Systems | Development | Analytics | API | Testing

Selecting a cloud testing platform is a high-stakes decision for IT managers and QA leads. The market is crowded with vendors touting AI, speed, and integration, but the real test is whether a platform delivers the core capabilities your team genuinely needs. Begin by defining your non-negotiables – features that are essential for your workflows and compliance requirements.

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.

*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.

You ask your agent a question. The answer is slightly off. You point out the gap. Spotter fixes it, and that fix doesn't disappear when the session ends. Your team doesn't re-explain the same thing tomorrow. The next analyst doesn't start from scratch. The correction stays, and the work gets better from here. That's what memory makes possible. Not just for you. For everyone who comes after.

Twenty years of boots-on-the-ground testing experience reveals a clear pattern: the industry has moved from tracking manual test cases in Excel sheets, to managing Selenium Grid configurations, to watching algorithms generate scripts in seconds. Right now, if you are in a managerial role, your feeds are absolutely flooded with pitches promising that.

In June 2026, the highest-grossing law firm in the world committed $500 million to build its own AI platform. The firm put more than 180 engineers and data scientists and over 250 of its lawyers on the effort. It chose to build because general-purpose tools could not execute their transactions or reason over their massive institutional knowledge. That is the bill for a first-class AI product built from scratch.

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.

The agentic AI conversation is too obsessed with the front door. Customer-facing agents get most of the attention because they are visible.

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.