Three open-source AI agent governance patterns from OutTheBox.ai
0
1 commits
updated Sep 19, 2026
Three working AI agent governance patterns, free and open source: a decision table, a worked example, and a Microsoft Agent 365 template for each. No sign-up, no trial, no cut-down version, the same content as the paid library, just fewer patterns.
Most AI agent governance content today is either infrastructure (identity, sandboxing, policy enforcement, the plumbing that runs an agent safely) or documentation (a PDF someone has to read and remember). These three patterns are neither. They're executable decision logic for the questions the plumbing doesn't answer:
If you're running agent infrastructure with runtime enforcement already in place, these patterns sit above that layer: they're the governance decisions your enforcement tooling needs a rule to check against, not a replacement for it.
pattern.md — the full pattern: trigger condition, decision logic,
provenance, and how it relates to neighbouring patterns.decision-table.dmn — the same logic as an executable DMN decision
table, tested against every input combination.agent365-template.json — a native Microsoft Agent 365 governance
template.summary.md — a one-page summary of the problem and what the
pattern does.This is 3 patterns out of 92, covering agent identity, cost, and sprawl governance in general. The full library adds 20 packs across lifecycle, pre-deployment, oversight, multi-agent coordination, drift, data governance, change management, and three regulated verticals (employment, worker monitoring, education), all cross-referenced against each other. See outthebox.ai if that's useful to you.
MIT. Use it, fork it, adapt it, ship it. See LICENSE.
This repo is provided as-is. No support commitment, issues and PRs are welcome but may not get a fast response, this is a solo project.
1 commits
Three open-source AI agent governance patterns from OutTheBox.ai
0
1 commits
updated Sep 19, 2026
Three working AI agent governance patterns, free and open source: a decision table, a worked example, and a Microsoft Agent 365 template for each. No sign-up, no trial, no cut-down version, the same content as the paid library, just fewer patterns.
Most AI agent governance content today is either infrastructure (identity, sandboxing, policy enforcement, the plumbing that runs an agent safely) or documentation (a PDF someone has to read and remember). These three patterns are neither. They're executable decision logic for the questions the plumbing doesn't answer:
If you're running agent infrastructure with runtime enforcement already in place, these patterns sit above that layer: they're the governance decisions your enforcement tooling needs a rule to check against, not a replacement for it.
pattern.md — the full pattern: trigger condition, decision logic,
provenance, and how it relates to neighbouring patterns.decision-table.dmn — the same logic as an executable DMN decision
table, tested against every input combination.agent365-template.json — a native Microsoft Agent 365 governance
template.summary.md — a one-page summary of the problem and what the
pattern does.This is 3 patterns out of 92, covering agent identity, cost, and sprawl governance in general. The full library adds 20 packs across lifecycle, pre-deployment, oversight, multi-agent coordination, drift, data governance, change management, and three regulated verticals (employment, worker monitoring, education), all cross-referenced against each other. See outthebox.ai if that's useful to you.
MIT. Use it, fork it, adapt it, ship it. See LICENSE.
This repo is provided as-is. No support commitment, issues and PRs are welcome but may not get a fast response, this is a solo project.
1 commits