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.
As enterprises put more AI applications and agents into production, managing the associated costs is becoming an increasingly important operational challenge. AI spending is distributed across multiple providers, models, applications, and teams, making it difficult to understand where costs…
Today ServiceNow launched the ServiceNow Control Tower Alliance , and Kong is proud to be a founding member to help enterprises put AI to work safely, at scale. It's a milestone for Kong, and we think it's an important moment for the industry. Here's what the alliance is, why it matters, and why we…
New AI cost management capabilities help organizations price, attribute, plan, control, and optimize AI spend across models, applications, teams, and agents. AI is getting easier to build with. Paying for it is getting harder to manage. As organizations move from AI experiments to production…
In April 2026, Uber burned through its entire year’s AI budget by the middle of the month, driven largely by usage of Anthropic's Claude Code and Cursor. Nothing was broken. No one misused the tool. Engineers were doing exactly what every team's been told to do: adopt AI, use it well, use it…
You've mapped the architecture. Your agents retrieve context from external tools, coordinate with other agents, and propagate mutations through your systems. The model logic is solid. An agent gateway is the control layer for traffic between agents, models, tools, APIs, and data sources. It applies…
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:…