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With 34% of software failing to meet quality standards, and 77% of AI failures significant enough to damage user trust and product integrity, new research highlights why global brands must pair AI orchestration with expert human insight.

Debugging Android apps can feel daunting given the sheer size of the ecosystem. However the suite of Android Studio debugging tools allows us to find, fix and prevent problems across our entire user base. Wherever they are, whichever devices they’re using. This guide will give you a practical workflow to maximize the functionality of Android Studio, and empower you to debug Android apps from first breakpoint to production issues.

Production AI no longer generates one kind of traffic. It generates four patterns, and most teams govern only one. **AI traffic management** starts with a single decision: **proxy AI traffic** through one control point instead of letting it flow straight from application code to model providers. Skip that step and security teams have no policy chokepoint, token spend climbs with no meter, and every new provider adds an integration nobody owns.

As enterprises race to adopt AI, weak data foundations are preventing more than half (58%) of organizations in the United States and Canada from realizing value and contributing to an estimated $108 billion in wasted global AI investment each year, according to a report from Hitachi Vantara. The reason is rarely bad models or lack of ambition.

While the benefits of LLM orchestration layers and autonomous agents are clear, they also bring a new set of non-deterministic failure modes that traditional unit testing cannot detect. A study by RAND Corporation found that 80.3% of AI projects fail to achieve the desired business outcomes, and this is because of issues in the data pipeline and model integration, not algorithmic problems.Using traditional software, you will get predictable results from known inputs.

We have big news for platform and AI infra teams: *Kong AI Gateway 2.0 is available today in private beta*. It runs on its own dedicated runtime, ships on its own release cadence, and carries a completely reimagined user experience designed around the way teams actually build with AI: models, MCP servers, and agents as first-class citizens, not plugins bolted onto an API gateway.

Last April FloQast, an American maker of accounting software, published something unusual: a detailed engineering post on Amazon Web Services’ machine-learning blog, co-authored with AWS personnel, explaining precisely how its AI-powered transaction-matching feature works under the hood. The post described cloud infrastructure, model selection, and the specific technique (generating matching rules from user-supplied examples) that powers its AutoRec product.

Learn how to use BigQuery to analyze Gemini Enterprise usage logs, audit compliance, and build Looker dashboards at scale for your organization.