A support agent reads hostile content
A ticket, document or webpage asks the agent to ignore its role and use another tool.
Enterprise profile
AI agents need authority limits, not only safer prompts.
Quasentra sits between an agent's intent and its business tools. It decides whether an operation may run, must wait for a human, or must be blocked—before the supported tool executes.
Start with one workflow. No infrastructure replacement and no credit card for the Basic plan.
For software housesAdd enforceable controls to the agents you deliver.
For internal AI teamsKeep permissions visible across agents and tools.
For business ownersRequire approval before sensitive side effects.
The risk in plain language
An agent can be manipulated even when the model is capable.
Prompt injection is one route to failure. Over-permissioned tools, leaked credentials, confused tenants and unaudited side effects can turn a bad instruction into a real business action.
The agent already has broad access
The same process can read customers, send email, issue refunds or delete records.
The instruction becomes an incident
Without an independent decision point, the tool may execute before a human can intervene.
What Quasentra changes
The model proposes. Policy decides. The tool executes last.
Quasentra does not ask another model whether an operation feels safe. It evaluates authenticated tenant, agent, action, resource and constraints against deterministic policy.
- Newly discovered tools start denied by default
- API keys are scoped to named agents and capabilities
- Approvals bind the exact action and arguments and are consumed once
- Decisions and incidents remain visible in one dashboard
- High-risk credentials can stay behind a server-side gateway
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Agent requests a toolExample: refund invoice INV-200
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Quasentra evaluates policyIdentity, action, resource, limits and replay checks
ALLOWAPPROVALDENY
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Only an authorized operation proceedsThe business tool remains the final execution step
Market context
Different products protect different layers.
The market includes strong prompt, model, governance and agent-security platforms. Quasentra's present focus is narrower: developer-installed authorization immediately before supported agent tools run.
| Platform | Publicly stated focus | How Quasentra differs in focus |
|---|---|---|
| Lakera Guard | Prompt-injection and model input/output protection. | Quasentra centers deterministic permission checks on named business-tool operations. |
| Prompt Security | Enterprise visibility and protection for generative-AI use and applications. | Quasentra starts from code-level agent/tool registration and synchronizes it to an operator dashboard. |
| Zenity | Security and governance for enterprise AI agents from build time to runtime. | Quasentra currently emphasizes a lightweight Python SDK and explicit per-action policy for locally built agents. |
| Cisco AI Defense | Enterprise AI discovery, validation and runtime protection across AI applications. | Quasentra is designed for teams that want to begin with one agent and one integration rather than a broad security estate. |
| NVIDIA NeMo Guardrails | Open-source programmable guardrails for LLM conversational systems. | Quasentra provides hosted policy operations, approvals, scoped keys, incidents and audit around tool execution. |
Comparison summarizes public product positioning, not an independent feature audit. Products can overlap and change. Buyers should validate every platform against their own architecture and risk model.
Why teams choose this approach
Practical controls without maintaining policy twice.
Code remains the source
Developers declare agents, tools and intended permissions beside the application. The dashboard receives the discovered inventory automatically.
Predictable decisions
Authorization does not invoke an LLM, keeping security outcomes explainable and model-token cost out of the decision path.
Local-team accessibility
Start with familiar Python frameworks, a hosted control plane and a low-volume free tier instead of a large enterprise rollout.
Human control where it matters
Refunds, external messages and destructive actions can pause for approval of the exact operation.
Stronger gateway option
For higher-risk workflows, credentials can remain server-side so a compromised local agent process cannot use them directly.
Evidence after the event
Decisions, approval history, incidents and tamper-evident audit records help teams investigate what the agent attempted.
Current coverage
What is available today.
- 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 developers also expose the original sensitive tool or credentials.
- Gateway enforcement is recommended for high-risk side effects.
- n8n, MCP and additional language SDKs are not part of the current verified release.
- Quasentra does not currently claim SOC 2, ISO 27001 or another third-party certification.
A low-risk buying path
Prove value on one business workflow.
WEEK 01
Select
Choose one agent with clear tools and a measurable risk.
WEEK 01
Integrate
Install the SDK, register tools and keep new capabilities denied.
WEEK 02
Observe
Review allowed, blocked and approval-required operations.
WEEK 02
Decide
Expand only when the control and operating model fit your team.
Give one production-bound agent a smaller blast radius.
Start free, or contact us to scope a software-house or enterprise pilot.