The rise of Big Data I want to take you back to 2012 . In those early years, our focus was big data, and everyone else was looking at the cloud. We were pioneering, but in a world that was still beginning to understand the value of data.
Executive Summary At the AI Engineer Code Summit in New York City, Anthropic shared key insights into the Claude Agents SDK that reshape how effective AI agents are built in practice. By exposing the same agent harness that powers Claude Code , the SDK highlights a shift away from prompt-centric approaches toward more structured, reliable agent architectures. These learnings reflect a growing challenge many teams are encountering in practice: increasing model capability and code generation speed without losing control, auditability, or reliability. This post distills the core technical takeaways and explains why the infrastructure around the model—the agent harness—is just as critical as the model itself. The full workshop recording from the summit is available on YouTube . In this blog post, we dive into our main learnings.
Executive Summary Choosing an agentic AI framework is an architectural decision, not a tooling choice. While many frameworks offer similar building blocks, they differ significantly in abstraction level, ecosystem coupling, observability, governance, and production readiness.
Executive Summary Agentic workflows are AI-driven systems designed to reason, plan, and act autonomously across complex tasks and environments. Unlike prompt-based interactions or deterministic automation, they coordinate multiple AI agents, tools, and feedback loops to achieve high-level goals under uncertainty. This article introduces the core components of an agentic workflow, explains how autonomous AI agents operate within them, and clarifies when agentic approaches are appropriate for production AI systems.
Executive Summary The field of Artificial Intelligence is in a constant state of evolution. For years, the focus was on building a single, powerful model capable of tackling any task thrown its way. But as the complexity of our problems grows, it's becoming clear that a lone genius, no matter how brilliant, can be outmatched by a well-coordinated team of specialists. This is the paradigm shift that brings us to multi-agent AI systems. A Multi-Agent System (MAS) is a team of specialized AI agents that collaborate to tackle complex problems. Use a multi-agent system when your challenge involves multiple tasks, dynamic environments, or specialized expertise that require agent coordination and agent communication. For simpler or well-defined workflows, a single intelligent agent or structured agentic workflow is often a better, faster, and more cost-efficient choice. The golden rule is to use the simplest setup that effectively solves your problem.
Executive Summary The article explains how Artificial Intelligence (AI) is transforming procurement from a tactical, manual function into a strategic driver of value, innovation, and resilience. By leveraging technologies such as machine learning, natural language processing, and Generative AI, organizations can automate and optimize every stage of the procurement lifecycle—from intelligent sourcing and supplier risk management to contract analysis and the procure-to-pay (P2P) process. The rise of integrated “Agentic AI” systems enables end-to-end workflow automation, predictive risk detection, and data-driven decision-making, while maintaining a human-in-the-loop approach to ensure strategic oversight. Ultimately, AI empowers procurement teams to reduce costs, improve efficiency, and proactively manage risks, positioning the function as a key enabler of organizational competitiveness and agility.
Executive Summary ML6 went to the AI Engineer summit in Paris. We are confident that agents will remain a vital part of the industry, and MCP will be at the forefront of this trend. While MCP adoption is skyrocketing, its potential remains heavily underutilized. And while many agent projects are still explorative, those that find a suitable use case can radically transform their processes through iterative engineering.
Things are moving fast in LLM and generative AI space. A lot of things moved forward with the speed of light. Is this a temporary momentum we experience or are we at day 1 of an ever expanding universe of possibilities?
Transform Enterprise Operations with Google’s Latest AI Stack: Smarter Agents, Multimodal Interfaces, Real Business Impact The annual Google Cloud Next event took place in Las Vegas in April, where Google presented its latest innovations for Google Cloud Platform (GCP).
What is Gemini, Google’s newest AI model? Yesterday, Google launched the first version of their new family of models: Gemini. The launch immediately sparked discussions - is Google, with these new models, opening up the competition field with the OpenAI models? In this blog, we’ll share our view on the Gemini launch.