Systems | Development | Analytics | API | Testing

Building Secure AI Agents with Kong's MCP Proxy and Volcano SDK

Modern AI applications are no longer just about sending prompts to an LLM and returning text. As soon as AI systems need to interact with real business data, internal APIs, or operational workflows, the problem becomes one of orchestration, security, and control. The challenge is to build secure AI agents without embedding fragile logic or exposing sensitive systems directly to a model. This is where a layered architecture using Volcano SDK, DataKit, and Kong MCP Proxy becomes compelling.

Best Practices for AI in CI/CD QA Pipelines

AI transforms CI/CD testing from reactive bug detection into proactive quality assurance that accelerates release cycles while improving software reliability. Start embedding AI into your testing workflows now because teams that wait will struggle to match the velocity of competitors who already have. Continuous integration and continuous deployment pipelines have become the backbone of modern software delivery.

Chat with Your Data: The Official Databox MCP

Your AI is brilliant, but it’s blind. Until now. We are thrilled to launch the official Databox MCP (Model Context Protocol). This open standard server bridges the gap between your business data and your favorite AI tools, turning general-purpose LLMs into specialized data analysts that know your business data. Stop manually exporting CSVs or taking screenshots of dashboards. With Databox MCP, you can connect 130+ data sources (Google Analytics, HubSpot, Salesforce, Stripe, and more) directly to tools like Claude, ChatGPT, Cursor, and n8n.

What Is MCP? Connecting AI Across the Software Delivery Lifecycle

AI promises speed and automation — but most teams are still stuck jumping between disconnected tools across development, testing, and operations. In this video, we introduce the Model Context Protocol (MCP) and how it enables AI assistants to securely access tools, systems, and real-time context across the software delivery lifecycle. MCP is the foundation of Perforce Intelligence, allowing AI to: The result: less friction, faster feedback, and AI that works with your existing systems — not around them.

Identity Passthrough for AI: Why Your LLM Needs to Know Who's Asking

When a user asks your AI assistant a question, who actually runs the database query? In most enterprise AI deployments, the answer is troubling: a shared service account with broad access to everything. The user's identity evaporates the moment their request enters the AI system. This architectural pattern creates security gaps, compliance failures, and data leakage risks that undermine enterprise AI adoption.

2026 Predictions: What's Next for Data Streaming and AI | Life Is But A Stream

AI isn’t just evolving—it’s reshaping who your customers are, how systems operate, and what real time really means. From machines making purchase decisions to agents increasing query volume across databases, the realities of 2026 are forcing leaders to rethink data architecture and governance strategies at a fundamental level. In this episode, Joseph is joined by Will LaForest (Field CTO, Confluent), Adi Polak (Director of Developer Advocacy & Experience, Confluent), and independent analyst, Sanjeev Mohan, to break down critical insights from Confluent’s 2026 Predictions Report.

Powering agentic software quality with MCP servers | From the Bear Cave

In this From the Bear Cave session, Dan Faulkner, CEO of SmartBear, and Vineeta Puranik, CTO/CPO of SmartBear, discuss why agentic automation is becoming essential in software development and delivery, how MCP Server enables connected autonomy across the SDLC, and what this transformation means for business outcomes and the future of human + AI collaboration.