Systems | Development | Analytics | API | Testing

Gartner Just Described the Platform Enterprises Need to Compete in the Context Economy, Kong Already Built It

A Response to Gartner’s Latest Research Last week, Gartner published a report titled MCP Servers Will Fuel the Next AI Revenue Surge — Context as a Service (1) that should be required reading for every enterprise technology leader. Then, Kong CEO Augusto Marietti (Aghi for short) wrote out his thoughts on the subject and why context is king. I’d like to continue that conversation.

In the Context Economy, Context is King

Gartner published a report last week that I think marks a genuine inflection point for how enterprise technology leaders should think about AI strategy. The headline finding: we have crossed a threshold where competitive advantage in the AI era is no longer about access to data — it's about the semantic intelligence wrapped around it. Gartner calls this the "context economy," and they believe it will reshape how software is built, sold, and monetized over the next several years. I agree.

Designing Unified APIs for Customer UIs & Internal Tools with Clean Permissions | DreamFactory

A unified API serves both external users and internal operators from one contract while enforcing different capabilities and data scopes. It centralizes authentication, authorization, validation, and auditing so every consumer follows the same rules. DreamFactory defines this as one surface with segmented access aligned to jobs-to-be-done. The goal is consistent behavior across channels, fewer duplicated services, and easier change management.

Maintaining compliance when adopting AI in regulated industries

Key Takeaway: Organizations in regulated industries can adopt AI without compromising compliance. Automated testing enables continuous validation of AI-enabled systems while maintaining the predictability, documentation, and audit-readiness that regulators require. In compliance-first industries, such as banking, healthcare, or telecommunications, AI adoption is rarely a simple technology decision. You are often caught between two competing pressures.

Maintaining the Vibes: How to Turn AI Coding into Enterprise Value

We are living through a renaissance in software development. In February 2025, computer scientist Andrej Karpathy coined the term "vibe coding" to describe a new state of human-computer interaction. In this model, developers stop acting like bricklayers—manually laying every line of syntax—and start acting like architects. They design the outcome with natural language, and AI handles the construction, translating their vision into working software.

Unified Document Processing: Why Standalone IDP Can't Compete with End-to-End Document Automation

Intelligent document processing (IDP) promised a paperless future for businesses and organizations. But despite significant investment, a critical gap often persists between the technological capability to extract data and the organizational ability to actually drive meaningful business outcomes. 78% of enterprises are now operational with some form of AI-powered document processing, yet 52% of staff time remains consumed by manual document tasks.1 This paradox reveals the new IDP market reality.

How AI Augments Human Creativity at Scale: The WPP Blueprint

Learn how AI agents are reshaping enterprise decision-making, AI governance, and brand creativity. Daniel Hulme, Chief AI Officer at WPP & CEO of Satalia/Conscium, explains how AI agents, decision intelligence, and his concept of “brand brains” (AI systems designed to create brand-specific, production-grade content) are changing how organizations operate. He shares why companies don’t have data problems but decision-making problems, and how AI can augment human creativity at scale.

AI Agents & Enterprise AI Governance: The WPP Blueprint for Brand Brains | The Data Chief

AI agents are transforming enterprise AI, governance frameworks, and business decision-making. In this episode, we explore agentic AI systems, decision intelligence, and brand brains — AI systems designed to produce brand-specific, production-grade content that differentiates businesses. Join @wpp's Daniel Hulme & podcast host Cindi Howson for this insightful discussion. If you're a Chief Data Officer, Chief AI Officer, or enterprise leader, this conversation explains how to deploy AI agents safely, govern them effectively, and automate complex decisions while augmenting human creativity.

Enterprise AI Infrastructure Security Series - 1) Intro

Welcome to Part One in this series covering AI Enterprise Security with ClearML. How do you secure an AI platform, ensure compliance, and still give your teams the access they need to move fast? ClearML builds security, compliance, and cost control into every layer of the platform — the guardrails are invisible to your AI/ML teams, but not absent. In this video, I introduce the six layers of the ClearML Enterprise security stack: Identity & Access, Configuration Governance, Automation Security, Compute & Data Access Governance, Model Serving, and Audit & Compliance.

Models to Meaning: AI Value in Production w/ Open Source - MLOps Live #42 w/ QuantumBlack

In this session of MLOps Live, Joseph Perkins, Product Manager at Vizro by QuantumBlack, and Gilad Shaham, Director of Product Management, Iguazio (A McKinsey Company) discuss how modern AI teams are moving beyond heavy engineering to deliver production-ready, business-visible AI systems using open-source frameworks like MLRun and Vizro. In this session, you’ll learn how: The session includes a live demo of a gen AI application, showing how MLRun and Vizro work together to deliver both operational control and business visibility in production.