AI Workforce Engine.
Engineering case study by Priyansh Dobariya · FastAPI · LangChain · Redis · Qdrant
Agents with handoff boundaries.
A backend runtime for specialized industry agents, shared lifecycle context, tool access, and human escalation.
Behind the build.
Declarative agent loading
Routing and escalation
Scoped memory and streaming
Tools & technologies
What this solves
Operational tasks span distinct specialties, and one generic assistant cannot safely own every decision. The runtime organizes agents by role and makes escalation a first-class path.
How it works
FastAPI REST and WebSocket endpoints reach an agent router. Agent definitions load from declarative role files; the runtime adapter invokes models and tools. Redis holds short-lived context and Qdrant supports vector-backed lifecycle memory. Pipeline code composes agent steps and the escalation engine detects handoff conditions.
From input to outcome
A request identifies an industry and agent, loads its role and permitted tools, gathers the relevant session or lifecycle context, and streams the response. A confidence threshold, keyword rule, or tool failure can route the interaction to human review instead of continuing automation.
Decisions visible in the code
- Declarative role definitions separate domain instructions from runtime routing and tool execution.
- Distinct memory scopes reduce accidental mixing of one session, agent, or customer lifecycle with another.
What the workflow enables
Teams can build specialized operational flows on a common runtime while keeping explicit boundaries for handoff and context.
The repository describes eight industry configurations; that does not establish that all were deployed or independently evaluated.