Own the definitional queries teams ask before they turn a company policy into something an agent actually enforces.
Definitional · FAQPage
What is policy-as-code? Turning company rules into agent-enforced checks
Target query: what is policy as code
Policy-as-code is the practice of expressing a company policy as machine-checkable rules an agent can evaluate at runtime — not a PDF someone reads once. For an agent that can approve a refund, email a customer, or move data, policy-as-code turns "refunds over $50 need manager approval" into a check that actually fires before the action. It is decision-support that makes the policy executable; it is not a legal opinion or a compliance certificate.
refs: https://artificialintelligenceact.eu/ · https://eur-lex.europa.eu/eli/reg/2024/1689/oj · https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
How-to · HowTo
Turn company policy into agent-enforced rules: a practical framework
Target query: turn company policy into agent rules
Start by extracting each policy sentence as a condition + action: "if refund > $50 then require manager_approval". Then map the highest-risk rules to the EU AI Act obligations they touch (Art. 9 risk management, Art. 14 human oversight, Art. 26 deployer responsibility). The result is an exportable rules file an agent evaluates on every run. The rules are a view of your obligations, not a guarantee of conformity.
refs: https://artificialintelligenceact.eu/ · https://eur-lex.europa.eu/eli/reg/2024/1689/oj
Definitional + examples
Mapping policy checks to EU AI Act obligations
Target query: eu ai act policy mapping obligations
When an agent operates in an Annex III high-risk use, the deployer carries duties like risk management (Art. 9), data governance (Art. 10), human oversight (Art. 14), and accuracy/robustness (Art. 15). Policy checks that gate high-risk actions help you evidence those controls. Conformity, however, stays the deployer's responsibility across the full system — a rules file is one input, not the certificate.
refs: https://artificialintelligenceact.eu/ · https://eur-lex.europa.eu/eli/reg/2024/1689/oj · https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
How-to · HowTo
Agent policy compliance checklist
Target query: agent policy compliance checklist
Before an agent touches production: inventory the policies it must honor; convert each to a condition+action rule; wire the high-risk ones to human approval; log every policy decision; and map the top rules to the EU AI Act articles they support. Re-run the checklist after any prompt or tool change. The checklist is a control inventory, not a pass/fail certification.
refs: https://artificialintelligenceact.eu/ · https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
How-to · HowTo
Get started with AgentPolicy in minutes
Target query: get started agentpolicy policy as code
Paste your policy text, pick a domain (Finance / HR / Safety / General), and AgentPolicy returns agent-callable policy checks plus a compliance checklist mapped to EU AI Act obligations. Export the rules and wire them into your agent's runtime. It is the fastest path from "we have a policy" to "the agent can't silently break it."
refs: https://artificialintelligenceact.eu/
Long-form, cited explainers. Each carries 3+ authoritative EU AI Act / AI-governance sources and a decision-support disclaimer.
A pre-production checklist for agent policy enforcement — inventory, rules, human approval, logging, and EU AI Act mapping — plus an honest note on what a checklist does and does not certify.
Three trends shaping how teams enforce policy on agents in 2026 — runtime enforcement, evidence-as-default, and obligations shifting to the deployer under the EU AI Act.
Most policy failures in agent systems are not malice — they are silent overrides, missing evidence trails, and drift after a prompt change. Here are the gaps that appear when policy never becomes an enforced rule.
A practical map from everyday agent policy rules — approval gates, data residency, logging — to the EU AI Act articles they help evidence, and an honest note on what mapping does and does not prove.
A short walkthrough of turning a company policy into agent-enforced rules with AgentPolicy — paste, pick a domain, export, and wire into your agent's runtime.
Manual review catches intent; policy-as-code catches runtime. They are not competitors — they cover different failure modes. Here is when each earns its place, and why agents need both.
A four-step framework to convert policy prose into condition+action rules an agent evaluates on every run, with the highest-risk rules mapped to EU AI Act obligations.
Policy-as-code expresses a company policy as machine-checkable rules an agent evaluates before it acts — so a refund cap or a data-residency rule actually fires, instead of living in a PDF nobody re-reads.
Publish + syndicate per gtm-launch (IH + GEO indexes). Each post carries 3 authoritative refs.