Systems | Development | Analytics | API | Testing

AI can generate code in seconds, but testing it against real production behavior is where developers get stuck. Learn how capturing and replaying actual production traffic in development helps you catch bugs before you deploy!

You can add AI-driven testing to your Jira workflow by handing tickets to the QA Lead Agent in SmartBear BearQ for Jira. Move a finished ticket, and the agent checks for an existing test, links or creates one, runs it, and reports back. If your team builds with coding agents like Claude or Cursor in Jira, BearQ, the agentic QA system, adds the quality step to that AI-driven software development lifecycle (SDLC).

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.

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.

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 quickly set up a load testing foundation for your Node app using the free ProxyMock tool combined with an AI coding agent. This walkthrough demonstrates how to record traffic, mock dependencies, and analyze latency distribution results in just a few simple steps. Learn more: speedscale.com.

DevOps customers are rejecting standalone AI agents and web interfaces in favor of integrating context directly into their existing harnesses, such as Copilot or Claude. Rather than logging into separate tools, developers prefer combining models and technologies within a single context window to streamline software development and debugging. 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.