Decision Framework for AI Coding Tools in Golang 2026: Choose the Right One

 1┌────────────────────────────────────────────────────────────────┐
 2│             Five-Dimension Evaluation Framework                 │
 3├──────────────────────┬─────────────────────────────────────────┤
 4│  1. WORKFLOW FIT     │  How well does the tool match how the    │
 5│                      │  team works right now, today?            │
 6├──────────────────────┼─────────────────────────────────────────┤
 7│  2. TECHNICAL FIT    │  Can the tool handle your tech stack     │
 8│                      │  and codebase size?                      │
 9├──────────────────────┼─────────────────────────────────────────┤
10│  3. TEAM DYNAMICS    │  Does the tool scale with your team's    │
11│                      │  structure and size?                     │
12├──────────────────────┼─────────────────────────────────────────┤
13│  4. BUDGET TCO       │  Is the Total Cost of Ownership          │
14│                      │  acceptable relative to the value?       │
15├──────────────────────┼─────────────────────────────────────────┤
16│  5. STRATEGIC FIT    │  Does it align with the long-term        │
17│                      │  engineering direction?                  │
18└──────────────────────┴─────────────────────────────────────────┘

 1□ Is the team more comfortable with the terminal or an IDE?
 2  → Terminal → Claude Code
 3  → IDE → Cursor, Copilot, Kiro, Windsurf, Gemini
 4
 5□ Is spec-before-code already an established practice?
 6  → Yes → Claude Code + Spec Kit, or AWS Kiro
 7  → No → A more "code-first" tool (Cursor, Copilot)
 8
 9□ How often does the team need to edit multiple files at once?
10  → Very often → Cursor Composer or Claude Code
11  → Rarely → Copilot inline or Windsurf
12
13□ Is the GitHub PR workflow a core team ritual?
14  → Yes → GitHub Copilot (deep GitHub integration)
15  → No → choose based on other dimensions
16
17□ Are there batch automation tasks the AI should handle without supervision?
18  → Yes → Claude Code or AWS Kiro (best autonomous agents)
19  → No → All tools adequate
20
21□ Does the team actively pair program?
22  → Yes → Claude Code (best dialogue model)
23  → No → Cursor or Copilot are more efficient

 1< 10,000 lines (startup, new project):
 2  All tools adequate. Workflow fit becomes the main deciding factor.
 3
 410,000 – 100,000 lines (mid-size project):
 5  Claude Code: excellent (200K token context)
 6  Cursor: excellent (100K + smart RAG)
 7  Copilot: good (64K, but RAG helps)
 8  Windsurf: good (Cascade helps)
 9  Gemini: excellent (1M token, overkill but nice)
10  Kiro: excellent (200K via Bedrock)
11
12100,000 – 1,000,000 lines (large enterprise):
13  Gemini Code Assist: WINNER (1M token context)
14  Copilot Enterprise: excellent (Workspace mode)
15  Claude Code: good (needs smart context selection)
16  Cursor: adequate (RAG helps but there's a limit)
17  Windsurf: adequate
18  Kiro: adequate
19
20> 1,000,000 lines (very large monorepo):
21  Gemini Code Assist or Copilot Enterprise
22  Self-hosted with a custom RAG pipeline

 1// Test 1: Does the tool generate idiomatic Go error handling?
 2// Expect:
 3result, err := doSomething(ctx)
 4if err != nil {
 5    return nil, fmt.Errorf("package.Function: %w", err)
 6}
 7
 8// Red flag from the tool:
 9result, _ := doSomething(ctx)  // ignoring error
10return errors.New("something failed")  // no context wrap
11
12// Test 2: A lean interface
13// Expect a minimal interface on the consumer side:
14type OrderRepository interface {
15    GetByIDAndUserID(ctx context.Context, orderID, userID uuid.UUID) (*Order, error)
16}
17
18// Red flag:
19type OrderRepository interface {
20    GetByID(ctx context.Context, id uuid.UUID) (*Order, error)
21    Create(ctx context.Context, order *Order) error
22    Update(ctx context.Context, order *Order) error
23    Delete(ctx context.Context, id uuid.UUID) error
24    List(ctx context.Context, filter Filter) ([]*Order, error)
25    Count(ctx context.Context, filter Filter) (int64, error)
26    // 10 more methods...
27}
28
29// Test 3: Correct context propagation
30// Expect:
31func (uc *CancelOrderUseCase) Execute(ctx context.Context, input Input) error {
32    order, err := uc.repo.GetByIDAndUserID(ctx, input.OrderID, input.UserID)
33    // ctx passed to all downstream calls
34
35// Test 4: Go version compliance
36// If the project is Go 1.22 → the tool must not generate Go 1.23+ features
37// range-over-func, slices.Collect, iter.Seq, etc.

