Systems | Development | Analytics | API | Testing

‍Build Cache can meaningfully shorten CI feedback loops, but only if it’s connected to workflows where it’ll actually make a difference. So how do you figure out which workflows will benefit? That’s the part that’s been easy to get wrong — until now. Quick Connect is a new feature that takes the guesswork out of estimating time savings: it looks at your last 30 days of build data and surfaces the workflows that will benefit themost from caching.

Debug logging is a particular form of logging that records detailed information about how an application behaves during execution, so we can identify, understand, and fix issues. This guide will give you a rookie-to-pro guide to debug logging, showing you: By the end, you will have a clear, practical approach to using debug logs effectively in real applications.

*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.

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.

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.