claude-code-sa-workflow

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claude-code-sa-workflow

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Claude Code multi-agent workflow for Solution Architects — 9 specialized AI agents covering discovery, architecture design, IaC, and validation

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# SA Workflow — Claude Code Multi-Agent Orchestration

A production-style **solution architect workflow** built entirely with Claude Code subagents. Three phases, nine specialized AI agents, one human in the loop at every gate.

This repo contains the agent definitions, orchestration logic, and a complete end-to-end example run for a fictional company called **AuroraStream** — a streaming platform migrating to AWS.

---

## What This Is

Manual SA workflows look like this:

```
Discovery meeting prep  →  Architecture design  →  Architecture review
(5 prompt templates)       (6 prompt templates)     (5 prompt templates)
```

This repo replaces each manual prompt with a **specialized Claude Code subagent** that runs autonomously, writes files to disk, and hands off to the next agent — with a human checkpoint at every phase boundary.

```
Phase 1: Discovery    →  Gate 1  →  Phase 2: Design    →  Gate 2  →  Phase 3: Validation
discovery-agent              design-agent                    security-validator
                             diagram-agent                   scalability-validator
                             iac-agent                       cost-validator
                             documentation-agent             red-team-cto
```

The SA never skips a gate. AI does the 70–80% draft. The human adds the 20–30% that matters.

---

## The Agents

| Agent | Phase | What It Does |
|-------|-------|--------------|
| `discovery-agent` | 1 | Company research, stakeholder mapping, discovery questions, post-meeting processing |
| `design-agent` | 2 | Three architecture options with trade-off analysis |
| `diagram-agent` | 2 | Architecture diagram using Python `diagrams` library |
| `iac-agent` | 2 | Modular Terraform — vpc, compute, data, security, observability |
| `documentation-agent` | 2 | ADRs, reference architectures, architecture summary |
| `security-validator` | 3 | PCI DSS / compliance gap analysis |
| `scalability-validator` | 3 | Performance bottleneck and scaling risk analysis |
| `cost-validator` | 3 | Monthly AWS cost estimate with optimization recommendations |
| `red-team-cto` | 3 | CTO-skeptic challenge questions with prepared answers |

---

## Customer Workspace

Each engagement lives in `customers/<company-slug>/`:

```
customers/
  aurorastream/                    ← example run (full output)
    project-context.md             ← living doc, updated at every gate
    00-discovery/
      company-brief.md
      stakeholder-map.md
      questions.md
      meeting-summary.md
      requirements.md
      gap-analysis.md
      follow-up.md
    01-design/
      architecture-options.md
      reference-architectures.md
      aurorastream-architecture.png
      ADR-001-migration-strategy.md
      ADR-002-compute-strategy.md
      ADR-003-database-choice.md
      ADR-004-media-pipeline.md
      ADR-005-cdn-apac-strategy.md
      ADR-006-data-residency-controls.md
      ADR-007-iac-tooling.md
      architecture-summary.md
      terraform/
    02-validation/
      security-report.md
      scalability-report.md
      cost-estimate.md
      cto-qa-prep.md
      validation-summary.md
  _template/                       ← blank workspace for new engagements
```

---

## Requirements

- [Claude Code](https://claude.ai/code) (CLI or desktop app)
- Claude Max or API access with subagent support
- AWS CLI configured (for the pricing MCP server)
- Python `diagrams` library (`pip install diagrams`) for architecture diagrams
- Node.js for `mcp-remote` (AWS knowledge MCP)

### MCP Servers (optional but recommended)

The `.claude/settings.json` includes MCP server configs for:

| Server | Purpose |
|--------|---------|
| `awslabs.core-mcp-server` | Core AWS utilities |
| `awslabs.aws-documentation-mcp-server` | AWS docs lookup |
| `awslabs.aws-pricing-mcp-server` | Live pricing queries |
| `awslabs.cdk-mcp-server` | CDK construct docs |
| `awslabs.aws-diagram-mcp-server` | Diagram generation |
| `awslabs.terraform-mcp-server` | Terraform module docs |
| `aws-knowledge-mcp-server` | AWS knowledge base |

Install with: `pip install awslabs.core-mcp-server awslabs.aws-documentation-mcp-server awslabs.aws-pricing-mcp-server awslabs.cdk-mcp-server awslabs.aws-diagram-mcp-server awslabs.terraform-mcp-server` (requires `uvx`)

---

## Quickstart

1. Clone the repo and open it in Claude Code
2. Say: **"new customer"** or **"start engagement"**
3. The principal agent runs a 6-question intake to populate `project-context.md`
4. Confirm: **"proceed with Phase 1"**
5. Discovery agent runs — presents output at Gate 1A (company brief) and Gate 1B (questions)
6. After the meeting: paste your raw notes → Gate 1C
7. Confirm: **"proceed with Phase 2"**
8. Design, diagram, IaC, and documentation agents run in sequence
9. Gate 2 — review and revise the full package
10. Confirm: **"proceed with Phase 3"**
11. Four validation agents run in parallel
12. Gate 3 — final review, accept or revise

---

## Example: AuroraStream

The `customers/aurorastream/` folder is a complete end-to-end run for a fictional streaming platform:

- **Company:** AuroraStream — Series B, 8M subscribers, APAC expansion, GDPR/data residency constraints
- **Stakeholders:** CTO (cost mandate), VP Engineering (reliability), Head of Data (latency SLAs), CISO (PCI DSS)
- **Architecture decision:** Hybrid ECS Fargate + Aurora PostgreSQL + CloudFront CDK deployment

Key outputs:
- [Architecture diagram](customers/aurorastream/01-design/aurorastream-architecture.png)
- [7 Architecture Decision Records](customers/aurorastream/01-design/)
- [Terraform modules](customers/aurorastream/01-design/terraform/)
- [Security, scalability, cost, and CTO validation reports](customers/aurorastream/02-validation/)

---

## Decision Analysis Framework

Every architectural recommendation in this workflow is evaluated on six axes:

1. **Cost** — initial + ongoing operational
2. **Timeline** — implementation time, learning curve
3. **Team Capability** — current skills, training needs
4. **Leadership Alignment** — risk tolerance, strategic direction, politics
5. **Technical Fit** — scalability, reliability, security, maintainability
6. **Vendor Lock-in** — portability, exit strategy

---

## Philosophy

> *AI gives speed and breadth. The human provides depth, context, and judgment.*

This workflow produces **70–80% complete drafts** — not perfect documents. The SA adds the customer relationship layer: the off-hand comment in the meeting, the political context the TAM shared, the gut feel about what the CISO will actually accept.

The gates are not optional. They are the workflow.

---

## License

MIT — use it, adapt it, build on it.

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