Agent Teams

Talent Bench

15AI Agents

15 AI agents available instantly — 3 decision-makers (opus), 8 specialists (sonnet), 4 operators (haiku). No hiring pipeline. No ramp time.

⚡ 15 agents available in <5 minutes via npx install

AI agents build governed & Humans ship trusted. 80% autonomy & 100% accountability.

Section Two (Ch.8-12)

Building Your Talent Bench

“No company can outsource its way to digital excellence. Being digital means having your own bench of digital talent — product owners, experience designers, data engineers, data scientists, software developers, etc.”

The goal should be to have 70-80% of your digital talent be in-house. ADLC addresses this with 15 AI agents available instantly — no hiring pipeline, no lead time. Decision Layer (opus): product-owner, cloud-architect, security-compliance-engineer. Execution Layer (sonnet): 8 specialist engineers. Operations Tier (haiku): 4 SRE/QA specialists.

Source: Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Lamarre, Smaje, Zemmel, 2023)

Platform Evolution

Agent frontmatter is model-version-agnostic. Upgrade Claude Opus/Sonnet/Haiku — agents get smarter automatically. No code changes needed.

Component Map

15 components implementing this pillar

Type	Name	Why	Business Value
Agent	`product-owner (opus)`	Business validation and sprint governance — ALWAYS FIRST	Prevents misaligned delivery; enforces business-led roadmap
Agent	`cloud-architect (opus)`	Technical design and deployment strategy — ALWAYS SECOND	Architecture approval before any specialist execution
Agent	`security-compliance-engineer (opus)`	SOC2, APRA CPS 234, ISO 27001 compliance enforcement	Regulatory risk eliminated at design time
Agent	`python-engineer (sonnet)`	CloudOps Runbooks PyPI package, CLI commands, boto3	Production-grade Python with TDD and battle tests
Agent	`infrastructure-engineer (sonnet)`	CDK + Terraform IaC for AWS multi-account landing zones	Reproducible, auditable infrastructure as code
Agent	`kubernetes-engineer (sonnet)`	K3s, ArgoCD, Helm chart lifecycle management	GitOps delivery pipeline for containerised workloads
Agent	`qa-engineer (sonnet)`	3-tier test strategy: snapshot / LocalStack / AWS	90-100% bug detection before production
Agent	`observability-engineer (sonnet)`	DORA metrics, daily standup, sprint ceremonies	Data-driven velocity and quality measurement
Agent	`frontend-docs-engineer (sonnet)`	Docusaurus docs, React components, marketplace UI	Terminal-inspired, WCAG 2.1 AA compliant interfaces
Agent	`devops-security-engineer (sonnet)`	CI/CD pipelines, supply chain hardening, SBOM	SLSA Level 2+ provenance for every release
Agent	`sre-automation-specialist (haiku)`	Incident response, runbook execution, on-call automation	MTTR reduction through codified operational procedures
Agent	`gitops-cost-optimizer (haiku)`	FinOps cost attribution, rightsizing, waste detection	Autonomous cost reduction within READONLY guardrails
Agent	`qa-testing-specialist (haiku)`	Playwright E2E, BDD step definitions, smoke tests	Regression prevention at every sprint boundary
Agent	`technical-documentation-engineer (haiku)`	CLI docs sync, ADR generation, hand-curated content protection	Docs always match code — HAND_CURATED_CONTENT_DESTRUCTION prevented
Agent	`meta-engineering-expert (sonnet)`	Framework architecture, agent scoring, PDCA facilitation	Continuous improvement of the ADLC framework itself

Risk & Scalability

What happens without this pillar, and why ADLC scales from 1 person to enterprise

What if you skip?

McKinsey: “No company can outsource its way to digital excellence” (Rewired S2, p.69). Companies that rely on external contractors lack business context — “the technologists gain precious understanding of the business context… Context matters to developing great digital solutions” (p.72). Without an in-house bench, you are perpetually dependent.

Scalability

ADLC provides the full Digital Factory pod pattern with AI agents: 3 decision-layer agents (opus), 8 execution-layer specialists (sonnet), 4 operations-tier workers (haiku). Agent frontmatter is model-agnostic — upgrade the underlying model, agents improve automatically.