Your AI agents have enterprise data access and machine-speed decision authority. Guardrails enforce governance-defined, deterministic constraints on what agents can access, decide, and produce — not soft filters.
Without governed guardrails, these problems compound silently with every new data source and AI deployment.
LLM-based safety checks are probabilistic. An agent chaining tools can route around pattern-based controls — creating a false sense of constraint.
Value thresholds hard-coded in config files. Access scopes set at deployment. Compliance teams cannot see or modify them. Risk teams are excluded.
Enterprises that cannot demonstrate deterministic, governance-defined boundaries face compounding regulatory risk with every new agent deployed.
Compliance teams author guardrail policies — input thresholds, operational limits, output constraints
Guardrails applied to every agent in the mesh — inherited from governance, not configured per deployment
Every agent action evaluated against guardrails before execution — deterministic, not probabilistic
Threshold breaches trigger HITL routing or automatic rejection — configurable response per guardrail type
Every guardrail event logged — check, outcome, threshold, agent identity, timestamp
Hard constraints — not probabilistic filters. Agents cannot route around governance-authored guardrails regardless of instruction or tool chaining.
Input guardrails, operational guardrails, output guardrails — authored by compliance and risk teams, not engineering. Visible and modifiable by governance.
Input gates control what data agents can consume. Operational gates govern what decisions they can make. Output gates govern what they can produce.
Every guardrail check logged — pass, fail, threshold, trigger — for every agent action. Evidence that constraints are enforced, every time.
See how Guardrails works within the Tantor governed intelligence platform.