TrigGuard
TRIGGUARD AGENTS
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AI agent safety

Agents compress the gap between recommendation and execution. Safety is whether dangerous or irreversible effects are blocked at the execution boundary, not whether the model sounds polite.

// Risk

Tool execution and side effects

Agents chain tools: APIs, shells, browsers, infrastructure APIs. Each step can cause irreversible side effects. Governance must sit where tool calls become commits, see AI system control layer and pre-execution authorization.

// Shift

From recommendation to execution

Many systems still optimize for answer quality. Production failures increasingly come from execution: a transfer, a deploy, a data export, a privilege change. That shift is why category pages center on AI execution governance, not generic “AI safety” essays.

// Limits

Why common controls are incomplete alone

Monitoring, human review, rate limits, and sandboxes reduce risk but do not replace a hard gate at commit time when agents run unattended. For fail-mode semantics, see fail-closed AI systems; for policy kernels, policy enforcement engine.

// Stack diagram

Execution governance stack (agents)

One reference diagram for the control loop (SVG, searchable). The pillar hub keeps a lighter path figure to avoid repeating the same asset everywhere.

Execution governance stack: agent to execution surface Flow from autonomous agent through signal frame and TrigGuard evaluation to PERMIT DENY or SILENCE outcomes, then execution surface and signed receipt on PERMIT. EXECUTION GOVERNANCE STACK Autonomous agent / tool caller Signal frame · context TGSignalFrame (declared intent + state) TrigGuard evaluation Deterministic policy · same inputs → same decision DECISION PERMIT DENY SILENCE PERMIT only Execution surface Signed decision receipt (audit · verify)
Figure: deterministic outcomes before irreversible effects. The pillar page uses a simpler path diagram; this is the full agent-oriented stack.

// Control layer

Where TrigGuard sits

TrigGuard is an authorization and receipt layer for execution requests, compatible with agent frameworks but not defined by any single vendor. Implementation: protocol, docs, products, Verify; start from runtime docs for runtime integration.

// Hub

Category pillar

Return to the cluster hub: AI execution governance.