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*Kong makes every MCP client and server work with Enterprise-Managed Authorization, whether they speak the protocol or not.* The MCP demo impressed the room. Then someone asked how 5,000 employees would connect to 40 MCP servers, and the answer was: one OAuth consent screen at a time. Per user. Per server. No central policy, no unified audit trail, and nothing stopping a personal account from getting wired into a work tool.

AI Transport v0.4.0 includes changes to optionally support database hydration. Some applications may wish to store AI conversation history in an external store, such as a database. AI Transport's support for database hydration allows applications to reconcile that stored history with the live activity in the AI session. When using database hydration, your application persists messages for completed runs to the database.

Static analysis has always excelled at finding defects, vulnerabilities, and compliance violations. Before AI-assisted code remediation, however, developers still had to research the root cause, design a fix, and manually verify that the correction satisfies the relevant requirements. The new, built-in AI-assisted code remediation feature speeds up this process.

TL;DR AI is no longer the future. It is the present. Global enterprise AI spending will roughly reach $2.6 trillion in 2026, generative AI now touches 65% of Fortune 500 workflows, and your competitors in both the mid-market and enterprise space are deploying agents, copilots, and predictive models at a pace that would have seemed impossible 3 years ago.

At some point, every real estate or PropTech company hits the same wall: the tools that got you here aren’t the ones that will scale with you. The question isn’t whether to rebuild the stack — it’s knowing what you actually need before you spend money finding out.

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 . But scaling AI from pilot to production exposes a structural problem most teams discover too late: **direct LLM API integration** creates fragility at scale. The question is not whether your organization will consume multiple LLMs. It is how you will govern that consumption without building bespoke infrastructure for every provider.

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 . The shutdown was swift, with access suspended just days after the models launched. Enterprises that had built production workflows around those models lost access overnight. The event was a wake-up call, but the underlying risk had been building for years.

In late June 2026, two of the most widely used AI agent platforms were compromised within the same week. Langflow disclosed a critical unauthenticated remote code execution flaw. Dify, powering over one million applications, revealed four vulnerabilities that exposed private conversations and internal APIs across tenant boundaries. These weren't theoretical risks. They were production exploits hitting real infrastructure.

Your users increasingly work through AI assistants. When they ask an agent to check a case status, analyze last quarter's metrics, or kick off an approval workflow, that agent needs to access your enterprise systems. Enabling that connection is the core challenge of AI agent integration: giving AI assistants the ability to discover, understand, and safely interact with business applications and data on behalf of users.