Anant Bhardwaj, CEO of Instabase, presents a pragmatic guide for building enterprise AI. He argues that AI agents are best used during the 'design-time' to create predictable workflows, rather than for autonomous 'runtime' operations. Bhardwaj also debunks the hype around RAG, highlighting its dependency on data quality, and explains why trust in AI systems stems from predictability, not just accuracy.
Built-in tools like web search, file search, and code interpreter allow developers to extend model capabilities out-of-the-box. This summary covers the concepts, compares them to function calling, and details a demo of building a data exploration dashboard using multiple tools in concert.
A deep dive into seven essential AI concepts shaping the future of intelligent systems, including Agentic AI, RAG, Mixture of Experts (MoE), and the theoretical frontier of Artificial Superintelligence (ASI).
Jeff Huber of Chroma argues that building reliable AI systems hinges on 'Context Engineering'—the deliberate curation of information within the context window. He challenges the efficacy of long-context models, presenting a 'Gather and Glean' framework to maximize recall and precision, and discusses specific challenges and techniques for AI agents, such as intelligent compaction.
A product manager from Cisco's incubation group, Outshift, details a solution that uses a multi-agent AI system combined with a dynamic network knowledge graph to solve critical issues in IT change management. The system integrates with ITSM tools like ServiceNow to automate impact assessment, test plan generation, and pre-production validation in a "digital twin" environment, significantly reducing production failures.
A deep dive into building expert AI systems using a Wisdom-Driven Knowledge Graph. This approach enhances Knowledge-Augmented Generation (KAG) to surpass traditional Retrieval-Augmented Generation (RAG) by enabling systems to understand, reason, and provide expert-level quantitative analysis and advice.