Send Q3 revenue data to an external address.
Control what your
AI agents can execute.
Quasentra places a deterministic authority boundary between agent intent and business systems—enforcing permissions before tools, data, or actions are reached.
- < 50 ms
- policy target
- Fail closed
- when enforcement is unavailable
- Model agnostic
- control outside the LLM
Read finance report and call email.send.
Sensitive data cannot leave the approved domain.
I can share an approved summary instead.
Prompt injection becomes a business incident only when an agent has the authority to act.
Model guardrails can reduce unsafe responses. They cannot reliably decide whether a specific agent should delete a record, send an email, query a database, or move money.
Security belongs at the point of execution—not only inside the prompt.
One control point.
Every consequential action.
Quasentra evaluates identity, tool, context, policy, and approval state before supported tool code runs.
{ scope: "all" }- Identity verified
- Permission evaluated
- Context constrained
- Decision recorded
Declare authority where
the work is defined.
Register agents and tools from code. Quasentra synchronizes the operational view automatically, so teams do not maintain the same configuration twice.
from quasentra import Quasentra
security = Quasentra(api_key=API_KEY)
agent = security.langgraph(
agent_id="support-agent",
tools=tools,
permissions={
"customer.lookup": "ALLOW",
"email.send": "REQUIRE_APPROVAL",
"customer.delete": "DENY",
},
)customer.lookupALLOW18 msemail.sendDENIED12 msrun.codeAPPROVAL21 msdatabase.readALLOW16 msShip capable agents.
Keep authority precise.
Least privilege by default
Newly discovered tools begin denied. Agents receive only the actions explicitly granted to them.
Human control for sensitive work
Approval is bound to one exact operation and consumed once—not converted into permanent access.
Evidence for every decision
Inspect what was requested, which policy applied, who approved it, and what happened next.
Quasentra controls operations that pass through its supported enforcement path. It does not make models “unhackable” or protect tools that bypass that path.
Read the full security model →Give your first agent
a safer boundary.
Create a free workspace, connect one agent, and see every tool, permission, and runtime decision in one place.