The agent relies on old information.
It proposes a booking using the $299 itinerary saved earlier in the conversation.
Runtime authorization for AI agents
Put agents in production without losing control. Salus checks policy, evidence, and intent at runtime, returns exact fixes to the agent, and stops wrong actions before they become real.
It proposes a booking using the $299 itinerary saved earlier in the conversation.
The booking never reaches the airline. Live availability and the current total were never verified.
Salus returns the exact missing step. The agent searches live inventory and discovers the new $375 price.
The customer confirms the new price. Salus verifies the evidence, the booking runs, and a receipt is created.
Agents can have valid credentials, valid permissions, and perfectly formed tool calls and still take the wrong action. Salus controls the final step between intent and execution.
Salus holds unsupported writes before your backend performs them.
provider_called: false
Fixable actions return with the missing evidence and the next step.
missing: live_available_itinerary
See the policy, evidence, timing, verdict, and execution result.
provider_called: true
from salus import Salus
salus = Salus()
# do_refund is your existing tool implementation.
issue_refund = salus.protect(
"issue_refund",
do_refund,
side_effect=True,
risk="high",
)
Protect the tool that performs the write. Keep your model, agent loop, framework, and backend unchanged.
OpenAI · Anthropic · LangChain · LangGraph · CrewAI · AutoGen · Vapi · Retell · MCP
Start with one action that matters. Salus stops wrong actions, tells the agent exactly how to fix them, and sends only real judgment calls to your team.
Get started →