AI Readiness & API Transformation - Every website must be "Agent-ready" in the coming era [1] - APIs need to be transformed into reliable, AI-ready tools [2] - Postman's 90-day AI readiness playbook details how to turn APIs into reliable, AI-ready tools [2] Key Components for AI-Ready APIs - Predictable structures are essential for AI agents [3] - Machine-readable metadata is crucial for AI understanding [3] - Standardized behavior is necessary for seamless AI interaction [3] Postman Playbook Highlights - Automatic documentation can be achieved by standardizing API format, Postman's Spec Hub automatically generates and validates API docs for both humans and AI agents without any manual work [2] - Validated specs can be turned into hosted, function-style endpoints, letting AI agents invoke APIs like native commands [3] Impact of AI Agents - Agents will make purchases, not humans [3] - Agents will find the best options, not humans [3] - Agents will fill out job applications, not humans [3]

AI agents promise breakthroughs but often deliver operational chaos. Building reliable, deployable systems with unpredictable LLMs feels like wrestling fog – testing outputs alone is insufficient when the underlying workflow is opaque and flaky. How do we move beyond fragile prototypes? This talk, from the creator of Docker, argues the solution lies outside the model: engineering reproducible execution workflows built on rigorous architectural discipline. Learn how containerization, applied not just to depl ...

Group 1 - Digital Asset Technologies Inc. has appointed Marcus Ingram as the new CEO and Director, who will also continue as CEO of its portfolio company LiquidLink [1][2] - The appointment follows the resignation of Young Bann, who will remain as an Advisor to ensure a smooth transition [2] - Ingram aims to leverage blockchain technology to enhance payment systems and explore opportunities in Web3, DeFi, and decentralized infrastructure [3][4][5] Group 2 - Ingram believes LiquidLink has the potential to become a unicorn in a significant market and is committed to strategic investments in emerging digital economies [6] - Digital Asset Technologies focuses on equity investments in companies developing technology, particularly in blockchain and real-world asset tokenization [7] - LiquidLink is dedicated to building secure infrastructure for the tokenized economy, with its flagship product Xrpfy supporting multiple blockchains [8]

AI Agent Evaluation & Observability - The industry emphasizes the necessity of observability in AI development, particularly for evaluation-driven development [1] - AI trustworthiness is a significant concern, highlighting the need for robust evaluation methods [1] - Detecting problems in AI is challenging due to its non-deterministic nature, making traditional unit testing difficult [1] AI-Driven Evaluation - The industry suggests using AI to evaluate AI, leveraging its ability to understand and identify issues in AI systems [1] - LLMs can be used to score the performance of other LLMs, with the recommendation to use a better (potentially more expensive or custom-trained) LLM for evaluation than the one used in the primary application [2] - Galileo offers a custom-trained small language model (SLM) designed for effective AI evaluations [2] Implementation & Metrics - Evaluations should be integrated from the beginning of the AI application development process, including prompt engineering and model selection [2] - Granularity in evaluation is crucial, requiring analysis at each step of the AI workflow to identify failure points [2] - Key metrics for evaluation include action completion (did it complete the task) and action advancement (did it move towards the goal) [2] Continuous Improvement & Human Feedback - AI can provide insights and suggestions for improving AI agent performance based on evaluation data [3] - Human feedback is essential to validate and refine AI-generated metrics, ensuring accuracy and continuous learning [4] - Real-time prevention and alerting are necessary to address rogue AI agents and prevent issues in production [8]

AI Agent Evaluation - Azure AI Evaluation SDK is designed to rigorously assess agentic applications, focusing on capabilities, contextual understanding, and accuracy [1] - The SDK enables the creation of evaluations using structured test plans, scenarios, and advanced analytics to identify strengths and weaknesses of AI agents [1] - Companies are leveraging the SDK to enhance agent trustworthiness, reliability, and performance in conversational agents, data-driven decision-makers, and autonomous workflow orchestrators [1] Microsoft's AI Initiatives - Microsoft is promoting AI in startups and facilitating the transition of research and startup products to the market [1] - Cedric Vidal, Principal AI Advocate at Microsoft, specializes in Generative AI and the startup and research ecosystems [1] Industry Expertise - Cedric Vidal has experience as an Engineering Manager in the AI data labeling space for the self-driving industry and as CTO of a Fintech AI SAAS startup [1] - He also has 10 years of experience as a software engineering services consultant for major Fintech enterprises [1]

