Agentic SDLC: Enterprise Framework Design & Pilot

3 Steps to an AI-Ready SDLC Framework

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Step 1: Enterprise Review

4–5 weeks

• SDLC Process Mapping: Map end-to-end processes across specifications, delivery, and operations — including tool chain and platform readiness for AI framework hosting.
• AI Usage Audit: Catalogue where and how AI is used across the organisation — tools, process steps, documented vs. improvised usage.
• Maturity Assessment: Assess AI-native maturity per unit against a five-level model, from ad-hoc prompting to governed AI platform.
• Primitives Inventory: Catalogue existing reusable AI configurations — instructions, agents, skills, workflows, hooks, MCP servers — and identify duplication and gaps.
• CoE Readiness: Evaluate whether a Centre of Excellence function exists, its mandate, staffing, and authority to set standards.
• Gap Analysis: Identify gaps between current state and governed enterprise framework requirements.

Step 2: Framework Design

3–4 weeks

• AI Primitives Registry: Architect a governed collection of reusable AI configurations — agents, instructions, skills, workflows, hooks, spec templates — with contribution, review, versioning, and retirement workflows.
• MCP Registry: Design a centralised catalogue of approved MCP servers with approval workflows, trust classifications, access controls, and usage monitoring.
• Governance & Security Model: Establish artefact accountability, prompt hygiene rules, content security scanning, and primitive supply chain security with dependency resolution and version pinning.
• Target SDLC Processes: Redesign lifecycle processes so AI actively drives planning, decomposition, and execution while humans validate at defined decision points.
• CoE Operating Model: Specify mandate, structure, contribution model, and evolution cadence for the framework governance unit.

Step 3: Pilot Implementation

8–12 weeks

• Deploy Registries: Stand up operational Primitives and MCP Registries scoped to the pilot team but structured for enterprise extension.
• Execute PoC: Run a bounded backlog item end-to-end using the framework — success criterion: the team repeats the process independently.
• Establish CoE Seed Team: Activate the CoE operating model, run the first primitive contribution cycle, and conduct the first MCP server approval.
• Enablement Programme: Deliver practice-based enablement using real pilot artefacts — teams learn by executing, not from documentation, with trained internal facilitators.
• Scale-Out Blueprint: Document repeatable onboarding model including champion activation — pilot engineers onboard a non-pilot team before engagement ends.

Quick Facts

• Duration: 15–21 weeks — Review → Design → Pilot
• Tool-agnostic: anchored to open standards, ensuring portability across AI coding agents
• Advisory model: your teams own the outcomes
• Produces operational frameworks, not just strategy documents

‍Benefits

• Prompt supply chain security. Primitives scanned before deployment; lock files pin exact versions.
• Clear accountability. AI contributions tagged and auditable; agents operate within explicit boundaries.
• Governed at scale. Enterprise-wide framework with managed context, composable primitives, and defined agent boundaries.
• Dependency-managed configurations. One manifest per project — fully configured, version pinned agent setup.
• Measurable maturity. Concrete metrics: reproducible setup, registry adoption, team independence.
• Independence by design. Trained champions and peer-led enablement — your team operates independently before we leave.