An agent reads a hostile instruction.
A ticket, document, webpage, or user message attempts to redirect its behavior.
Why Quasentra ENTERPRISE PROFILE / 01
AI agents need authority limits.
Not only safer prompts.
Businesses are connecting probabilistic AI systems to deterministic operations: customer data, email, databases, payments, and infrastructure.
Quasentra creates an independent decision point before those operations execute.
AUTHORIZATION PATH0LLM calls
ENFORCEMENT OUTCOMES3Allow · Approval · Deny
SUPPORTED ECOSYSTEMS4Python agent frameworks
THE OPERATING PRINCIPLE
The model proposes.
Policy decides.
The tool executes last.
The business problem 02 / 05
An unsafe instruction is only half the incident.
The other half is excessive authority: a compromised or confused agent already holding the tools and credentials needed to act.
The process has broad permissions.
The same runtime can read customers, send messages, issue refunds, or delete records.
Quasentra checks authority.
The exact action is allowed, paused for approval, or denied before supported execution.
A different control layer 03 / 05
Security at the point
of consequence.
Quasentra does not ask another model whether an operation feels safe. It evaluates authenticated identity, declared action, resource, constraints, and approval state against deterministic policy.
DEVELOPER DECLARESAgent identityCallable toolsIntended permissions
QUASENTRA
Policydeterministic
Approvaloperation-bound
Evidencedecision-linked
BUSINESS RECEIVESLess standing accessHuman controlAuditable outcomes
Code remains the source
Developers declare agents, tools, and intended permissions beside the application. The operational inventory synchronizes automatically.
Decisions stay explainable
No model call sits in the authorization path, so outcomes follow explicit policy without adding model-token cost.
Credentials can move server-side
For higher-risk workflows, supported gateway connectors keep business secrets outside the agent process.
Market context 04 / 05
Different products protect
different layers.
The market includes capable prompt, model, governance, and agent-security platforms. Quasentra presently focuses on developer-installed authorization immediately before supported agent tools run.
PlatformPublicly stated focusQuasentra's current distinction
Lakera Guard ↗
Prompt-injection and model input/output protection.
Deterministic permission checks on named business-tool operations.
Prompt Security ↗
Enterprise visibility and protection for generative-AI use and applications.
Code-level agent/tool registration synchronized to an operator workspace.
Zenity ↗
Security and governance for enterprise AI agents from build time to runtime.
A lightweight Python SDK with explicit per-action policy for locally built agents.
Cisco AI Defense ↗
Enterprise AI discovery, validation, and runtime protection.
A one-agent starting point rather than a broad security-estate rollout.
NVIDIA NeMo Guardrails ↗
Open-source programmable guardrails for LLM conversational systems.
Hosted policy operations, scoped keys, approvals, incidents, and tool-execution audit.
This comparison summarizes public product positioning, not an independent feature audit. Products overlap and change; buyers should validate every platform against their architecture and risk model.
Clear by design 05 / 05
What is available today.
And where the boundary ends.
Current coverage
- Python SDK published on PyPI
- LangChain and LangGraph middleware
- CrewAI and OpenAI Agents adapters
- Default deny, constraints, and one-time approvals
- Tenant isolation, scoped keys, and replay protection
- Dashboard, incidents, and audit evidence
Boundaries we state clearly
- No security product guarantees that an agent cannot be compromised.
- Local wrappers can be bypassed if original sensitive tools or credentials remain exposed.
- Gateway enforcement is recommended for high-risk side effects.
- n8n, MCP, and additional language SDKs are not in the current verified release.
- Quasentra does not currently claim SOC 2 or ISO 27001 certification.
START SMALL. PROVE CONTROL.A low-risk path
A low-risk path
to accountable agents.
Begin with one production-bound workflow, observe real decisions, and expand only when the control model fits your team.
Choose one agent with clear tools and measurable risk.
Install the SDK and declare the intended permissions.
Review allowed, blocked, and approval-required operations.
Expand only after the operating model proves useful.
Give one agent a smaller blast radius.
No credit card is required for the Basic plan.