# Bad 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"

## ❌ Bad Plan (What NOT to Do)

### Phase 1: Assessment (Insufficient)

**Vague Assessment**:
- Need to research Kubernetes
- Compare different approaches
- Look at costs
- Check what people are using

**Problems**:
- No specific questions identified
- No data points enumerated
- No output format defined
- No user priorities understood

---

### Phase 2: Query Classification (Skipped)

**No Classification Performed**:
Just jumped straight to deploying subagents without thinking about query type.

**Problems**:
- Missed opportunity to select optimal strategy
- No rationale for subagent count
- Random delegation without systematic approach
- Reduces research quality and efficiency

---

### Phase 3: Research Plan (Poorly Structured)

#### Vague Subagent Allocation: 10 Subagents (Too Many, Too Granular)

**Subagent 1**: Research EKS
**Subagent 2**: Research GKE
**Subagent 3**: Research AKS
**Subagent 4**: Research kubeadm
**Subagent 5**: Research kops
**Subagent 6**: Research costs
**Subagent 7**: Research operational stuff
**Subagent 8**: Research what companies use
**Subagent 9**: Research Kubernetes in general
**Subagent 10**: Compare everything

**Problems with This Allocation**:

1. **Overly Granular** (Anti-Pattern):
   - 10 subagents for medium-complexity query is excessive
   - Should be 3-5 subagents maximum
   - Subagents 1-3 could be combined: "Hosted Kubernetes landscape"
   - Subagents 4-5 could be combined: "Self-managed Kubernetes landscape"
   - Creates unnecessary coordination overhead

2. **Vague Instructions**:
   - "Research EKS" - research what about it? Features? Pricing? Use cases?
   - "Research costs" - costs of what? How compare? What scenarios?
   - "Research operational stuff" - what operations? Complexity? Time? Tools?
   - No specific objectives, sources, or data points

3. **Unclear Scope Boundaries**:
   - Subagent 6 "costs" overlaps with Subagents 1-5 (each service has costs)
   - Subagent 9 "Kubernetes in general" overlaps with everything
   - Subagent 10 "compare everything" overlaps with all others
   - High likelihood of duplicated effort

4. **No Aggregation Strategy**:
   - How will 10 separate reports be combined?
   - What structure will organize findings?
   - No synthesis plan defined

---

### Example of Bad Subagent Instructions

**Subagent 6: Research costs**

"Research Kubernetes costs. Look at how much it costs to run Kubernetes. Find pricing information. Write a report about costs."

**Why This Is Bad**:

1. **No Specific Objectives**:
   - "Research costs" is too vague
   - Costs of what exactly? Infrastructure? Operations? Tooling?
   - For what scenarios? Small clusters? Large? Which providers?

2. **No Source Guidance**:
   - Where should research begin?
   - What types of sources are authoritative?
   - Primary vs secondary sources?
   - No prioritization

3. **No Data Points Specified**:
   - What specific numbers needed?
   - What granularity? (per node? per cluster? per hour?)
   - What cost components? (compute, storage, network, labor?)

4. **No Output Format**:
   - "Write a report" is not specific
   - What structure? What density?
   - Comparison table? Narrative? Scenarios?

5. **No Scope Boundaries**:
   - Should this cover hosted AND self-managed costs?
   - Does it overlap with other subagents researching specific providers?
   - No clarity on what NOT to research

6. **No Quality Criteria**:
   - How recent must data be?
   - What level of verification required?
   - When is research complete?

---

### Phase 4: Execution (No Plan)

**Unplanned Deployment**:
- Deploy all 10 subagents at once without thought
- No monitoring plan
- No adaptation strategy
- No diminishing returns detection

**Problems**:
- Inefficient parallel execution of redundant work
- No Bayesian adaptation based on findings
- Likely to continue past diminishing returns
- May miss critical information due to poor coordination

---

## Why This Is a Bad Research Plan

### Critical Failures

#### 1. Insufficient Assessment
- Jumped to execution without understanding query
- No enumeration of key questions
- No identification of required data points
- Output format undefined

#### 2. Skipped Query Classification
- No systematic thinking about research strategy
- Random subagent allocation
- No optimization for query type
- Missed opportunity for parallel efficiency

#### 3. Overly Granular Decomposition
- 10 subagents when 3-5 optimal
- Unnecessary coordination overhead
- Violates "fewer capable subagents" principle
- Wastes resources

#### 4. Vague Subagent Instructions
- No specific objectives
- No source guidance
- No data points enumerated
- No output format
- No scope boundaries

#### 5. Unclear Scope Boundaries
- Significant overlap between subagents
- High likelihood of duplicated research
- Inefficient use of parallel execution
- Difficult synthesis due to redundancy

#### 6. No Aggregation Strategy
- Unclear how 10 reports will be combined
- No structure pre-planned
- Synthesis ad-hoc rather than systematic

#### 7. No Execution Plan
- No monitoring for progress
- No adaptation strategy
- No diminishing returns detection
- Inefficient and likely incomplete

### Likely Outcomes

Following this bad plan would produce:

❌ **Inefficient Research**:
- Wasted effort on duplicated work
- 10 subagents when 4 would suffice
- Coordination overhead exceeds value

❌ **Incomplete Coverage**:
- Vague instructions likely miss critical data
- No verification of key questions answered
- Gaps in research due to poor planning

❌ **Poor Quality Sources**:
- No source prioritization guidance
- Subagents may use low-quality sources
- No verification protocol

❌ **Difficult Synthesis**:
- Redundant findings across 10 reports
- Unclear how to structure final output
- High likelihood of missing connections

❌ **Low Information Density**:
- Vague instructions produce vague reports
- Lack of specific data points
- More words, less insight

❌ **Delayed Delivery**:
- Excessive subagent count takes longer
- May continue past diminishing returns
- Inefficient process overall

---

## How to Fix This Bad Plan

### Apply Good Research Coordination Principles

1. **Start with Systematic Assessment**:
   - List ALL key questions explicitly
   - Enumerate required data points
   - Define output format upfront
   - Understand user priorities

2. **Always Classify Query Type**:
   - Depth-first, breadth-first, or straightforward?
   - Select strategy matching query type
   - Plan subagent count accordingly

3. **Optimize Subagent Count**:
   - Combine Subagents 1-3 → "Hosted Kubernetes Landscape"
   - Combine Subagents 4-5 → "Self-Managed Kubernetes Landscape"
   - Keep Subagent 6 as "Cost Comparison Analysis"
   - Keep Subagent 8 as "Enterprise Adoption Trends"
   - Remove Subagents 7, 9, 10 (redundant or vague)
   - Result: 4 capable subagents instead of 10 narrow ones

4. **Write Extremely Detailed Instructions**:
   - 1-4 specific objectives per subagent
   - Sources explicitly identified
   - Exact data points enumerated
   - Output format defined
   - Scope boundaries clear

5. **Define Clear Scope Boundaries**:
   - Each subagent knows exactly what to cover
   - Each knows what NOT to cover
   - Minimal overlap, maximum efficiency

6. **Plan Aggregation Strategy**:
   - Pre-define final report structure
   - Identify cross-stream dependencies
   - Document synthesis approach

7. **Plan Execution Carefully**:
   - Parallel deployment where appropriate
   - Monitoring for progress and gaps
   - Bayesian adaptation based on findings
   - Diminishing returns detection

### Result: Good Research Plan

Following these fixes transforms the bad plan into a systematic, efficient, high-quality research coordination effort that produces actionable, comprehensive results.

See `good-research-plan.md` for the properly structured version.
