Systems | Development | Analytics | API | Testing

Your AI-powered data science partner just got a major upgrade. The Data Science Agent now automates ML workflows with Spark, BigQuery ML, & BigQuery DataFrames.

Enterprises often host their backend services within private internal networks, whether in on-premises data centers or restricted cloud environments. This setup is essential for protecting sensitive data, meeting compliance requirements, and reducing exposure to external threats. But it also introduces a major hurdle: how do you let external consumers and partners access the right APIs without opening unnecessary doors into your private infrastructure?

The promise of agentic AI is huge. But how is it impacting the enterprise and the developers and IT professionals most likely to be working with it right now? To find out, Kong evaluated labor market data and surveyed 550 tech leaders, developers, IT decision-makers, and Kong users.

It's Monday morning, and your team is about to walk into a nightmare: over the weekend, your web app broke in three different browsers. Checkout flows are getting stuck, animations are glitching, and support tickets are already stacking up. Traditional forms of testing would take forever to find the root cause of this catastrophe, and until then, your team cannot release it. But today, no team or developer has days to spare.