Kong was born to connect. The world is shifting from connecting cloud services with apps to connecting LLMs through agents. API calls and tokens are moving in tandem; a new unit of intelligence is forming. As AI traffic explodes into hypervolumes, speed is all that matters. The same principles of…
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Gartner published a report last week (1) 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…
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The APIs that have been powering websites and apps created a massive market, but there are only up to 8 billion humans consuming them behind pixels. As LLMs are taking over the world — in the form of productized agents first — there will be 100X more machines than humans. The internet built for…
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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…
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AI agents move fast. Securing them shouldn't slow you down. Kong and Noma are partnering to change that. Noma is the newest member of Kong's Technology Partner Program . This program delivers high-quality, reliable integrations providing real business value for customers. Together, Kong and Noma…
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APIs are the connective tissue of modern software and as the number and complexity of APIs grow, managing them efficiently becomes critical. That is where API gateways come in. An API gateway is the infrastructure layer that sits in front of your APIs to manage traffic, enforce security policies,…
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AI agents are spreading across organizations rapidly. Each agent needs secure access to different Model Context Protocol (MCP) servers. Authentication becomes complex. Scaling creates bottlenecks. The dreaded "too many endpoints" problem emerges. You face a classic AI infrastructure headache. The…
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Legacy API management platforms were built for a different era. They weren't designed for microservices, multi-cloud deployments, or AI workloads. They're expensive, rigid, and hold engineering teams back. The problem is that migration has always felt hard. APIs are load-bearing infrastructure.…
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Every few months, a frontier AI lab makes a move that says the quiet part out loud: agents are only as useful as the systems they can reach. The latest example is Anthropic's acquisition of Stainless, the company behind the tooling that turns API specs into SDKs and MCP servers. Anthropic's own…
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This is part of a three-part series. For the full story, see Building the Agentic Commit Log and The Case for Kafka as the Agent's Memory Layer . There's a reason we don't find our keys by scanning every room like a security camera. We replay the tape. We remember the groceries, the front…
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This is the second post in a series. For the first part, see Why We Need to Stop Prompt Hacking . Generative AI systems do not fail because models are weak. They fail because architectures are incomplete. Once organizations accept that prompts cannot guarantee reliability, a new challenge emerges:…
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Generative AI has completely changed the landscape of enterprise automation, knowledge work and operational efficiency. In 2026, the question is no longer whether these models can perform complex tasks, but whether they can do so reliably enough for mission-critical systems. Despite the…
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As API ecosystems grow, developers and platform teams often work in separate environments. Platform teams manage APIs, gateways, and governance centrally, while developers recreate those configurations locally for testing and debugging. Over time, this can lead to configuration drift, inconsistent…
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