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How OAuth 2.0 Token Exchange Reshapes Trust Between Services — and Why the API Gateway Is Exactly the Right Place to Enforce It OAuth 2.0 Token Exchange fundamentally alters how services establish trust. And the API gateway is the ideal location to enforce this new model. Modern applications don’t run as a single monolithic.

How Kong Gateway 3.14 closes the consistency gap in IAM-based authentication across AWS, Azure and GCP — and what it means for your production deployments Enterprise security teams have clear requirements: no static credentials, no exceptions. Every service-to-service connection, whether it's Kong talking to databases, caches, or vaults, should authenticate using the same IAM-based identity model that governs the rest of their cloud infrastructure.

Modern API architectures are no longer linear. A single request can traverse multiple layers of authentication, transformation, enrichment, and observability. As these flows grow more dynamic, the need for fine-grained control over when plugins execute becomes critical. For years, the standard approach to API Gateway configuration followed a strict hierarchical model: you applied a plugin to a Service, a Route, or a Consumer.

We are in the middle of the fastest acceleration in software development that the industry has ever seen. Thanks to highly capable models from technology leaders like Anthropic and OpenAI, we have entered the era of vibe coding—a world where developers describe what they want in natural language and get working software in return.

Hybrid AI systems need secure ways to manage user identities across cloud and on-premises environments. Identity passthrough ensures that AI systems operate under the permissions of the actual user, not a shared service account. This approach reduces risks tied to credential theft, improves auditability, and supports compliance with regulations like GDPR and HIPAA. Key methods for identity passthrough include: Quick Takeaway: For organizations prioritizing simplicity, PHS is a good starting point.

You asked Claude to generate a bitrise.yml. It came back clean: right steps, reasonable workflow names, valid YAML. You almost merged it. Then you noticed it’s using before_run instead of step bundles. There are no version locks on steps. The triggers are structured in a format Bitrise deprecated months ago. It’s a valid config, but it would never pass code review. The quality of an agent's interaction with your CI/CD comes down to two things: what it can do and what it knows.

The shift from single-model AI features to multi-agent pipelines is no longer a future concern — it's running in production today. MCP has become the de facto protocol for tool-calling, agent-to-agent (A2A) communication patterns are proliferating, and enterprise teams are wiring together complex AI workflows that span multiple providers, services, and agents. Every hop in that data path is an opportunity for something to go wrong. The challenge is governance.