Every AI agent decision, within the lines you draw
Prisma observes the decisions AI agents make inside your workflows and holds them to your policy boundaries with privileges, restrictions, consequences and feedback at runtime. It feeds consequences and feedback back into the agent so the performance of long-running agents improves and stabilizes, with the evidence kept.
Discover the Prisma platformAt a glance
- Department
- Enterprise architecture and AI programs
- Regulatory context
- AI governance frameworks and rules such as the NIST AI Risk Management Framework, ISO/IEC 42001, the EU AI Act (human oversight and monitoring of AI in operation) and model risk management guidance
- What is governed
- The decisions and behavior of AI agents and AI-assisted workflows in production: their actions, their use of data and tools, and their communications, measured against organizational policy
- What Prisma delivers
- Observation of agent decisions against policy, privileges, restrictions, consequences and feedback applied at runtime, consequences and feedback fed back to improve and stabilize long-running agents, and an audit trail of every judgment
Governing decisions, not just performance
Most measurement of AI asks whether an agent is accurate, fast or useful. Decision governance asks a different question: is the agent making its decisions within the boundaries the organization has set? An agent can be accurate and still decide something outside its authority, use data it should not touch, say something it should not say, or skip a step that policy requires.
Decision governance for AI agents, often called AI agent governance, is the practice of observing the decisions agents make and the actions they take against organizational policy, and keeping them within those bounds as autonomy grows. It is not about whether the model is good. It is about whether the agent, in a real workflow, decides and acts within what the organization allows and nothing beyond it.
Any workflow an agent touches
Agents are no longer confined to one corner of the business. They answer customers, prepare onboarding files, triage alerts, draft letters, reconcile payments, summarize cases and prepare reports. Each of those workflows has its own policy, and each agent acts inside it with some degree of independence.
Governance therefore has to follow the agent, not the workflow. A single layer that observes behavior against policy across every workflow means that a new agent, or a new workflow, does not need a new way of keeping it in bounds.
What policy boundaries look like for an agent
For an agent, the boundaries are the same kinds of limits that apply to any person doing the work. They define the scope of what the agent may decide or do, the data and tools it may use, what it may say and to whom, which steps it must always take, and when it must stop and hand the case to a person.
Some cases fall in between. Policy does not always give a clear answer, and an agent can find itself in a situation nobody anticipated. Governance has to recognize that difference and treat an unclear case as one for human judgment, not as a case where the agent should decide on its own.
Observing and constraining at runtime
Prisma observes the decisions and behavior of agents against your policy, detecting violations and ambiguity, with the reasoning behind each finding. A violation records where behavior went outside bounds. Where the situation is not clearly covered, Prisma flags the ambiguity and escalates it to a human instead of leaving the agent to guess.
Intervention happens while the agent works. Prisma applies privileges, restrictions, consequences and feedback at runtime through Carrots'n'Sticks, so that agents stay within bounds, and the evidence of every judgment is kept.
Improving and stabilizing long-running agents
Agents do not run once and stop. They run for hours, days or weeks, carry context forward, work with changing data and tools, and meet situations their instructions never covered. Over a long run, behavior can drift: small departures from policy that repeat, errors that compound, an agent that was well behaved at the start and is less so later. Testing before deployment and fixed guardrails cannot keep that in check on their own.
Remediation is what closes the loop. Consequences and feedback from what Prisma observes are fed back into agent behavior, so that each judgment improves the next one, drift is corrected, and the agent settles into behavior that stays within bounds. Consequences and feedback improve and stabilize performance over time, which is what lets autonomy grow without governance holding it back.
One standard for people and for AI
The policies an organization already has were written for people. Prisma applies a single governance standard to work done by people and by AI, so an agent is held to the same policy as the work it took over, and a workflow that mixes both is governed as one.
That is what lets autonomy scale. When the behavior of agents is observed, bounded and recorded, more autonomy can be given with more trust, and governance does not have to be the thing that holds it back.
Don’t just take our word for it
Frequently asked questions
AI agents decision governance, also called AI agent governance, is the practice of governing the decisions AI agents make and the actions they take in real workflows, and keeping them within organizational policy boundaries. It covers what an agent decides and does, which data and tools it uses, what it says, and when it hands a case to a person, as autonomy increases.
Performance monitoring asks whether an agent is accurate, fast or useful. Decision governance asks whether the agent is deciding and acting within the boundaries the organization has set. An agent can perform well and still act outside its authority, so the two answer different questions and are both needed.
They are the limits within which an agent is allowed to act: the scope of what it may decide or do, the data and tools it may use, what it may say and to whom, the steps it must always take, and the situations in which it must stop and escalate to a human.
An agent that runs for hours, days or weeks carries context forward, works with changing data and tools, and meets situations its instructions did not cover. Its behavior can drift over time, with small departures from policy that repeat or compound. Governance observes the agent while it runs and keeps its decisions within policy, which testing before deployment cannot do on its own.
Through remediation. Consequences and feedback from what Prisma observes are fed back into agent behavior, so each judgment improves the next one, drift is corrected, and performance improves and stabilizes within policy bounds. That is how autonomy can grow without governance holding it back.
Any workflow that is executed or assisted by AI agents. That includes customer conversations, onboarding, investigations, payments and reporting, among others. Prisma applies one governance layer across them, so each new agent or workflow is held to policy in the same way.
Excessive agency is the risk that an AI system is given more functionality, permissions or autonomy than the task needs, so that it can take actions with harmful consequences. It is one of the risks named in the OWASP guidance for applications built on large language models. Governing behavior within policy boundaries is one way to contain it.
Frameworks and rules such as the NIST AI Risk Management Framework, ISO/IEC 42001 and the EU AI Act expect organizations to define accountability, keep humans able to oversee AI, and monitor AI systems while they operate. Prisma provides the runtime observation of behavior against your own policy, and the evidence that supports those expectations.
Carrots'n'Sticks is the way Prisma shapes agent behavior at runtime. Privileges and positive feedback reward behavior within bounds, and restrictions, consequences and corrective feedback apply when an agent steps outside them.
Prisma evaluates the decisions and behavior of agents against your policy and the rules that apply. A violation is recorded with the reason it breaks policy. Where the situation is not clearly covered by policy, Prisma flags the ambiguity and escalates it to a human instead of leaving the agent to guess.
No. Your agent frameworks, guardrails and security tools keep doing their work. Prisma adds oversight of behavior against your organizational policy, applies privileges, restrictions, consequences and feedback at runtime, and keeps the evidence.
Prisma is delivered as an SDK that runs inside your own environment. It can also be delivered through Palqee's managed cloud and interface if you need it.
