Systems | Development | Analytics | API | Testing

Kong Context Mesh composes APIs, data, and documents into a single MCP interface that AI agents can call directly, with no custom harness or extra instructions. Built on Kong Konnect, it supports different authentication strategies and data structures, and avoids context window bloat through progressive disclosure and on-the-fly execution. Agents discover tools just in time and retrieve only the context needed for the task. The demo connects internal APIs and external MCP servers, then uses Claude Code to complete a multi-source task.

10x faster coding. Same QA. Until now. You're coding faster with AI. But if your agents are siloed and disconnected, those gains aren’t yet showing up in your quality or delivery – it's getting lost in the handoffs. Join SmartBear for a live demonstration of the connected agentic SDLC. See how BearQ extends beyond creating tests to coordinating quality work across Jira, GitHub, and AI-powered workflows – detecting issues, triggering the right next action, and carrying context from one agent to the next.

Secure AI starts with securing the APIs underneath it. This session breaks down why APIs and MCPs are the control plane for AI apps and agents, where unmanaged API risk hides, and how security and platform teams can improve visibility, posture, runtime protection, and governance across production environments. KEY TAKEAWAYS TIMESTAMPS.

This video provides an overview of using Sessions to group traffic by actor credentials, track individual user journeys chronologically, and analyze request flows. It also covers how to check replay readiness for JWT sessions, replay recorded user journeys at their original pace, verify pass/fail counts, and save or sample specific sessions for easy sharing. Learn more: speedscale.com.

Learn how to integrate your AI coding agent with Speedscale using the MCP server to record, replay, tune mocks, and analyze reports directly within your workspace. Streamline your testing workflow by letting your agent automatically capture traffic, resolve failing responses, and optimize match rates. Learn more: speedscale.com.

Learn how to track cluster performance in real time using the replay progress panel, which highlights key phases including provision, mock, app, load, analyze, and result. You will also discover how to easily troubleshoot failed runs by expanding component lanes to review specific step reasons and cluster events. Learn more: speedscale.com.

Learn how to resolve failed GraphQL session queries by updating expired tokens before replaying your snapshot. This video demonstrates how to find the GraphQL operation, edit variable values by path, and successfully pass replay checks. Learn more: speedscale.com.

Proxymock allows you to import existing Postman collections, browser HAR files, and OpenAPI specifications to instantly create test requests and mock responses. You can also pull traffic directly from cloud storage buckets, or export your requests into load testing scripts for tools like K6, Gatling, and Locust. Coding agents can even manage these imports and exports using integrated MCP tools. Learn more: speedscale.com.

Learn how to analyze a 30-second local load test using AI and Proxymock to track latency and P99 metrics effortlessly.