Systems | Development | Analytics | API | Testing

Training models for physical robots is harder than training a typical model. The data has to be collected by hand through teleoperation, every change has to be tested on real hardware, and the loop from data to deployment runs constantly. In a recent proof of concept with a Singapore government agency, ClearML, Dell Technologies, and Hugging Face’s LeRobot framework turned that high-touch, manual process into an automated pipeline.

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.

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.

*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.

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

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.

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.