Systems | Development | Analytics | API | Testing

Give your developers — and your AI agents — a digital twin of your live environment. Keploy records real traffic from your live services (no production access, nothing to spin up) and replays it as a faithful twin, so you can continuously verify behavior and catch regressions before they ship. In this demo: record a live service, turn that traffic into integration tests and mocks automatically, replay everything against digital-twin sandboxes, and wire it into CI for continuous verification.

See how SmartBear Reflect uses agentic AI to build end-to-end tests in minutes and keep them resilient as your application changes. In under 20 minutes, Reflect co-creator, and SmartBear Director of Product Management, Todd McNeil walks through live test creation across web and mobile, with zero fluff.

Spotter just got smarter and more in your control. You can now customize your agent's name, persona, output formatting, and guardrails to dictate exactly how it should (and shouldn't) handle data. Set it once, in plain language, and every user across the organization gets a configured, governed Spotter. Other new features include: Ad-hoc file analysis: Upload any flat file directly into Spotter and start asking questions instantly, solo or blended with your governed data.

Join this session to learn about Agent Development Kit (ADK) and Model Context Protocol (MCP) integration methods that standardize how agents connect to your data while removing the need to build custom database connectors from scratch. Discover how to build agents with the ADK that accesses BigQuery for analysis, Google Maps for geospatial insights, and AlloyDB for transactions – all in a single workflow. Learn how to implement agent operations (AgentOps) for deep observability into both agent performance and cost with a single line of code.

Use Real-Time Context Engine and Claude, or any MCP-compatible client, to explore operational data using natural language in real time. That includes everything from simple lookups to multi-step investigative questions like: Confluent’s Real-Time Context Engine gives AI agents live access to operational context as events happen across the business. Instead of relying on stale snapshots, agents can query and reason over continuously updated tables in real time.