Systems | Development | Analytics | API | Testing

What did Atlassian Team ’26 reveal about the future of software quality and AI-powered delivery? In this recap from the event floor inside the Anaheim Convention Center, SmartBear shares key themes from the event, including Atlassian Rovo, the Teamwork Graph, AI-driven workflows, and how QA teams are adapting to faster, AI-assisted software delivery inside Jira. See quick highlights from the event floor, SmartBear’s latest Zephyr innovations, and how conversational AI and quality intelligence are becoming part of the modern software delivery workflow.

Most AI revenue will flow through tokens — and the two bottlenecks are tokens per watt (energy cost) and tokens per second (throughput). Tokens per watt determines how much output you can generate from a fixed energy supply — already constrained and getting tighter. Tokens per second sets the ceiling on how fast that revenue can flow. Kong's AI Gateway optimizes both at the connectivity layer: semantic caching and semantic routing increase token output without adding watts or latency.#Shorts.

Learn how to generate a client SDK for a production service when you have no documentation, no OpenAPI spec, and no remaining team knowledge of the original Ruby code. This demo shows you how to capture real production data from a running app and transform it into a functional Java client library in minutes. Visit proxymock.io OR speedscale.com to learn more.

LLMs are absorbing the business logic of microservices for agentic use cases — but both patterns will coexist in enterprise infrastructure for a long time. Cloud-native infrastructure (microservices + APIs) keeps powering web and mobile experiences. The agentic layer — LLMs, MCP tool calls, and context traffic — runs in parallel, activating the same APIs and CRUD operations underneath. Kong manages both swim lanes: the API traffic between clients and microservices, and the context traffic flowing between agents and LLMs.#Shorts.

CLI offers speed and developer freedom for API access; MCP provides centralized security, governance, and observability at enterprise scale. With CLI, credentials live on the developer's local machine and audit trails are shell-only — fast, but ungoverned. MCP adds authentication, centralized policy enforcement, and observability across all API calls, at the cost of some speed and higher token consumption. Kong's MCP Gateway is built for teams that need the governance trade-off without giving up too much velocity.#Shorts.

Software is going headless: the internet is shifting from GUIs built for humans to APIs, MCP servers, and CLIs built for machines and agents. Machines will consume the internet at a scale 1,000x greater than humans — more agents will exist than people, and programmatic access moves far more data than any click ever could. This transition requires API and AI infrastructure capable of moving terabytes at a scale never built before. Kong provides the connectivity layer for this machine internet — the infrastructure between agents, LLMs, and the services they consume.#Shorts.