An open-source operating model for running work through a governed repository. Humans decide. Agents execute and evaluate. You can start with Git first and add agents later.
Open-source operating model: the repo is the governance backbone, this page is the demo, and the interactive animation is the reference scenario. Bring your own runtime, observability platform, and domain systems. Public proof below is aggregated across `agentic-enterprise`, `sandboxcorp`, and `agent-command-center` as of 2026-03-16. Discuss on LinkedIn ↗
Agentic Enterprise is the open-source governance layer for running a company with agents — roles, loops, policies, repo conventions, and integration patterns.
Bring your own runtime, observability stack, and toolchain. This repo defines how they fit together. It is not a single closed control plane.
The demo shows the operating model in motion through a representative reference scenario. It demonstrates the pattern. The pattern itself — not one private implementation — is the public story.
The concept gets stronger when the hard objections are answered directly instead of hand-waved away.
This repo governs decisions, approvals, policies, and traceability. It does not replace ERP, CRM, ITSM, HRIS, or observability systems.
The model is not asking a small company to create five management tiers. A lean team can collapse multiple layers into the same people and still use the framework coherently.
The framework shows active public proof, but it does not yet claim Fortune 500 operating scale. Treat the three-repo reference stack as evidence of repeatability, not as a universal benchmark.
The percentages describe controls modeled in the framework. They do not prove deployed controls, operating effectiveness, or auditor sign-off in your environment.
The bottleneck is no longer execution capacity. It is governance, coordination, and trust.
Ticket boards and status rituals can become bottlenecks when humans and agents share the same workflow. The issue is not tickets existing; it is fragmented governance and weak traceability.
Wikis, runbooks, and meeting notes often drift away from the changes they describe. The framework brings instructions and governed artifacts closer to the work that changes the system.
When AI increases execution speed, governance has to scale too. That means automated quality gates, narrow human approvals, and explicit evidence instead of ad hoc meetings.
Six capabilities make the operating model real.
Signals become missions, missions become governed work, and observability feeds the next loop.
Signals flow in from production, customers, and market. Agents triage. Humans decide Go/No-Go. A mission brief is born.
Hours – DaysOrchestration assembles a crew. Agent fleets execute. Quality evaluates continuously. Humans step in at approval points and true exceptions.
Days – WeeksRelease contracts are cut. Progressive rollout begins. Metrics confirm outcomes. One human Go/No-Go for GA.
Days24/7 production health. Agents emit telemetry; observability surfaces anomalies and files signals back into the repo. The loop never stops.
ContinuousAll work, decisions, and policies map back to one versioned governance backbone. Two coordination channels: the repo and the observability platform.
| Traditional Approach | Agentic Enterprise | What changes |
|---|---|---|
| Ticket system | Work artifacts in work/ or issue tracker | Git files or native issues; configurable per team |
| Ticket workflow | PR merge or comment-based approval | Humans comment, agents handle the rest; Git stays system of record |
| Sprint planning | Mission brief creation | Goal-oriented, not time-boxed |
| Daily standup | Git log + observability dashboards | Always current, real-time fleet health |
| Enterprise wiki | The repository itself | Single source of truth, versioned |
| RACI matrix | CODEOWNERS | Executable, enforced by Git host |
| Phase gates | CI/CD pipeline checks | Automated, no human bottleneck |
| Manual monitoring | Observability integration | Telemetry feeds signals back into governance |
| Story points | Fleet throughput metrics | Measured from telemetry, not guessed |
| Siloed tool configs | Integration Registry | Governed, auditable, agent-accessible |
| Annual reorg | Evolution proposals via PR | Continuous, transparent, reversible |
A structurally complete framework: org structure, processes, agent instructions, quality policies, and work artifacts. All Markdown and YAML.
org/5-layer org structure. Agent instructions per layer. Division charters for every team.
process/4-loop lifecycle. Step-by-step guides for Discover, Build, Ship, Operate.
work/Active work artifacts. Signals, missions, decisions, releases, retrospectives.
work/missions/Strategy's atomic unit. Carries an outcome contract, a defined crew, a fleet config, and a time-bounded scope.
org/4-quality/policies/Machine-readable quality policies across nineteen domains — including security, privacy, observability, incident response, availability, continuity, cryptography, and risk management.
org/agents/Agent Type Registry. Governed definitions for every agent role — capabilities, boundaries, and lifecycle states.
org/integrations/Integration Registry. Governed connections to observability, ITSM, CI/CD, business systems.
CONFIG.yamlSingle config file. Fill in your company name, mission, products. Done.
As of 2026-03-16, these are the directly inspectable public signals across wlfghdr/agentic-enterprise, WulfAI/sandboxcorp, and wlfghdr/agent-command-center. The goal is credibility, not vanity metrics.
Roadmap, compliance, framework, and operating work tracked publicly across the three linked repositories.
Governed change history with review and merge records that readers can inspect directly across the full reference stack.
An auditable Git trail on the three `main` branches showing the stack is actively exercised, not just described.
Quality gates across architecture, security, privacy, observability, delivery, continuity, and more.
Start with Git, CODEOWNERS, signals, missions, and PRs. Add a runtime and observability platform when the core operating model already makes sense for your team.
Clone the open-source repo. Everything is Markdown and YAML — no runtime dependencies, no build step.
Set your company name, repo basics, and review boundaries. That gives the operating model a real governance spine immediately.
File a signal, convert it to a mission, and ship the work through a PR. That is the minimal operating loop, and it works without agents.
Layer in observability, more quality policies, and integrations once the Git-first workflow is already proving itself.
When you are ready, point agents at AGENTS.md. The runtime becomes an execution layer on top of a workflow your team already understands.
Built-in governance controls map directly to enterprise certification frameworks. Percentages reflect editorial self-assessments of controls currently modeled in the repository — not aspirational targets, not certification claims, and not machine-verified audit numbers.
Honest assessment, not marketing claims. These percentages are editorial posture markers for the template, not certification status and not outputs of the stricter coverage validator. Where the remaining work is adopter-owned, the template increasingly ships implementation guides and templates instead of pretending the runtime evidence already exists. Formal certification still requires an independent audit of your deployed instance, configured controls, and real operating evidence.
Open source. No vendor lock-in. Start with minimal adoption, then add runtime and observability depth when the workflow already proves its value.
Running this with OpenClaw? → OpenClaw setup guide ↗
This page is the demo. The embedded visualization defaults to the real examples/e2e-loop/ chain from the repo, then lets you explore other representative reference scenarios.