# Good Research Plan Example

## Query

"Research the current state of Kubernetes cluster management in 2025: hosted vs self-managed, cost comparison, operational complexity, and enterprise adoption trends"

## Phase 1: Assessment and Breakdown

### Main Concepts Identified
- Kubernetes cluster management approaches (hosted vs self-managed)
- Cost analysis (TCO comparison)
- Operational complexity (management burden, expertise required)
- Enterprise adoption trends (market direction)

### Key Questions to Answer
1. What are the major hosted Kubernetes options in 2025? (EKS, GKE, AKS, others)
2. What does self-managed Kubernetes entail? (infrastructure, tools, processes)
3. How do costs compare across approaches? (compute, operational overhead, tooling)
4. What is the operational complexity difference? (time investment, expertise needed)
5. What are enterprise adoption trends? (shift toward hosted, hybrid approaches)

### Required Data Points
- Hosted K8s offerings with feature comparisons
- Self-managed K8s tooling ecosystem (kubeadm, kops, Cluster API)
- TCO breakdowns with real numbers
- Operational metrics (time spent on management tasks)
- Adoption statistics from enterprises
- Case studies showing decision factors

### Output Format
Detailed comparative analysis organized by:
- Approach comparison (hosted vs self-managed)
- Cost analysis with specific numbers
- Operational complexity breakdown
- Adoption trends with data
- Decision framework for selection

### User Priorities
- Actionable comparison for decision-making
- Real cost data (not just theoretical)
- Understanding operational trade-offs
- Current market direction

## Phase 2: Query Classification

**Classification: BREADTH-FIRST**

### Rationale
This query has four distinct independent sub-questions:
1. Hosted Kubernetes landscape (what options exist, features)
2. Self-managed Kubernetes landscape (tooling, processes)
3. Cost comparison (quantitative analysis across approaches)
4. Enterprise adoption trends (market research)

Each sub-question requires different sources and research approaches:
- Sub-question 1: Cloud provider docs, feature comparisons
- Sub-question 2: CNCF landscape, tooling docs, community practices
- Sub-question 3: Cost calculators, case studies, TCO analyses
- Sub-question 4: Analyst reports, surveys, adoption studies

These are best researched in parallel by separate subagents, then aggregated.

**Why not depth-first?**
- Not multiple perspectives on single issue
- Each sub-question is distinct domain requiring different expertise
- Synthesis is aggregation rather than reconciliation of perspectives

**Why not straightforward?**
- Too complex for single investigation
- Multiple independent research streams needed
- Requires synthesis across diverse sources

## Phase 3: Research Plan Development

### Subagent Allocation: 4 Subagents (Breadth-First Strategy)

#### Subagent 1: Hosted Kubernetes Landscape

**Scope**: Research managed Kubernetes offerings from major cloud providers

**Primary Objectives**:
1. Catalog major hosted K8s options (EKS, GKE, AKS, DigitalOcean, Linode, etc.)
2. Compare features and capabilities across providers
3. Identify differentiators and unique offerings
4. Document SLA/support commitments

**Sources to Prioritize**:
- AWS EKS documentation and feature announcements
- Google GKE documentation and release notes
- Azure AKS documentation and roadmap
- Cloud provider pricing pages
- CNCF landscape for additional providers
- Recent announcements (Q4 2024 - Q1 2025)

**Specific Data Points**:
- Kubernetes versions supported
- Control plane management (who manages, SLA)
- Add-on services (monitoring, service mesh, etc.)
- Multi-region/multi-cluster capabilities
- Security features (encryption, RBAC, policies)
- Integration with cloud-native services

**Output Format**:
Dense report covering:
- Provider-by-provider feature matrix
- Key differentiators highlighted
- SLA and support commitments
- Recent improvements and roadmap items

**Scope Boundaries**:
- Focus ONLY on hosted/managed offerings
- Exclude self-managed tooling (separate subagent)
- Exclude cost analysis (separate subagent)
- Exclude adoption trends (separate subagent)

---

#### Subagent 2: Self-Managed Kubernetes Landscape

**Scope**: Research self-managed K8s tooling, processes, and requirements

**Primary Objectives**:
1. Document primary self-managed approaches (kubeadm, kops, Cluster API, Rancher, etc.)
2. Identify operational requirements and tooling ecosystem
3. Understand expertise needed and learning curve
4. Document ongoing maintenance burden

**Sources to Prioritize**:
- Kubernetes official documentation (kubeadm, Cluster API)
- CNCF landscape for cluster management tools
- Community best practices (Reddit, HackerNews, blogs)
- Case studies from companies self-managing
- Tool documentation (kops, Rancher, k3s, etc.)

