Santanu Bhattacharjee

Causal Guardrails: Structural Causal Models Keeping Agentic AI Sane

Agentic AI systems are rapidly moving from demos to mission‑critical workflows, but most of them still behave as pattern‑matchers with tools, not as systems that understand cause and effect. The result is familiar: agents that sound confident while suggesting actions that quietly violate business logic, break regulations, or create hidden risk. This talk introduces “causal guardrails”—an architecture where structural causal models (SCMs) sit around GenAI agents to constrain, explain, and validate their decisions. Instead of relying solely on prompts and heuristics, agents must route their plans through explicit causal graphs that encode allowed interventions, downstream impacts, and hard constraints. The session will walk through intuitive examples (credit risk, IT ops, or recommendation workflows), show how to combine LLM-based agents with SCMs in practice, and discuss how this improves robustness, debuggability, and auditability. Attendees will leave with concrete patterns for using causal modeling to keep autonomous GenAI “sane” in real enterprise environments, not just in benchmarks.