In this episode of the Colaberry AI Podcast, we delve into LinkedIn's journey in advancing their generative AI capabilities, with a focus on the evolution from foundational GenAI applications to the development and scaling of AI agents.
The text outlines the technical architecture supporting these AI agents, emphasizing the importance of reusing existing infrastructure, designing for human-in-the-loop control, and leveraging observability for continuous improvement. We'll explore the emergent design patterns in the AI agent space, such as intent alignment and the role of data, while also highlighting the critical security and privacy considerations in their implementation.
Moreover, we'll discuss LinkedIn's adoption of open protocols to foster interoperability and prevent fragmentation within the growing agent ecosystem. This approach aims to facilitate collaboration and drive the broader advancement of generative AI technologies.
Join us as we unpack the technical insights and strategic decisions that have shaped LinkedIn's GenAI agent tech stack evolution, and the implications for the future of human-AI collaboration.
🎯 Key Takeaways:
🤖 GenAI Agent Development: LinkedIn's journey from foundational GenAI apps to scalable AI agents
🏗️ Technical Architecture: Reusing infrastructure, human-in-the-loop design, and observability-driven improvement
🤝 Emergent Design Patterns: Intent alignment, data role, and security/privacy considerations
🌐 Open Protocols: Fostering interoperability and preventing fragmentation in the agent ecosystem
📈 Implications for the Future: Insights into the evolution of human-AI collaboration
🧾 Ref: The LinkedIn Generative AI Application Tech Stack: Extending to Build AI Agents
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