**Specific Data Points**:
- Installation/setup complexity (time, steps, expertise)
- Upgrade procedures and frequency
- Monitoring and observability requirements
- Disaster recovery and backup approaches
- Security hardening necessary
- Day 2 operations workload

**Output Format**:
Dense report covering:
- Approach comparison (kubeadm vs kops vs Cluster API, etc.)
- Operational requirements breakdown
- Expertise and team size needs
- Maintenance time investment estimates
- Tooling ecosystem for operations

**Scope Boundaries**:
- Focus ONLY on self-managed approaches
- Exclude hosted services (separate subagent)
- Exclude cost analysis (separate subagent)
- Exclude adoption trends (separate subagent)

---

#### Subagent 3: Cost Comparison Analysis

**Scope**: Research and compare total cost of ownership across approaches

**Primary Objectives**:
1. Gather hosted K8s pricing models and real costs
2. Calculate self-managed costs (infrastructure, operational overhead, tooling)
3. Compare TCO across approaches with specific scenarios
4. Identify hidden costs and cost optimization strategies

**Sources to Prioritize**:
- Cloud provider pricing calculators
- TCO case studies and analyses
- Cost optimization guides
- Real-world cost breakdowns from companies
- Analyst reports with cost comparisons

**Specific Data Points**:
- Hosted K8s pricing (control plane, worker nodes, add-ons)
- Self-managed infrastructure costs (compute, storage, network)
- Operational costs (engineer time, SRE team size)
- Tooling costs (Rancher licensing, monitoring, etc.)
- Cost scenarios: small (10-50 nodes), medium (50-200), large (200+)

**Output Format**:
Dense report covering:
- Pricing model breakdown by approach
- TCO calculations for 3 scenarios (small, medium, large)
- Cost comparison table with specific numbers
- Hidden costs identified (operational overhead, tooling)
- Cost optimization strategies

**Scope Boundaries**:
- Focus ONLY on cost analysis
- Exclude feature comparisons (separate subagent)
- Exclude operational complexity beyond cost (separate subagent)
- Use findings from Subagents 1 and 2 for context, but focus on $$

---

#### Subagent 4: Enterprise Adoption Trends

**Scope**: Research market direction and enterprise decision patterns

**Primary Objectives**:
1. Gather adoption statistics (hosted vs self-managed split)
2. Identify trends over time (shift toward hosted, hybrid approaches)
3. Document decision factors from enterprise case studies
4. Understand future outlook from analysts

**Sources to Prioritize**:
- Gartner, Forrester, IDC analyst reports
- CNCF surveys and state of Kubernetes reports
- Cloud provider customer case studies
- Enterprise architecture blogs and talks
- Recent surveys (Q4 2024 - Q1 2025)

**Specific Data Points**:
- % of enterprises using hosted vs self-managed
- Trend direction (year-over-year changes)
- Reasons for choosing hosted (top 3-5 factors)
- Reasons for choosing self-managed (top 3-5 factors)
- Hybrid approaches (percentage, patterns)
- Company size correlation (startups vs enterprise)

**Output Format**:
Dense report covering:
- Current adoption split (with percentages)
- Trend analysis (historical and projected)
- Decision factor breakdown by approach
- Enterprise case study patterns
- Future outlook from analysts

**Scope Boundaries**:
- Focus ONLY on adoption trends and market research
- Exclude technical feature details (separate subagent)
- Exclude cost specifics (separate subagent)
- Cite enterprise examples, don't deep-dive implementation

---

### Scope Boundary Clarity

**Clear Division of Labor**:
- Subagent 1: WHAT hosted options offer (features, capabilities)
- Subagent 2: WHAT self-managed requires (tooling, operations)
- Subagent 3: HOW MUCH each approach costs (TCO analysis)
- Subagent 4: WHO is adopting what and WHY (market trends)

**Minimal Overlap**:
- Each subagent focuses on distinct aspect
- No redundant research expected
- Synthesis will combine four independent reports

### Aggregation Strategy

**Final Report Structure**:
1. Executive Summary (synthesis of all findings)
2. Approach Overview
   - Hosted Kubernetes (from Subagent 1)
   - Self-Managed Kubernetes (from Subagent 2)
3. Cost Analysis (from Subagent 3)
   - Pricing models
   - TCO comparison
   - Cost scenarios
4. Operational Complexity (synthesize from Subagents 1 and 2)
   - Hosted: What provider manages
   - Self-managed: What you manage
   - Effort comparison
5. Market Trends (from Subagent 4)
   - Current adoption
   - Trend direction
   - Decision factors
6. Decision Framework
   - When to choose hosted (synthesize findings)
   - When to choose self-managed (synthesize findings)
   - Hybrid approaches (synthesize findings)

