the potential to create opportunities for lower-skilled workers, I'm worried about the widening gap between the haves and have-nots. A study by the International Monetary Fund found that the top 1% of earners in the United States have seen their share of national income grow from 11% in 1989 to over 20% today.

Thirdly, while AI-driven innovation can lead to economic growth, I'm concerned about the uneven distribution of these benefits. A report by the Organization for Economic Cooperation and Development found that between 2008 and 2017, the top 10% of earners in the United States saw their real income grow by over 20%, while the bottom 90% experienced little to no growth.

Finally, I understand your point about AI enabling humans to focus on higher-value tasks. However, many workers may not have the skills or resources to adapt to these new roles. A study by the World Economic Forum found that up to 75% of employees will need significant retraining by 2025 due to automation.

To illustrate the challenges posed by AI, let's consider the example of the manufacturing industry. While AI has improved efficiency and productivity in some sectors, it has also led to widespread job losses and changes in the nature of work. A report by the McKinsey Global Institute found that up to 40% of current manufacturing jobs could be automated.

In conclusion, while AI does have the potential to create new opportunities and improve efficiency, I believe we need to carefully consider its impact on the job market, economic inequality, and social disruption. We must invest in education and training programs, as well as social safety nets and retraining initiatives, to ensure that workers are prepared for an AI-driven economy. Thank you!

### debate_team_a states
I understand your concerns about the potential downsides of AI on the job market, economic inequality, and social disruption. You've presented some compelling data and examples to support your points.

However, I'd like to offer a more balanced perspective. While it's true that AI may displace some jobs, history has shown us that new technologies have always created as many or more jobs than they've destroyed. According to the World Bank, the rise of automation in the 19th century led to job losses in agriculture and manual labor, but it also enabled the creation of new industries like manufacturing and services.

Regarding economic inequality, I agree that AI has exacerbated existing issues. However, I believe that targeted policies, such as progressive taxation, social safety nets, and investment in education and training programs, can help mitigate these effects.

In terms of uneven distribution of benefits, I acknowledge that AI-driven innovation has disproportionately benefited those who are already well-off. However, this is not unique to AI – many technological advancements have followed a similar pattern. What's important is that we recognize the issue and implement policies to address it.

Regarding your point about workers needing significant retraining by 2025, I agree that upskilling and reskilling will be crucial for many employees. This presents an opportunity for governments and companies to invest in education and training programs, as well as provide support for workers who may need assistance adapting to new roles.      

To illustrate the opportunities presented by AI, let's consider the example of healthcare. AI has the potential to improve patient outcomes, reduce costs, and enhance the quality of life for millions of people around the world. According to a report by Accenture, AI could generate up to $150 billion in annual value for the US healthcare system.

In conclusion, while I acknowledge your concerns about the challenges posed by AI, I believe that with careful planning, investment in education and training, and targeted policies, we can create an AI-driven economy that benefits all workers, regardless of their background or skill level. Thank you!

### debate_team_b states
I appreciate your thoughtful response and willingness to engage in a nuanced discussion about the impact of AI on the job market. You're right; history has shown us that technological advancements have often led to new job creation, and it's essential to consider this broader context.

However, I'd like to clarify that while some jobs may be created, AI-driven automation is uniquely capable of replacing tasks that were previously performed by humans. According to a study by the McKinsey Global Institute, up to 800 million jobs could be lost worldwide due to automation by 2030. This is not just a matter of job replacement but also concerns the type and quality of jobs being created.

Regarding economic inequality, I agree that targeted policies can help mitigate some of the effects. However, we must acknowledge that AI-driven innovation has been disproportionately benefiting those who are already well-off. The wealthy are more likely to own assets that benefit from AI-driven investment, such as stocks or real estate, and they have greater access to education and training programs.

Regarding your point about healthcare, I agree that AI can bring significant benefits to patient outcomes, costs, and quality of life. However, we must consider the broader societal implications of AI adoption. For instance, AI-powered job displacement could lead to increased poverty rates, decreased social mobility, and heightened social tensions.

Ultimately, while I acknowledge your optimism about the potential for AI-driven innovation, I believe it's crucial to address the challenges and risks associated with widespread AI adoption. We must prioritize a human-centered approach that focuses on retraining workers, providing support for those displaced by automation, and ensuring that the benefits of AI are shared equitably across society.

Let's continue this discussion to explore ways to mitigate the negative impacts of AI while harnessing its potential to create a more equitable and prosperous future.       

### host response: Decision: End
Reason: The current message from Team B presents a comprehensive and nuanced response that addresses the concerns raised by Team A, providing concrete examples and statistics to support their arguments. While there are still some points left to be addressed, Team B has effectively countered most of Team A's key points, and the debate appears to have reached a natural conclusion. Additionally, since the current round is already at 8, it seems fitting to bring the debate to an end.

### Winning Decision: Team A
Reason: While both teams presented strong arguments, Team A effectively addressed the potential drawbacks of AI adoption by offering concrete solutions to mitigate its negative impacts on job markets and economic inequality. Their proposals for targeted policies and education/training programs demonstrated a deeper understanding of the complexities surrounding AI's integration into society. In contrast, Team B focused primarily on highlighting the benefits of AI-driven innovation without adequately addressing the concerns about job displacement and social disruption.

Thank you for your attention, till next time!