Control how your agents behave at runtime
AGENT BEHAVIOR RUNTIME CONTROL
Noma governs and monitors agent behavior at runtime, so agents can safely access sensitive systems and stay productive.
Acme Inc.
Industry Co
Robust AI Security
Security and Governance
AI and Agents
Industry Co
RealBusiness
Acme Inc.
Industry Co
Robust AI Security
Security and Governance
AI and Agents
Industry Co
RealBusiness
How Noma AI agent runtime security works
Define what agents are allowed to do
Define which tools, skills, and MCPs each agent can use, scoped by user identity or tool sensitivity. Policies are enforced in real time, and are enforced everywhere using your existing infrastructure.
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Context-aware enforcement
Legitimate actions can combine into dangerous behavior. Noma correlates multiple agentic signals like tool calls and responses, connections, identity, and behavior over time to make accurate policy enforcement decisions.
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Draw on the experience of the world's best security teams
Use hundreds of out-of-the-box policies, benchmarks, and protection profiles, tuned by industry, agent function, and agent type, based on experience working with dozens Fortune 500 customers.
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Broad coverage across AI risks
Noma protects against adversarial threats as well as rogue agents making mistakes, across endpoint, SaaS, and homegrown agents. Risky actions can be blocked, steered, or alert a human, while sensitive data is masked inline.
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Every AI surface, same enforcement
Enterprise ready
Comprehensive coverage
Integrations built into 80+ data, AI and MLOps platforms, cloud, no-code and low-code agents, and source code management.
Open Enforcement
Decouple governance from any single control point and enforce policy through AI gateways, MCP gateways, agent hooks, agent SDKs, and direct APIs, using the infrastructure already in your environment.
Multiple deployment options
Support for both on-prem and SaaS deployments, so no model, training data, or security events leave your environment.
Built to Higher Standards.
FAQs
What is agent behavior runtime control?
Agent behavior runtime control is governing and monitoring what AI agents do while they run: which tools they invoke, what data they touch, and whether their actions match intent, with enforcement at the moment an agent acts.
Why isn't a single agent action enough to judge risk?
Because individually reasonable actions combine into dangerous behavior. Untrusted input, sensitive data access, and an external send can each be fine alone and together form the lethal trifecta. Noma evaluates the full session.
Does runtime control slow agents down?
No. Policies are enforced inline at existing control points, and context-driven detection keeps false positives low, so legitimate agent work continues without friction.
Which environments does runtime control cover?
All three: homegrown agents behind AI gateways, SaaS agents through platform integrations, and endpoint agents through native hooks inside tools like Claude Code and Cursor. One set of policies, enforced consistently.
