Agent Immune System with Causal Guardrails

Agent Immune System (AIS) is a runtime defense for AI agents. Agents act on whatever is in their context, so an attacker can hide instructions in a retrieved document or support ticket and make the agent exfiltrate data. This is indirect prompt injection, the defining attack surface for agents. Detection tools raise an alert but never say which input caused it or how to fix it. AIS closes that gap in one loop. When a session is flagged, AIS does 3 things that most defenses miss. First, AIS detects from provenance flow, not keywords, so that an external send of sensitive data while untrusted input is present is stopped fast and hard.  Second, Causal replay re-runs the session with each untrusted input removed to prove the exact input responsible. Third, AIS vaccinates with an open model on AkashML that generates evasive variants, a guardrail is synthesized, and it is verified against the entire recorded corpus in ClickHouse, deploying only on zero false positives. Re-running the attack is then blocked. Live on ClickHouse Cloud on over 601,000 rows.   Detection in ~59 ms, verification in ~74 ms, 9 of 9 variants blocked with 0 false positives. The novel and innovative part is the combination of runtime causal attribution plus auto-remediation regression-tested against production history.