Sazabi is built specifically for the observability challenges of multi-agent AI systems, where a single user request can trigger dozens of LLM calls, tool invocations, and inter-agent communications. Traditional APM tools lack the context to trace these complex agentic workflows, showing individual API calls without understanding the relationships between them. Sazabi provides a purpose-built tracing system that visualizes the complete execution graph of agentic workflows.
The platform traces requests through agent chains, showing each LLM call with its prompt and response, tool calls with their inputs and outputs, and handoffs between agents. Developers can identify bottlenecks in multi-step reasoning chains, debug agent loops that spin without converging, and analyze token usage patterns across different parts of the workflow. The interface is designed for high-velocity teams, emphasizing speed of navigation and clarity of information over exhaustive configuration options.
Sazabi is backed by engineering leaders from Vercel, Graphite, and LangChain who experienced the observability gaps in building AI products firsthand. The platform is in active development with a sales-contact model for early access. For engineering teams building production multi-agent systems where debugging agentic behavior is a significant challenge, Sazabi provides the specialized observability infrastructure that generic monitoring tools cannot offer.
