Systems | Development | Analytics | API | Testing

Dashboards, sampling, and data lakes were built for human debugging. Closing the bug-to-fix loop for AI agents requires rethinking how runtime data is collected and correlated. Observability as we know it is on its way out. For over a decade, we built telemetry stacks around a single consumer: a human, staring at a dashboard, trying to make sense of a system under stress.

AI is accelerating how teams build and ship software, but validating quality is getting harder, not easier. More AI-generated code means more to test, more API drift to catch, and more documentation that falls behind. The work is growing faster than teams can keep up.

Digital banking is entering a different phase in 2026. Growth is no longer driven by mobile apps alone. It is being driven by embedded finance, AI powered personalization, instant payments, and API driven banking ecosystems. According to BCG, traditional banks are steadily losing ground to fintechs and digital first banking platforms as customer expectations continue to shift toward real time and seamless financial experiences. At the same time, the market is getting crowded.

Version v0.2.0 of @ably/ai-transport reorganises the SDK to better support a wide range of interaction patterns. Everything in an AI session – input, output, agent lifecycle, control signals – is captured durably, allowing you to easily build the sophisticated interaction patterns that support modern AI user experiences. When we first built @ably/ai-transport, we modelled an AI conversation the way most people first picture it: as a request and a response.

The gap between how fast organizations can generate software and how confidently they can validate it is becoming one of the defining operational risks of this era.

Moving a retrieval-augmented generation (RAG) prototype from a Python notebook into production isn't an API orchestration challenge. It's a distributed systems problem. For engineering managers and data platform leads, the build-versus-buy decision on streaming infrastructure will dictate your artificial intelligence (AI) feature velocity for the next three to five years. This guide assumes you've already prototyped a RAG pipeline.

The EU AI Act's general provisions are already in force, and high-risk AI system obligations apply from August 2026. The National Institute of Standards and Technology (NIST) AI Risk Management Framework and its Generative AI Profile set the baseline for what auditors expect, framing governance around four functions: identify, measure, manage, and monitor. Deploying artificial intelligence (AI) agents in regulated environments isn't a sandbox experiment anymore. It's a strict governance challenge.

AI runs on data, and global enterprises are awash with petabytes of data. That might suggest that it’s easy for companies to advance their businesses through the power of AI. Yet enterprise data is often fragmented across departmental and technological silos, and that data is often inconsistent, ungoverned and disconnected from mission-critical systems. As a result, many AI initiatives stall before they can deliver operational value, and the root cause is rarely the model.

In the first two articles of this series we showed what the OctoPerf MCP Server does. This one is for the builders: how we designed it, and specifically how we kept its token cost under control. Because here is the thing nobody tells you when you start writing a Model Context Protocol server: the hard part is not exposing your API to an LLM. The hard part is not exposing too much of it.

AI agents move fast. Securing them shouldn't slow you down. Kong and Noma are partnering to change that. This program delivers high-quality, reliable integrations providing real business value for customers..