Runtime governance and kill switches for AI agents. Enforce policies, halt threats, prove compliance.
Runtime AI agent governance is the process of monitoring and controlling autonomous AI systems while they are executing actions in production. Unlike pre-deployment testing or static guardrails, runtime governance intercepts tool calls (like payments, database writes, or API requests) and evaluates them against real-time policies. This provides a non-bypassable "Sentinel Layer" that enforces safety, prevents rogue behavior, and supports alignment with emerging regulatory standards.
HaltState provides the critical runtime layer for managing autonomous AI agents in production.
Real-time policy enforcement for every AI action. Define rules in plain English, HaltState enforces them in under 50ms. Block payments over thresholds, require approvals for sensitive operations.
Instant kill switches at any scope. Agent going rogue? One click to freeze it. Suspicious behavior detected? Automatic quarantine. Scoped controls from single agent to entire fleet.
Cryptographically verifiable audit trails. Export Proof Packs for compliance audits. Every decision logged, every policy change tracked. Built to support SB 53 (TFAIA) alignment.
Pre-deployment testing and "guardrails" provided by LLM providers are no longer enough for autonomous agents. HaltState operates at the execution layer, ensuring that no matter what the LLM generates, your business logic and safety policies are 100% enforced.
HaltState supports alignment with California SB 53, the Transparency in Frontier Artificial Intelligence Act (TFAIA). Our "Sentinel Bridge" provides the runtime transparency and control capabilities that emerging AI governance frameworks expect.
Your intelligent assistant for governance operations. The HaltState Concierge has tool access to your entire platform - check agent status, review pending approvals, configure policies, and troubleshoot issues through natural conversation.
Policy Evaluation
Audit Coverage
To Integrate
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Add the HaltState decorator to any function. Policies are enforced automatically before execution.
from haltstate import guard
@guard("payment.process")
async def process_payment(amount: float):
# HaltState enforces policies automatically
return await stripe.charge(amount)Align with emerging AI governance requirements
Common questions about AI agent runtime governance
A kill switch is a non-bypassable control mechanism that allows humans or automated logic to instantly freeze an AI agent's ability to execute tools or communicate with external systems. It is the ultimate fail-safe for autonomous agents.
A Proof Pack is a cryptographically signed bundle containing every policy evaluation, tool call, and system response associated with an agent's session. It serves as an immutable audit trail for compliance and safety reporting.
HaltState intercepts agent "tool calls" before they execute. Our Sentinel engine checks the request context against your defined policies (e.g., "deny if amount > $500") and either permits, denies, or triggers a human approval flow.
HaltState is engineered for high-performance enterprise applications. Most policy evaluations complete in under 50ms, adding virtually zero detectable latency to the AI agent's perception or response time.
When a tool call triggers a "Human-in-the-Loop" policy, HaltState pauses the execution and notifies an administrator. The agent remains in a suspended state until a human grants or denies the specific action.
California's SB 53, the Transparency in Frontier Artificial Intelligence Act (TFAIA), establishes requirements for transparency and control over AI systems. HaltState's runtime enforcement and audit logs provide the operational capabilities to support alignment with these emerging standards.
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