Systems | Development | Analytics | API | Testing

Leveraging AI For a Better API Strategy

“API strategy” is a term prominently established in the ecosystem and heavily discussed, implemented, and followed by organizations. The term is more relevant now since API strategy has become, for the most part, AI strategy, since AI agents and services are now consuming APIs and tools to work towards business-specific goals under human tutelage. So the longstanding definition and scope of API strategy must take into account AI consumers.

New: Ask your data anything, and get clear answers in seconds

You know that moment. You open your dashboards, and something in the numbers looks off. Revenue is trending down, the pipeline feels lighter, or your campaigns aren’t delivering the results you expected. You can see the numbers, but you need to understand what’s happening and whether this is a short-term fluctuation or an early signal of something bigger. So you start digging. You move between dashboards, compare time periods, cross-reference metrics, and pull in context from different teams.

Operationalizing the Model Context Protocol: Unified Governance with the WSO2 MCP Gateway

The WSO2 API Platform offers an MCP Gateway that sits between MCP clients and the MCP servers they use, applying security, access control, rate limits, observation, and policy enforcement across all tool calls. Instead of requiring teams to write these controls directly within their MCP servers, the platform extends its existing API governance layer to cover MCP traffic.

Ep 65 | The Vibecoding Liability: How Unchecked AI Can Kill Cloud ROI

Cloud computing promised efficiency, scalability, and reliability. But as AI workloads grow more complex, many enterprises are learning the hard way that these promises don’t come automatically. In this episode of The AI Forecast, Paul Muller sits down with Linthicum Research founder David Linthicum to talk through the real state of hybrid cloud strategy and enterprise architecture in the age of cloud computing and AI.

SmartBear Application Integrity Core | Redefining software quality for the AI era

Agent-powered code generation is happening at unprecedented speed, creating a growing gap between development velocity and your ability to validate what's being built. This leaves organizations unsure if their applications are doing what's intended or missing what's required. That's why SmartBear delivers application integrity for the AI era – ensuring continuous, measurable assurance that your software just works as intended, with governance to operate at AI speed and scale.

Meet SmartBear BearQ - QA for the Age of AI

The AI revolutionized coding, but software testing hasn’t caught up. Until now. Meet BearQ: QA built for the age of AI. BearQ introduces a new paradigm of autonomous, agentic quality assurance. Instead of static scripts and brittle frameworks, BearQ’s specialized AI agents – the QA Lead Agent, Tester Agent, and Explorer Agent – work continuously to: Testing was a static checkpoint. Now it’s a living, learning system that ensures application integrity.

Launching Project SnowWork - Bringing Outcome Driven AI to Every Business User

Project SnowWork empowers business teams to automate multi-step workflows end-to-end, and drive real outcomes. Create revenue snapshots, diagnose missed forecasts, and generate summary slides with next steps — all without any coding experience needed.

Enterprise AI Infrastructure Security - 4) Service Accounts & Automation Security

Securing ClearML for the Enterprise — Part 4: Service Accounts & Automation Security In this video we walk through ClearML's service accounts — the identities behind your automated workloads — and how impersonation ensures least-privilege execution across your agents, pipelines, and schedulers. What we cover: Previous videos in this series.

The new rules of QA for AI-driven finserv

Contents AI is now embedded across the entire software development lifecycle. Developers use it to generate code. Product managers use it to prototype features. Teams use it to move from idea to deployment faster than ever. Code moves faster. Features ship more frequently. Iteration cycles shrink. Across industries, companies that embrace this speed have a distinct competitive advantage. But in highly regulated industries, including financial services, speed can’t come at the cost of quality.

What is an AI Data Gateway? | DreamFactory

An AI Data Gateway is a secure intermediary that connects enterprise data sources (like databases and file systems) with AI systems. It simplifies how AI accesses data while enforcing strict security, compliance, and governance measures. Instead of allowing direct access to sensitive data, the gateway uses secure REST APIs to control and monitor all interactions.