Saz: Engineering Safe Agentic Workflows for Real-World Operations#

Fariman Torkashvand (GÉANT)

• Why: Agentic AI that plans actions, calls tools, and triggers real network automation is arriving in operations; this is a concrete architecture for doing it without losing control. The risk framing — hallucinated decisions, escalating costs, unclear accountability — matches what any operator planning agent deployment should be worrying about.
• What: Saz, a self-hosted agentic workflow engine designed to embed AI in operational environments through structured workflows, human-authored instructions, and strict policy enforcement. Its distinguishing feature is a dual-agent safety model: a verifier agent evaluates the planner agent's output before execution. (from abstract only; slides not extractable)
• How: Constraints over autonomy — workflows are structured and authored by humans, policy is enforced engine-side, and the verifier/planner split provides an internal check step. The output is patterns for AI-driven workflows that remain controlled, auditable, and human-governed.
• Where applicable: General pattern, GÉANT-flavored implementation. Self-hosted, which matters for the sovereign-AI theme of the session. Early-stage work rather than a long-production retrospective.