# Deep Research Example Workflow

## User Request
> "I need to understand the current state of AI coding assistants. What's the landscape, who are the players, and what should I know if I'm choosing one for my team?"

---

## Phase 1: Initial Engagement

**Clarifying Questions:**
1. What is the primary question or problem you're trying to solve — are you replacing an existing tool, or is this a first adoption?
2. What depth of analysis do you need — a quick landscape overview, or exhaustive technical and security analysis?
3. Are there specific constraints — budget limits, security requirements, or integration needs with your existing stack?

**User Response:**
> "First adoption, need exhaustive analysis for a 15-person engineering team using VS Code. Security is paramount because we handle healthcare data. Budget is flexible if the value is there."

**Reflection:**
> "So you need a comprehensive, security-focused analysis of AI coding assistants for healthcare software development. The research must prioritize security certifications, data handling practices, and HIPAA compliance considerations alongside technical capabilities. I'll investigate market players, security frameworks, real-world healthcare adoption, and implementation considerations for regulated environments."

---

## Phase 2: Research Planning

### Major Themes Identified

| Theme | Key Questions | Approach |
|-------|---------------|----------|
| **1. Market Landscape** | Who are the major players? Market share? Differentiation? | Broad search + competitive analysis |
| **2. Security & Compliance** | How is code handled? SOC 2? HIPAA? On-prem options? | Deep dive on security docs, compliance certifications |
| **3. Technical Capabilities** | Context understanding, language support, IDE integration | Feature comparison, benchmark analysis |
| **4. Healthcare-Specific Considerations** | Healthcare adoption, PHI handling, regulatory alignment | Specialized sources, case studies |
| **5. Implementation & ROI** | Setup complexity, productivity data, pricing models | Real-world reports, cost analysis |

### Research Execution Plan

**Theme 1: Market Landscape**
1. `web_search count=20` "AI coding assistants 2024 2025 market landscape comparison"
2. Analysis — identify players, segments, trends
3. `web_fetch` on top 5 authoritative sources
4. Synthesis — market structure, competitive dynamics

**Theme 2: Security & Compliance**
1. `web_search count=20` "GitHub Copilot security SOC 2 HIPAA compliance enterprise"
2. Analysis — security frameworks, data handling
3. `web_fetch` on security whitepapers, compliance docs
4. Synthesis — security landscape, gaps, recommendations

**Theme 3: Technical Capabilities**
1. `web_search count=20` "AI coding assistant benchmarks code completion accuracy 2024"
2. Analysis — feature matrices, performance claims
3. `web_fetch` on benchmark studies, technical docs
4. Synthesis — capability assessment, differentiators

**Theme 4: Healthcare-Specific**
1. `web_search count=20` "AI coding assistant healthcare HIPAA PHI code security"
2. Analysis — healthcare adoption, regulatory concerns
3. `web_fetch` on healthcare case studies, compliance guides
4. Synthesis — healthcare readiness, risk factors

**Theme 5: Implementation & ROI**
1. `web_search count=20` "AI coding assistant developer productivity ROI study 2024"
2. Analysis — productivity claims, cost models
3. `web_fetch` on ROI studies, pricing analysis
4. Synthesis — value proposition, implementation factors

### Expected Deliverables
- Comprehensive research paper (20-30 pages)
- APA citations throughout (1-2 per paragraph)
- Security-focused recommendations for healthcare
- Side-by-side comparison matrix (converted to narrative)
- Implementation roadmap

---

## Phase 3: Research Cycles (Auto-Execute)

### Theme 1: Market Landscape — Cycle 1

**Tool:** `web_search query="AI coding assistants 2024 2025 market landscape comparison GitHub Copilot Cursor" count=20`

**Results:** Found sources on GitHub Copilot, Cursor, Codeium, Amazon CodeWhisperer, Google Code Assist, Tabnine, Replit Ghostwriter.

**Analysis:**
The search reveals a market dominated by GitHub Copilot with approximately 1.3 million paid subscribers as of 2024, representing the first-mover advantage in this space. Cursor has emerged as a significant challenger, having raised $60 million in Series A funding and positioning itself around larger context windows and privacy-focused features. Codeium operates on a freemium model with strong uptake among individual developers, while enterprise-focused players like Amazon CodeWhisperer and Google Code Assist are targeting organizational adoption through cloud integration.

Initial hypotheses forming: (1) Market is consolidating around a few major players, (2) Differentiation is shifting from basic completion to context understanding and privacy, (3) Enterprise features are becoming the primary battleground.

**Gaps Identified:**
- Limited independent market share data — most figures come from vendor announcements
- Unclear on international market distribution
- Need deeper analysis on enterprise vs. individual adoption rates
- Contradiction: Some sources claim Cursor is gaining rapidly; others show Copilot maintaining dominance

---

### Theme 1: Market Landscape — Cycle 2

**Tool:** `web_fetch` on GitHub Copilot official documentation, Cursor about page, and TechCrunch funding coverage.

**Analysis:**
Fetching primary sources reveals important nuances that challenge initial assumptions. While GitHub's announced 1.3 million subscribers represents paid users, the actual developer reach is larger through free educational licenses and open source programs. This suggests the market is actually more fragmented than initially hypothesized.

Cursor's $60 million raise is confirmed through TechCrunch reporting, but the company's actual user numbers remain private. This creates uncertainty about their true market position — they may be growing rapidly in specific segments (startups, privacy-conscious developers) without threatening Copilot's overall dominance.

