DAD-M is a structured framework for AI-assisted project work. It separates analysis, design, implementation, and validation into four explicit phases: Discover, Apply, Deploy, and Monitor.
The repository is published as a public method repository for people who want a more controlled alternative to ad-hoc prompting.
DAD-M is a reusable operating model for projects that need more than ad-hoc prompting. It is designed around milestones, documented outputs, and repeatable delivery instead of one-off chats.
Many AI-supported tasks start fast but become hard to reproduce once a project spans several steps, files, or stakeholders. DAD-M addresses that by:
- collecting facts before design
- separating planning from execution
- requiring concrete deliverables per phase
- feeding review results back into the next cycle
| Phase | Purpose | Typical outputs |
|---|---|---|
| Discover | Understand the current state and constraints | system overview, file structure, dependencies, risks, open questions |
| Apply | Turn facts into a solution design | architecture, interfaces, data models, pseudocode, acceptance criteria |
| Deploy | Implement the approved design | code, documents, scripts, configuration, proofs |
| Monitor | Validate results and capture follow-up work | test results, error analysis, regression risks, recommended fixes |
More detail: framework/core/phases.md
DAD-M is useful for people who need structure around AI-assisted delivery, especially in:
- software development
- system analysis
- documentation-heavy work
- automation workflows
- project planning with explicit milestones
It is most useful when work needs reviewable outputs, explicit scope boundaries, and milestone-by-milestone control.
- Read the method summary in docs/overview.md.
- Use the setup sequence in framework/core/bootstrap.md.
- Define milestones with framework/core/milestones.md.
- Run the first cycle and record outputs using framework/core/deliverables.md.
- Keep the work inside the rules in governance/guardrails.md.
The repository uses one dominant layout:
docs/for public explanation and onboardingframework/core/for the canonical method documentsframework/templates/for reusable framework templatesgovernance/for operating rules and evidence boundaries
Key pages:
- docs/overview.md: concise method overview
- docs/getting-started.md: step-by-step first use
- docs/use-cases.md: where the framework fits and where it does not
- docs/examples/rbac-case-example.md: public milestone example for a technical access-control design
- docs/variants/education.md: early public variant for validated learning and knowledge-building workflows
- framework/core/phases.md: phase definitions and boundaries
- framework/core/milestones.md: milestone design
- framework/core/deliverables.md: expected outputs and proof format
- framework/core/bootstrap.md: starter prompt structure and setup flow
- governance/guardrails.md: operating boundaries
- governance/quality-principles.md: method quality rules
- governance/evidence-policy.md: claim and publication policy
- Define the project brief and safety boundaries once.
- Break the work into milestones with clear scope and acceptance criteria.
- Run Discover to collect the facts for milestone M1.
- Run Apply to design the solution within those facts.
- Run Deploy to implement only the approved design and capture proofs.
- Run Monitor to validate the result and prepare the next milestone.
For a concrete public example, see docs/examples/rbac-case-example.md.
DAD-M is intentionally strict about workflow boundaries. Discover is for facts, Apply is for design, Deploy is for implementation, and Monitor is for validation. Dependency changes, network use, and work outside the allowed scope should stay explicitly controlled.
This repository documents DAD-M as a public framework repository. The current focus is a clear method overview, practical starting guidance, and conservative governance notes.
A DAD-M Education variant is also being documented as an early public extension for validated learning and knowledge-building workflows. A public summary for that variant is available in docs/variants/education.md.
This repository does not claim formal approval, organization-wide adoption, or enterprise validation.
I curated this Repository in a wyay that hopefully helps with structuring your projects and uses the key advantages of agentoc systems while keeping responsibility firmly in the hands of human operators.