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 FeedbackHub in minutes
Target query: get started feedback-hub policy as code
Paste your policy text, pick a domain (Finance / HR / Safety / General), and FeedbackHub 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.
The recurring errors founders make when clustering feedback: mixing themes, ignoring frequency, over-counting duplicates, and treating the top theme as a mandate. How to avoid each.
A comparison of ways to cluster user feedback — manual spreadsheets, general AI chat prompts, and a purpose-built tool like FeedbackHub — covering setup effort, theme consistency, and where each fits a solo founder's workflow.
An honest look at FeedbackHub alternatives for clustering user feedback — spreadsheets, general AI chat, and full feedback-management platforms — and the trade-offs a solo founder should weigh before adopting one.
Straight answers to common FeedbackHub questions: what it does, how clustering works, Free vs Pro pricing, whether it replaces a product manager, and how to read the build-next suggestion.
A practical guide to using FeedbackHub: paste feedback lines, optionally name the product, and get theme buckets, a frequency count, and a suggested build-next pick. Covers Free vs Pro and how to read the output.
Where FeedbackHub earns its keep: solo founders triaging launch feedback, indie makers prioritizing features, and small teams turning a messy thread into a short theme list with a build-next suggestion.
A practical workflow for clustering user feedback by theme: collect raw lines from Slack, DMs, and surveys; group them into Pricing, Bugs, UI/UX, Performance, and Other; count frequency; and pick a build-next theme — without maintaining a spreadsheet.
Feedback clustering groups raw user comments into repeating themes — Pricing, Bugs, UI/UX, Performance, Other — with a frequency count, so a founder can see what users actually keep saying instead of re-reading a messy thread.
Publish + syndicate per gtm-launch (IH + GEO indexes). Each post carries 3 authoritative refs.