# SEF Agents: Marketing Storyboard

**Target Audience:** Business Leaders, Technical Leaders, Product Managers
**Format:** 10-Slide Narrative Arc
**Goal:** Convey value proposition across stakeholder types

---

## Storyboard Structure

| Act | Purpose | Slides |
|-----|---------|--------|
| 1. The Problem | Create tension | 1-3 |
| 2. The Solution | Introduce SEF | 4-6 |
| 3. The Proof | Demonstrate value | 7-9 |
| 4. The Ask | Call to action | 10 |

---

## Act 1: The Problem (Slides 1-3)

### Slide 1: "The AI Coding Paradox"

**Hook:** AI assistants promised 10x productivity. Reality is different.

| What They Said | What Happened |
|----------------|---------------|
| "Ship faster" | More code, same bugs |
| "Higher quality" | AI-generated tech debt |
| "Reduce costs" | Rework cycles increased |

**Visual:** Graph showing AI adoption vs. defect escape rate (correlation, not causation)

**Stakeholder Resonance:**
- **Business:** ROI on AI tools unclear
- **Technical:** Fighting AI-generated code smells
- **Product:** Features ship, but quality slips

---

### Slide 2: "The Missing Layer"

**Key Insight:** AI assistants are typing accelerators, not engineering partners.

| AI Assistants | What's Missing |
|---------------|----------------|
| Generate code | No quality enforcement |
| Answer questions | No workflow context |
| Autocomplete | No pattern learning |
| Session-based | No persistent memory |

**Analogy:** "Cursor/Copilot are skilled typists. But who manages the project?"

**Stakeholder Resonance:**
- **Business:** No visibility into AI-assisted work
- **Technical:** Standards not enforced
- **Product:** AC validation happens after, not before

---

### Slide 3: "The Real Costs"

**Quantified Pain:**

| Metric | Industry Average |
|--------|------------------|
| Rework rate | 30% of dev time |
| Defect escape to prod | 15% of features |
| Knowledge loss (turnover) | 3-6 months ramp-up |
| Review cycles | 2.5 iterations avg |

**Statement:** "Your AI assistant is fast. But fast in the wrong direction is expensive."

**Stakeholder Resonance:**
- **Business:** Cost of poor quality
- **Technical:** Fatigue from rework
- **Product:** Delayed releases

---

## Act 2: The Solution (Slides 4-6)

### Slide 4: "Introducing SEF Agents"

**Positioning:** Engineering Operating System for AI-Assisted Development

**Category Distinction:**

| AI Assistants | SEF Agents |
|---------------|------------|
| Code Generation Tool | Engineering OS |
| Task-focused | Workflow-focused |
| Stateless | Persistent context |
| Individual | Team orchestration |

**One-liner:** "SEF Agents works WITH your AI assistant to add quality, memory, and workflow."

**Visual:** Layer diagram showing AI Assistant → SEF Agents → Team

---

### Slide 5: "13 Specialized Agents"

**Core Concept:** Role-specific guidance, not generic AI

| Phase | Agent | Function |
|-------|-------|----------|
| 0 | Discovery | Codebase archaeology, CODE_MAP |
| 1 | Product Manager | Requirements, testable AC |
| 2 | QA Lead | AC validation BEFORE dev |
| 3 | Architect | System design, boundaries |
| 4 | Developer | Elite Code Quality Protocol |
| 5 | PR Reviewer | Compliance, AI anti-patterns |
| 6 | Tester + Security | Parallel validation |
| 7 | Scrum Master | Workflow completion |
| Utility | Forensic Engineer | Incident investigation |
| Utility | Strategist | Trade-off analysis |
| Utility | Platform Engineer | DevEx, health scanning |

**Stakeholder Resonance:**
- **Business:** Full SDLC coverage
- **Technical:** Role-appropriate guidance
- **Product:** AC validated before development

---

### Slide 6: "Synchronous Engineering"

**Revolutionary Concept:** Collapse the delivery lifecycle

| Traditional | Synchronous |
|-------------|-------------|
| Sequential phases | Parallel execution |
| Handoff delays | Single-phase delivery |
| Context loss | Persistent memory |
| Rework loops | First-pass quality |

**Key Differentiators:**
1. **Pattern Learning:** Captures and reuses team patterns
2. **Workflow Orchestration:** Story state, dependencies, blockers
3. **QA Moves Left:** Validation before code, not after
4. **No Handoffs:** Agents collaborate, don't pass work

**Visual:** Traditional waterfall vs. collapsed synchronous timeline

---

## Act 3: The Proof (Slides 7-9)

### Slide 7: "Quality Enforcement"

**25+ Built-in Tools:**

| Category | Tools |
|----------|-------|
| Compliance | `validate_compliance`, `detect_ai_patterns` |
| Quality | `scan_debt`, `scan_code_quality`, `scan_complexity` |
| Security | `run_security_audit`, `scan_external_dependencies` |
| Workflow | `init_workflow`, `get_workflow_state`, `suggest_next_agent` |
| Documentation | `generate_codemap`, `validate_docs` |

