Agents to hack team work // you may work with agents so soon
“If you have a screwdriver in your hand, all problems look like screws.” — don’t overuse AI.
Researchers have developed an AI assistant designed to enhance teamwork by aligning the beliefs and actions of human and AI agents working together on tasks.
This AI “team coordinator” intervenes when necessary to ensure that all team members are on the same page, improving efficiency in complex scenarios like search-and-rescue missions, surgeries, and even video games. The system models how team members understand each other’s roles and actions, using probabilistic reasoning to predict and align their plans. This innovation could significantly reduce confusion and errors, especially in high-stakes environments.
// sounds like dystopian scenario
The article emphasizes that while AI can improve specific tasks, its broader impact will transform the entire business landscape, affecting all levels of the workforce. To prepare, companies need to understand upcoming AI trends and challenges. Key trends include the integration of AI in digital resource management, enhancing human capabilities through a “Person Plus AI” strategy, and using AI to personalize experiences and automate tasks. Companies should focus on data quality, cybersecurity, and leveraging AI for both internal growth and revenue generation. Additionally, businesses must be cautious not to overuse AI, ensuring its application remains seamless and effective:
1. Digital Resource Areas: Companies will create digital resource areas to manage AI and other digital tools as essential parts of the workforce, changing recruitment and retention strategies.
2. ‘Person Plus AI’ Strategy: AI should enhance human abilities, not replace them. This strategy amplifies human potential, leading to sustainable business growth.
3. Purpose-Focused AI Model Matching: Companies should match AI models to specific tasks, focusing on purpose, cost, and effectiveness for true ROI.
4. Integration of MLOps and LLMOps: Integrating machine learning and large language model operations into platform engineering will streamline AI development and improve software quality.
5. Embedded AI-Powered Cybersecurity: AI embedded in hardware will enable proactive cybersecurity, reducing the burden on IT teams by detecting threats autonomously.
6. Automation of Repetitive Work: Automating repetitive tasks will reshape teams and shift focus towards applying information, requiring robust data management strategies.
7. Hyper-Personalized Engagement: AI will enable hyper-personalization in customer and employee interactions, but companies must address data governance and privacy.
8. Increased Use of ML for Decision-Making: Advanced machine learning will empower faster, more confident business decisions by providing reliable insights.
9. Focus on Accessibility: AI must comply with accessibility laws, enhancing customer engagement and employment opportunities for people with disabilities.
10. ‘User-Built’ Experiences: AI will allow users to customize their own experiences, tailoring actions to meet their specific needs.
11. Specialized AI Agents: AI agents will automate more tasks, especially in reading and writing, increasing organizational output and efficiency.
12. Product-Oriented AI Development: Companies should focus on developing AI applications with a product-oriented mindset, prioritizing real-world usability and continuous improvement.
13. AI for Revenue Generation: CIOs should lead AI initiatives that focus on revenue generation, creating leadership opportunities and driving growth.
14. Focus on Data Quality: High-quality data is crucial for AI success, so businesses should invest in data management and governance.
15. AI for Frontline Workers: AI will significantly boost productivity in sectors like manufacturing by providing real-time guidance and improving efficiency.
16. Agentic Architecture: The rise of open-source AI models will make it easier and more cost-effective for organizations to integrate AI into their operations.
17. Cognitive-Capture AI: AI will help preserve and transmit specialized knowledge within companies, particularly in the face of high turnover and an aging workforce.
18. AI Investments for Growth: Companies should balance AI investments to drive both internal efficiency and external market growth, turning AI-driven improvements into services.
19. Increase in Cybercrime: As AI becomes more widespread, it will also make cybercrime easier, necessitating stronger cybersecurity measures.
20. Avoiding Overuse of AI: Companies should use AI strategically, avoiding over-reliance to ensure that its application remains effective and seamless.
// too much hype around, AI is a tool not a magic
Kubiya, a leader in Conversational AI for DevOps, has secured an additional $6 million in funding, bringing its total seed round to $12 million. This new funding supports the launch of their AI Teammates platform, which allows developers to delegate complex tasks to digital colleagues via natural, human-like conversations. The platform is already being used by companies like A+E Networks and Morse to streamline operations and improve efficiency. The funding round was led by Heavybit, with Hyperwise VC participating, and included tech luminaries like HashiCorp co-founder Armon Dadgar and Slack co-founder Cal Henderson. Kubiya’s AI Teammates offer a scalable, adaptable automation solution that reduces the time and effort needed for tasks traditionally requiring extensive coding and planning.
Here is a summary of the key points from the transcript:
- Harrison Chase is the co-founder and CEO of Anthropic’s Langchain, a framework that abstracts and simplifies working with large language models (LLMs) and building agentic systems.
- Langchain has seen rapid growth, with over 15 million monthly downloads, 100,000 apps powered by it, and 75,000 GitHub stars. This success is attributed to Langchain being one of the first middleware frameworks to help developers work with the emerging capabilities of LLMs.
- Harrison discusses the evolution of “agentic” systems that use LLMs to perform tasks in a more autonomous way, highlighting the potential but also the current limitations and challenges. Key challenges include communication/prompting issues, latency, and cost.
- Langsmith, another Anthropic product, provides observability and testing/evaluation capabilities to help developers build more reliable and production-ready agentic applications on top of LLMs.
- Harrison sees great potential in fusing retrieval-augmented generation (RAG) techniques with few-shot learning/prompting to enable more personalized and adaptive LLM-powered systems. This is an area Anthropic is actively researching and developing tooling for.
- Overall, the discussion covers the current state of LLM-powered agentic systems, the role of frameworks like Langchain and Langsmith, and future directions around multimodal capabilities, personalization, and moving from prototypes to production-ready deployments.
Epic Systems, a major healthcare software company, is developing over 100 new AI features to enhance its offerings for doctors and patients. These features were highlighted at Epic’s annual Users Group Meeting, where CEO Judy Faulkner and other executives, dressed in costumes fitting the event’s “storytime” theme, presented their plans.
The new AI tools aim to simplify tasks for doctors, such as revising communications into plain language, automating prescription orders, and streamlining insurance processes. Epic also demonstrated an AI agent that can assess a patient’s recovery via the MyChart app, although this technology is still in its early stages. Additionally, Epic is exploring direct claims submissions through its software, potentially transforming the insurance claims process.
While these innovations show promise, it’s uncertain how widely they will be adopted or when they will become available.