AI Agents and Memory - The presentation focuses on the importance of memory in AI agents, emphasizing that memory is crucial for making agents reflective, interactive, proactive, reactive, and autonomous [6] - The discussion highlights different forms of memory, including short-term, long-term, conversational entity memory, knowledge data store, cache, and working memory [8] - The industry is moving towards AI agents and agentic systems, with a focus on building believable, capable, and reliable agents [1, 21] MongoDB's Role in AI Memory - MongoDB is positioned as a memory provider for agentic systems, offering features needed to turn data into memory and enhance agent capabilities [20, 21, 31] - MongoDB's flexible document data model and retrieval capabilities (graph, vector, text, geospatial query) are highlighted as key advantages for AI memory management [25] - MongoDB acquired Voyage AI to improve AI systems by reducing hallucination through better embedding models and re-rankers [32, 33] - Voyage AI's embedding models and re-rankers will be integrated into MongoDB Atlas to simplify data chunking and retrieval strategies [34] Memory Management and Implementation - Memory management involves generation, storage, retrieval, integration, updating, and forgetting mechanisms [16, 17] - Retrieval Augmented Generation (RAG) is discussed, with MongoDB providing retrieval mechanisms beyond just vector search [18] - The presentation introduces "Memoriz," an open-source library with design patterns for various memory types in AI agents [21, 22, 30] - Different memory types are explored, including persona memory, toolbox memory, conversation memory, workflow memory, episodic memory, long-term memory, and entity memory [23, 25, 26, 27, 29, 30]

MCP and AI Agent Development - MCP is presented as a way of interfacing between AI and external resources, enabling functionalities like database access and complex computations [3] - The industry is currently focused on building internal demos and connecting them to APIs, but needs to move towards robust authentication and authorization [9][10] - The industry needs to adapt existing tooling for MCP due to its dynamic client registration, which can flood developer dashboards [12] Enterprise Readiness and Security - Scaling MCP servers requires addressing free credit abuse, bot blocking, and robust access controls [12] - Selling MCP solutions to enterprises necessitates SSO, lifecycle management, provisioning, fine-grained access controls, audit logs, and data loss prevention [12] - Regulations like GDPR impose specific logging requirements for AI workloads, which are not widely supported [12] Challenges and Future Development - Passing scope and access control between different AI workloads remains a significant challenge [13] - The MCP spec is actively developing, with features like elicitation (AI asking humans for input) still unstable [13] - Cloud vendors are solving cloud hosting, but authorization and access control are the hardest parts of enterprise deployment [13]

long sleeves and the collars. Stay stiff all day. Get 20% off using code TV at collars and co.com. >> Tech stocks are still ripping higher today. Nvidia, Microsoft, Broadcom.These are all fresh record highs. But Deirdre Bosa has a new deep dive on an overlooked problem with the latest wave of AI models, which has powered much of the recent rally. Deirdre what can you tell us.So Kelly the next frontier in AI. It's not just chatting or summarizing, it's reasoning. And that means thinking through problems, mak ...

AI Engineering Roadmap - The roadmap emphasizes the progression towards Large Language Models (LLMs), Natural Language Processing (NLP), and AI agents [2] - The roadmap suggests exploring Computer Vision (CV) and Reinforcement Learning (RL) as equally valuable paths for AI engineers [2] Resources for AI Development - The roadmap provides links to resources for Machine Learning (ML) and AI beginners [3] - The roadmap includes resources for hands-on experience with LLMs and advanced Retrieval Augmented Generation (RAG) techniques [3] - The roadmap offers resources for building AI agents, from beginner level to production-ready [3] - The roadmap links to a hub for AI engineering resources [3]

Acquisition Announcement - Data cybersecurity company Rubrik announced its intent to acquire Predibase, a startup focused on training and fine-tuning open source AI models [1][2] - The deal's financial terms were not disclosed, but reports suggest it falls between $100 million and $500 million [1] Company Background - Predibase was founded in 2021 and has raised over $28 million in venture capital from notable investors such as Felicis, Greylock, and Sancus Ventures [2] - Rubrik, founded in 2014, has raised more than $1.6 billion in venture capital and went public in April 2024 [6] Strategic Implications - The integration of Predibase is expected to enhance Rubrik users' ability to build AI agents using platforms like Amazon Bedrock, Azure OpenAI, and Google Agentspace [2] - Bipul Sinha, CEO of Rubrik, emphasized that combining Predibase's capabilities with Rubrik's secure data platforms can transform AI applications by addressing performance and cost issues [3] Industry Trends - Rubrik's acquisition is part of a broader trend where companies are acquiring firms to strengthen their technology stack for AI agent development [3] - Other recent acquisitions in the industry include Salesforce acquiring Informatica for $8 billion and Snowflake acquiring Crunchy Data [4]