**Cross-Stream Dependencies Identified**:
- Cost analysis uses feature knowledge from Subagents 1 and 2
- Decision framework synthesizes all four subagent findings
- Operational complexity combines technical and market insights

### Expected Deliverables

**Four Independent Reports** (from subagents):
1. Hosted K8s landscape report (~500-800 words)
2. Self-managed K8s landscape report (~500-800 words)
3. Cost comparison analysis (~500-800 words)
4. Adoption trends report (~500-800 words)

**Final Synthesis** (from lead agent):
- Comprehensive analysis (~2000-2500 words)
- High information density
- Actionable decision framework
- No citations (as required)

## Phase 4: Execution Plan

### Deployment Strategy

**Parallel Execution** (all 4 subagents simultaneously):
- No dependencies between Subagents 1, 2, 3, 4
- Each has independent sources and objectives
- Maximum efficiency through parallelization

**Detailed Instructions Prepared**:
- Each subagent receives instructions as detailed above
- Clear primary objectives (1-4 items each)
- Specific sources identified
- Exact data points listed
- Output format defined
- Scope boundaries explicit

### Monitoring Plan

**Progress Signals**:
- Watch for subagents completing early
- Review initial findings for unexpected gaps
- Note if any subagent reports missing data

**Bayesian Adaptation**:
- If cost data shows strong preference for one approach, emphasize that in synthesis
- If adoption trends contradict expected patterns, investigate further
- If operational complexity difference smaller than expected, adjust recommendations

**Diminishing Returns Detection**:
- If multiple sources repeating same cost figures, sufficient data gathered
- If adoption trend consistent across all reports, consensus reached
- If feature comparisons saturate, no new capabilities found

### Synthesis Approach

**Integration Strategy**:
1. Read all four subagent reports thoroughly
2. Extract key facts into master list
3. Build cost comparison table from Subagent 3 data
4. Build feature matrix from Subagents 1 and 2 data
5. Identify adoption patterns from Subagent 4 data
6. Synthesize decision framework using all findings
7. Structure final report logically
8. Write with high information density
9. No citations included

**Quality Verification Before Delivery**:
- All original questions answered? ✓
- Cost data specific and quantified? ✓
- Feature comparisons comprehensive? ✓
- Adoption trends backed by data? ✓
- Decision framework actionable? ✓
- Format matches user needs? ✓
- No citations in output? ✓

## Why This Is a Good Research Plan

### Strengths

1. **Systematic Assessment**:
   - Main concepts clearly identified
   - Key questions explicitly enumerated
   - Data points specified before research begins
   - Output format defined upfront

2. **Correct Query Classification**:
   - Identified as breadth-first (four independent streams)
   - Rationale provided with clear logic
   - Alternative classifications considered and rejected with reasons

3. **Optimal Subagent Count**:
   - 4 subagents matches 4 distinct sub-questions
   - Not overly granular (could have been 8+ subagents)
   - Not too broad (could have been 1-2 subagents)
   - Follows guideline: 3-5 for medium complexity

4. **Extremely Detailed Subagent Instructions**:
   - Each has 1-4 specific objectives
   - Sources explicitly identified by name/type
   - Exact data points listed (no vagueness)
   - Output format clearly described
   - Scope boundaries prevent overlap

5. **Clear Scope Boundaries**:
   - Each subagent knows exactly what to research
   - Each knows what NOT to research (other subagents' domains)
   - Minimal overlap, maximum efficiency
   - Clean division of labor

6. **Well-Planned Aggregation**:
   - Final report structure pre-planned
   - Cross-stream dependencies identified
   - Synthesis strategy defined
   - Quality checklist included

7. **Efficient Execution**:
   - Parallel deployment (no unnecessary sequential steps)
   - Monitoring plan for adaptation
   - Diminishing returns detection planned
   - Synthesis approach detailed

### Expected Outcome

Following this plan will produce:
- Comprehensive coverage of all four aspects (hosted, self-managed, cost, trends)
- High-quality sources with specific data
- Actionable decision framework
- Efficient research (no wasted effort)
- Professional deliverable ready for immediate use

This plan demonstrates **research coordination excellence** through systematic methodology, effective delegation, and clear synthesis strategy.
