AI-Enabled Configuration Management Specification

Workflow

Producing an AI-enabled CM specification follows this sequence:

1. Scope the system — Identify operational context, enterprise constraints, AI maturity level
2. Define the governance model — Authority structure, RACI/RASIC, escalation paths
3. Specify functional requirements — See functional-requirements.md
4. Define AI agent architecture — See ai-agents.md
5. Map control surfaces and baselines — See control-surfaces.md
6. Design drift detection framework — See drift-detection.md
7. Establish metrics and health indicators — See metrics.md
8. Compose the deliverable — See deliverable-structure.md for required sections and format

Core Principles

Treat context as a controlled architectural variable, not an ambient condition.

• AI augments human authority; it does not replace it
• Entropy is reduced through semantic baselining, reconciliation protocols, and gated progression
• Progression halts when semantic integrity is violated
• Operational mechanisms, not philosophical descriptions

Operating Constraints

The CM system addresses environments characterised by:

• Probabilistic AI behaviour with non-deterministic outputs
• Evolving prompts, model versions, and training data
• Shifting domain definitions under commercial pressure
• Artefact proliferation across lifecycle stages
• Multiple stakeholder interpretation layers

Explicit threats the specification must counter:

Deliverable Structure

The specification output must contain these sections (see deliverable-structure.md for full detail):

1. Executive Overview
2. System Architecture (textual description)
3. Formal Requirements (numbered, FR-xxx / NFR-xxx)
4. Workflow Narratives
5. Data Model Schema (conceptual)
6. Governance Matrix (RACI/RASIC)
7. Risk Register
8. Implementation Phases

Use formal systems engineering language. Avoid generic project management phrasing. Align terminology with configuration control, semantic governance, and systems architecture.

ZDP Integration (Optional)

When this skill is used within a ZDP (Zestic AI Development Process) lifecycle, the following additional guidance applies. This section can be ignored for standalone usage.

ZDP Context

AI-enabled CM specification maps to the ZDP Design stage as a governance artefact produced alongside the Architecture Document. The CM specification’s lifecycle stages align directly with ZDP’s 6D model (Discovery through Drive). CM gate definitions (FR-C01-C05) should reference ZDP gate types (PFA, LCO, LCA, IOC, FOC, CLR) when producing specifications for ZDP-governed programs.

Additional Guidance

When producing CM specifications within a ZDP lifecycle:

• Map CM control surface baselines (control-surfaces.md) to ZDP stage-gate boundaries
• Reference ZDP artefact types in the artefact classification schema (FR-B01): PVVH, business scenarios, domain model, design brief, prompt specs align to control surfaces 1-10
• Integrate epistemic status classification from perspective-investigation into gate readiness assessments (FR-C01-C04): gate checklist items should carry Known/Sufficient, Partially Known, Contested, Underdetermined, or Out-of-Scope status
• Reference the Responsible AI Risk Register (from /responsible-ai) as input to Risk Register section (Section 7 of deliverable)
• Align drift monitoring categories (drift-detection.md) with ZDP Drive stage monitoring requirements

Cross-References

If available, coordinate outputs with:

• /architecture — CM specification complements the Architecture Document
• /responsible-ai — AI-specific risks feed into CM risk register
• /perspective-investigation — epistemic status classification for gate items
• /mlops-monitoring — operational drift monitoring complements CM drift specification
• /requirements-traceability — CM traceability requirements (FR-H) align with traceability matrix production

Reference Navigation

Load only the reference files relevant to the current task. For a full specification, read all. For targeted work (e.g., only drift detection), read only the applicable file.