**Key Point:** Standards enforced automatically. Not optional.

**Stakeholder Resonance:**
- **Business:** Consistent quality baseline
- **Technical:** Automated guardrails
- **Product:** AC compliance verified

---

### Slide 8: "Security by Design"

**Enterprise-Ready Security:**

| Claim | Verification |
|-------|--------------|
| No network calls | Zero HTTP clients, sockets |
| No data exfiltration | All operations local-only |
| No hardcoded secrets | Scanned, audited |
| OSS dependencies | Permissive licenses only |
| Stdio transport | No external communication |

**Command:** `uv run python -m sef_agents.security_scan`

**Stakeholder Resonance:**
- **Business:** Enterprise compliance
- **Technical:** Auditable security
- **Product:** Risk mitigation

---

### Slide 9: "Integration Path"

**Zero Friction Adoption:**

| Step | Action |
|------|--------|
| 1 | Add 4 lines to MCP config |
| 2 | Restart AI assistant |
| 3 | Type "Set agent to developer" |
| 4 | Quality enforcement active |

**Compatible With:**
- Cursor
- Claude Desktop
- Windsurf
- Any MCP-compatible client

**No Learning Curve:** Uses natural language. Ask questions, get guidance.

---

## Act 4: The Ask (Slide 10)

### Slide 10: "Next Steps"

**Call to Action by Stakeholder:**

| Stakeholder | Action |
|-------------|--------|
| **Business** | Schedule ROI discussion |
| **Technical** | 15-minute live demo |
| **Product** | Review workflow orchestration |

**Pilot Program:**
1. Select 1 project
2. Enable SEF Agents
3. Measure: rework rate, review cycles, defect escape
4. Compare to baseline

**Risk-Free:** No code changes. No infrastructure. Local-only. Disable anytime.

---

## Appendix: Stakeholder-Specific Messaging

### A. Business Leaders

**Primary Concerns:** ROI, risk, velocity, quality metrics

**Key Messages:**
1. Reduce rework by enforcing standards at creation time
2. Decrease defect escape through pre-development validation
3. Accelerate onboarding with persistent codebase knowledge
4. Audit-ready compliance without manual overhead

**Metrics to Highlight:**
- Rework reduction
- Review cycle reduction
- Defect escape rate
- Time to onboard

---

### B. Technical Leaders

**Primary Concerns:** Developer experience, code quality, security, integration

**Key Messages:**
1. Works WITH existing tools (Cursor, Copilot)
2. 13 specialized agents for role-appropriate guidance
3. Detects AI-generated anti-patterns
4. Pattern learning captures team conventions
5. Local-only, auditable, secure

**Technical Proof Points:**
- MCP standard (Anthropic)
- 25+ quality tools
- Security audit command
- No network calls

---

### C. Product Managers

**Primary Concerns:** Feature velocity, quality, AC validation, workflow visibility

**Key Messages:**
1. AC validated BEFORE development begins
2. Story state tracking with dependency graphs
3. Workflow orchestration across phases
4. Single-phase delivery (shippable in one commit)
5. Reduced handoff delays

**Workflow Benefits:**
- QA Lead validates AC pre-development
- Blockers tracked, escalated
- Phase transitions logged
- Handoff audit trail

---

## Narrative Flow (Presenter Notes)

### Opening (30 seconds)
"Every team we talk to has adopted AI assistants. Cursor, Copilot, ChatGPT. And every team has the same problem: more code, same bugs. Why?"

### Problem (2 minutes)
"AI assistants are typing accelerators. They generate code fast. But fast in the wrong direction is expensive. There's no quality enforcement, no workflow context, no pattern learning."

### Solution (3 minutes)
"SEF Agents is an Engineering Operating System. It works WITH your AI assistant. 13 specialized agents provide role-specific guidance. Synchronous engineering collapses the delivery lifecycle."

### Proof (2 minutes)
"25+ tools enforce standards automatically. Security is auditable. Integration takes 4 lines of config. Zero infrastructure."

### Close (30 seconds)
"Let's run a 15-minute demo. Pick one project. Measure the difference. No risk, no commitment."

---

## Competitive Positioning

| Competitor | What They Do | What SEF Adds |
|------------|--------------|---------------|
| Cursor | AI code generation | Workflow orchestration |
| Copilot | Autocomplete | Pattern learning |
| ChatGPT | Q&A, code help | Role specialization |
| SonarQube | Static analysis | Pre-development validation |
| Jira | Issue tracking | Story-aware AI guidance |

**Position:** SEF Agents is not a replacement. It's the orchestration layer that makes AI assistants enterprise-ready.
