Your written AI policy,
enforced on every action, not just filed away
A written AI policy is only as good as whatever actually enforces it, and most businesses running several agents have the document without the enforcement. An AI policy and guardrails agent checks every other agent's action against that written policy before it executes, scope limits, rate limits, approval gates, blocking what falls outside and logging every attempt, so the policy governs what actually happens, not just what is supposed to.
The role today
A business running several AI agents usually develops an informal sense of what feels risky, an agent probably should not send money without a check, probably should not message a customer with something unreviewed, but that sense rarely gets written down, and even less often gets built into the agents as an enforced limit rather than a hope.
The second cost is that even where a written policy exists, it tends to live in a document nobody checks before building the next agent, so the policy and the actual behavior of the fleet drift apart over time, with nobody noticing until an agent does something the policy clearly prohibited but nothing actually stopped.
The third is inconsistent review: a new agent’s risk gets assessed, if at all, by whoever built it that day, so a genuinely risky agent can go live with the same light scrutiny as a low-stakes one, simply for lack of a checklist that forces the distinction.
What the agent takes over
This agent checks every other agent’s action against your written policy before that action executes: scope restrictions on what tools or data an agent can reach, rate limits, budget caps, and approval gates on anything the policy marks as sensitive. The policy exists both as a document your team can read and update, and as enforcement the agents cannot simply ignore or drift away from over time.
Every attempt that falls outside an agent’s allowed scope is blocked and logged, not silently dropped, which gives your team a real record of what was tried and a basis for deciding whether an agent’s design needs adjusting, not just whether the gate caught it. New agents go through a review checklist against the policy before launch, so risk gets consistent scrutiny rather than whatever attention the day happened to allow.
Typical scope: any existing or planned agent fleet your team wants brought under one consistently enforced policy. One of our own builds runs eight agents under exactly this kind of governance, with budgets, margins and rate limits enforced as hard rules.
What stays with humans
Deciding what the policy actually says, what counts as risky, and what agents are allowed to do without a human check, is your team’s call; we draft options and flag what we have seen go wrong elsewhere, but the policy reflects your risk tolerance. Reviewing and approving each new agent against the checklist before launch stays a human step, and updating the policy as your use of agents evolves is something your team can do independently.
Guards
Enforcement runs as hard rules in code, rate limits, scope restrictions, approval gates, not as guidance an agent could be prompted around. Every policy violation attempt is logged and alerted, never silently blocked without a trace. The whole setup is tested against each agent’s real behavior in a dry run before going live, and can be tightened or loosened by your team at any time without needing us involved.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Agency runs it | from $2,500 + support plan | Policy drafted and enforcement built by us, monthly governance review | 2 to 3 weeks |
| Full control, handover-ready | from $4,200 | Same enforcement on your own infrastructure, documented policy, your team owns it | 3 to 5 weeks |
Running cost is usually $15 to $50 a month in monitoring and model usage, depending on agent count and action volume.
Related
See the AI agents service page and automation-everything for the surrounding build. Within this group: agent orchestrator, security monitoring agent, and access and permissions agent cover the adjacent governance ground this agent works alongside. For the one-time project version of this same work, see automate AI policy and guardrails setup and automate agent approval queue and audit log. Real governance behind this page: the ProBay AI agent team case study, where a margin guard has caught every single order that would have shipped below cost.
Running AI agents without a policy that is actually enforced? Get in touch and we will map what your agents can do today.
FAQ
How much does an AI policy and guardrails agent cost?
From $2,500 covering enforcement for one to three agents, live in 2 to 3 weeks. A larger agent fleet or a more detailed policy for a regulated industry usually runs $4,000 to $6,000.
How long before it is actually enforcing anything?
2 to 3 weeks: drafting or formalizing the policy with your team takes a few days, then wiring the enforcement into each agent and dry-running it against real behavior takes the rest, before anything blocks live traffic.
Does this work with agents we already have running?
Yes. It is built to sit alongside your existing agents and check their actions against the policy, whether those agents were built by us or by someone else, as long as their actions are observable.
What happens when an agent tries something outside its allowed scope?
The action is blocked and the attempt is logged and alerted, never silently dropped without a trace. That log is also how your team spots a pattern worth addressing at the agent's own design, not just at the gate.
Who owns and can change the policy itself?
Your team does. We help draft it and build the enforcement, but the policy document and the thresholds behind it stay yours to review and update as your use of agents evolves, without needing us involved for every change.