About Moltiplex
Coding agents are getting better fast. They can already read code, trace call paths, explain unfamiliar services, and help engineers make changes across large codebases. But when the question moves from “what does the code do?” to “what is happening in production right now?”, the workflow still breaks.
Engineers end up switching tools, opening dashboards, pulling traces, copying logs, and pasting runtime evidence back into the IDE so the agent can reason properly.
Moltiplex exists to close that gap.
What Moltiplex is
Moltiplex gives coding agents compact runtime context: machine-readable production evidence they can use through tools, MCP, and API—inside the engineering workflows you already use. It helps agents reason from code and runtime together, without forcing engineers to manually bridge the two.
What Moltiplex is not
- Not an IDE
- Not a replacement for your observability stack
- Not a generic AI wrapper or chatbot layer
It is a runtime context layer for AI-native engineering workflows.
Who it is for
Moltiplex is aimed at engineers, tech leads, and platform leads who use Cursor, Kiro, or Claude Code, own production outcomes, and want AI-assisted investigation to actually work against live systems.
Teams already on OpenTelemetry are the natural fit. If you are not instrumented yet, cursor-auto-otel is a practical place to start.
Early and focused
Moltiplex is early. We are narrowing in on one problem first: making AI-assisted production investigation useful for teams already working this way, shaped with real users and design partners—not broad platform claims.
If that matches how your team works, we would like to talk.
Prefer email without the button? hello@moltiplex.io