Bridge AI innovation with enterprise scale and security. Explore strategies, architectures, and success stories that show how organizations are deploying AI responsibly and effectively across complex, API-driven environments.
Most teams building multi-agent systems hit the same wall at roughly the same point. The prototype works. Agents chain together, tasks complete, the demo impresses the room. Then someone asks: "What happens when this runs a thousand times a day? What happens when an agent calls an external API…
You've probably seen the demos. An AI model kicks off a task, hands pieces of it to other AI models, and somehow the whole thing gets done. Emails drafted, code reviewed, reports summarized — all without a human in the loop. While a single agent doing one thing is impressive, the true paradigm…
A decade ago, cloud and API sprawl got ahead of governance, and enterprises spent years trying to account for costs they'd never tracked. Today, we're seeing the same pattern around AI, with hundreds of customers proxying traffic via Kong AI Gateway, which includes LLM, MCP, and agent connectivity.…
Production AI no longer generates one kind of traffic. It generates four patterns, and most teams govern only one. AI traffic management starts with a single decision: proxy AI traffic through one control point instead of letting it flow straight from application code to model providers. Skip that…
We're thrilled to announce a strategic technology partnership between Kong and ModelOp . As enterprises rapidly transition into the agentic era, they face a critical challenge: how to deploy AI fast enough to stay competitive without taking on unacceptable regulatory or security risks. Together,…
Shadow AI detection is the practice of finding and governing unsanctioned AI tools, models, and API integrations that employees deploy without security approval. It has become urgent because these tools route live enterprise data to external models in real time, and traditional security stacks…
Enterprise AI adoption is accelerating. In PwC's April 2025 survey of 308 US business executives, 88% said they plan to increase AI-related budgets in the next 12 months [1]. But scaling AI from pilot to production exposes a structural problem most teams discover too late: direct LLM API…
LLM provider switching went from a theoretical concern to an operational emergency in June 2026, when Anthropic disabled Claude Fable 5 and Mythos 5 following a US government directive [3][6]. The shutdown was swift, with access suspended just days after the models launched. Enterprises that had…
Vendor lock-in is one of the most underestimated risks in enterprise technology. It starts with convenience and ends with dependency. And as organizations accelerate AI adoption, the problem is getting worse. What used to be a cloud infrastructure concern now extends to LLM providers, agentic…
AI adoption is accelerating. Governance is not keeping pace. According to Deloitte research, 74% of organizations plan to adopt agentic AI within two years, yet only 21% have a mature AI data governance model in place [1]. The gap between ambition and readiness is showing up in production metrics:…
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:…
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…
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…
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.…
AI agents are only as capable as the tools they can reach. When an agent needs to query a database, file a support ticket, or pull data from a CRM, it has to find the right tool, authenticate, and invoke it — all at runtime. The Model Context Protocol (MCP) standardizes how agents communicate…
An enterprise AI gateway should provide a centralized point of policy enforcement for routing, governing, securing, and observing artificial intelligence traffic at scale. LiteLLM is one of many AI gateways that can cover the foundational AI connectivity needs teams often start with. For…
