BlackBox-Labs --AI Infrastructure & Orchestration

**BlackBox Labs** is an AI Infrastructure Control Plane designed to make multi-agent AI systems observable, reliable, and manageable. Rather than focusing only on AI-generated responses, it provides the infrastructure needed to orchestrate, monitor, and analyze agent workflows. Built with **Next.js**, **FastAPI**, and **LangGraph**, the platform coordinates three specialized agents: a **Planner** that creates an execution strategy, a **Researcher** that gathers and expands information, and a **Reviewer** that validates and synthesizes the final response. Each stage is independently tracked and monitored throughout execution. The platform includes a **Model Routing Layer** that dynamically routes requests across providers such as **Google Gemini** and **OpenRouter-hosted models**. To improve reliability, BlackBox Labs supports automatic retries, provider fallback mechanisms, timeout protection, and error recovery, allowing workflows to continue even when individual models or providers fail. A core feature of the project is **observability**. Users can inspect execution timelines, monitor agent status, view routing decisions, analyze provider usage, track fallback events, and review execution metadata including runtime, model selections, and success metrics. BlackBox Labs also provides an **analytics layer** with insights into success rates, execution volume, average runtime, provider utilization, fallback frequency, and agent performance trends. In addition to the web dashboard, the platform integrates with **Band.ai** through a custom remote agent, enabling users to access the same orchestration engine from an external collaborative agent environment. By combining multi-agent orchestration, model routing, reliability engineering, observability, analytics, and Band.ai integration, BlackBox Labs demonstrates how AI workflows can be operated as infrastructure systems rather than isolated chatbots.