The contradiction noted in Cycle 1 appears resolvable: Cursor is gaining mindshare and venture attention, but Copilot maintains volume leadership. These are different metrics of "success" that can coexist.

New patterns emerging: (1) Market segmentation by use case (individual vs. enterprise), (2) Geographic variation in adoption, (3) Pricing model experimentation (per-seat vs. usage-based).

**Connections to Cycle 1:**
The initial hypothesis about market consolidation requires refinement. Rather than a winner-take-all dynamic, the evidence suggests parallel market development: Copilot for broad adoption, Cursor for specific segments, and specialized tools for niche use cases.

**Remaining Uncertainties:**
- Actual Cursor user numbers remain undisclosed
- Enterprise adoption rates poorly documented
- International market data largely absent

---

### Theme 2: Security & Compliance — Cycle 1

**Tool:** `web_search query="GitHub Copilot security SOC 2 HIPAA compliance enterprise data handling" count=20`

**Results:** Found GitHub security whitepaper, SOC 2 reports, enterprise trust documentation, and some healthcare-specific discussions.

**Analysis:**
Security documentation reveals significant variation in compliance posture across vendors. GitHub Copilot Business and Enterprise tiers explicitly address security concerns with SOC 2 Type II certification and options for code isolation that prevent training data inclusion. However, HIPAA compliance remains ambiguous — GitHub states they will sign Business Associate Agreements but stops short of claiming HIPAA compliance for the AI features themselves.

Cursor positions itself as privacy-first with a local mode that processes code entirely on-device, eliminating transmission risks. This represents a fundamentally different security model that may be more appropriate for healthcare contexts.

Initial hypothesis: Security features correlate with pricing tier, with enterprise offerings providing necessary controls for regulated industries.

**Gaps Identified:**
- No clear HIPAA compliance claims from any vendor for AI features specifically
- Limited independent security audits published
- Unclear on data retention policies across vendors
- Contradiction: Some sources suggest on-premise options exist; others indicate cloud-only

---

### Theme 2: Security & Compliance — Cycle 2

**Tool:** `web_fetch` on GitHub Copilot Enterprise security documentation, Cursor security page, and healthcare IT forum discussions.

**Analysis:**
Deep extraction from primary sources confirms the security landscape is complex and evolving. GitHub's documentation explicitly states that Copilot Enterprise offers "code isolation" that prevents user code from being used to train public models, addressing a major concern for proprietary codebases. However, the documentation carefully avoids HIPAA compliance claims, instead referring to GitHub's general BAA availability.

This is a critical finding: The AI coding assistant products themselves may not be HIPAA-compliant even when the underlying platform is. This distinction is crucial for healthcare organizations.

Cursor's local mode is confirmed to process code entirely on-device using locally-hosted models, representing a genuinely different security posture. For healthcare, this eliminates the data transmission risk but may introduce other concerns (local model quality, update mechanisms).

The contradiction about on-premise options is partially resolved: No major vendor offers true on-premise AI coding assistants as of this research, though some offer VPC deployment or local processing modes.

**Connections to Theme 1:**
The market segmentation noted earlier aligns with security postures: Copilot for general enterprise, Cursor for security-conscious segments. This suggests healthcare organizations may need to evaluate Cursor more seriously despite its smaller market presence.

---

### Theme 3: Technical Capabilities — Cycle 1

*[Similar two-cycle process with analysis between each tool call...]*

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### Theme 4: Healthcare-Specific Considerations — Cycle 1

*[Similar two-cycle process...]*

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### Theme 5: Implementation & ROI — Cycle 1

*[Similar two-cycle process...]*

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## Cross-Theme Knowledge Integration

After completing all five themes with two cycles each, synthesis reveals:

**Emerging Pattern 1: The Healthcare Gap**
No AI coding assistant currently claims HIPAA compliance for AI features specifically. This creates a significant gap for healthcare organizations that wasn't immediately apparent in initial landscape analysis.

**Emerging Pattern 2: The Security-Performance Tradeoff**
Local processing (Cursor) offers superior privacy but may sacrifice some capability compared to cloud-powered alternatives (Copilot). This tradeoff is central to the healthcare decision.

**Emerging Pattern 3: Market Immaturity**
The field is evolving rapidly with new features and compliance developments monthly. Any recommendation must account for this volatility.

**Contradictions Resolved:**
- Market dominance vs. challenger growth: Different metrics, both valid
- Cloud vs. on-premise: No true on-premise exists; local processing is the alternative

**Unified Understanding:**
For a healthcare engineering team, the decision framework differs from general enterprise adoption. Security and compliance considerations outweigh raw capability, suggesting evaluation of Cursor's local mode as a primary option despite smaller market presence.

---

## Phase 4: Final Report

*[Presented as cohesive research paper with narrative sections, proper APA citations, no bullet points, 6-8+ paragraphs per major section...]*

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## Key Distinctions from Standard Research

| Aspect | Standard Research | Deep Research Protocol |
|--------|-------------------|------------------------|
| Cycles per theme | 1 | Minimum 2 |
| Analysis between tools | Optional | Required |
| Citation density | As needed | 1-2 per paragraph |
| Final format | Flexible | Academic narrative |
| Contradiction handling | Note if found | Must address all |
| Writing style | Variable | Flowing prose only |