1Need integration with...            → Choose...
2─────────────────────────────────────────────
3AWS Lambda, ECS, RDS, S3          → AWS Kiro (native)
4Google Cloud Run, GKE, BigQuery   → Gemini Code Assist (native)
5GitHub Actions, Dependabot        → GitHub Copilot (native)
6Kafka, Redis, PostgreSQL          → All tools, but Claude Code
7                                     has the deepest understanding
8Custom internal tools              → Claude Code (extensible)
9Jira/Linear for task tracking      → Claude Code (via bash tool)

 1Solo developer (1 person):
 2  Priority: feature richness + price
 3  Best fit: Windsurf (free tier) or Claude Code (pay per use)
 4  Avoid: Enterprise plans — overkill
 5
 6Small team (2-5 people):
 7  Priority: shared context, consistency across developers
 8  Best fit: Claude Code (shared CLAUDE.md in the repo)
 9              or Cursor (shared .cursorrules)
10  Action: invest 2 hours to set up a good CLAUDE.md
11
12Medium team (5-20 people):
13  Priority: onboarding, governance, adoption rate
14  Best fit: GitHub Copilot (familiar to everyone)
15              or AWS Kiro (structured onboarding via steering docs)
16  Action: pilot with 3-4 early adopters first
17
18Large team (20+ people):
19  Priority: enterprise compliance, usage tracking, admin controls
20  Best fit: GitHub Copilot Enterprise or Gemini Code Assist Enterprise
21  Action: security review, DPA, procurement process

 1Teams that already understand SDD (post Topics 1 & 2):
 2  → Claude Code or AWS Kiro (both spec-first aligned)
 3  → CLAUDE.md already exists, just optimize it
 4
 5Teams just getting started with AI tools:
 6  → GitHub Copilot (most familiar, IDE plugin)
 7  → Or Windsurf (free, low risk to explore)
 8  → Don't start with the Claude Code CLI — the curve is too steep
 9
10Teams with strong seniors and many juniors:
11  → AWS Kiro (steering docs from seniors → juniors follow)
12  → Or Claude Code with a detailed CLAUDE.md
13
14Distributed teams (remote, different timezones):
15  → Claude Code (async friendly, CLAUDE.md as a knowledge base)
16  → Windsurf Cascade (retains context across sessions)

 1┌──────────────────────┬────────────────┬─────────────────────────┐
 2│ Tool                 │ Base Cost      │ Note                    │
 3├──────────────────────┼────────────────┼─────────────────────────┤
 4│ Claude Code          │ $30-100        │ Per-token, variable.    │
 5│ (API cost)           │                │ Light user: $20-30      │
 6│                      │                │ Heavy user: $80-100     │
 7├──────────────────────┼────────────────┼─────────────────────────┤
 8│ Cursor               │ $20 (Pro)      │ Flat rate, predictable  │
 9│                      │ $40 (Business) │ Business = team features│
10├──────────────────────┼────────────────┼─────────────────────────┤
11│ GitHub Copilot       │ $10 (Individual)│ Cheapest option        │
12│                      │ $19 (Business) │ Business = admin + audit│
13│                      │ $39 (Enterprise)│ Custom model capability│
14├──────────────────────┼────────────────┼─────────────────────────┤
15│ AWS Kiro             │ $0 (Beta 2026) │ Free while in beta      │
16│                      │ TBD post-beta  │ Bedrock cost separate   │
17├──────────────────────┼────────────────┼─────────────────────────┤
18│ Windsurf             │ $0 (Free tier) │ Limited daily usage     │
19│                      │ $15 (Pro)      │ Pro: unlimited Cascade  │
20├──────────────────────┼────────────────┼─────────────────────────┤
21│ Gemini Code Assist   │ $0 (GCP trial) │ Free for GCP users      │
22│                      │ $19 (Standard) │ Standard: full features │
23│                      │ Custom (Ent.)  │ Enterprise: custom model│
24└──────────────────────┴────────────────┴─────────────────────────┘

 1Assumption: Indonesian Go developer, rate Rp 15-25 million/month
 2                                      ≈ $900-1,500/month
 3
 4If an AI tool saves 1 hour per day (conservative estimate):
 5  Value per developer per month:
 6  = (1 hour / 8 hours) × $1,200 avg salary
 7  = 12.5% × $1,200
 8  = $150/month value per developer
 9
10The most expensive tool (Claude Code heavy user): $100/month
11ROI = $150 / $100 = 1.5x (minimum)
12
13If it saves 2-3 hours/day (realistic for SDD + AI tools):
14  Value = 25-37% × $1,200 = $300-450/month
15  ROI = $300-450 / $100 = 3-4.5x
16
17Plus:
18  - Fewer production bugs (post-deploy fix cost = 5-10× dev cost)
19  - Faster feature delivery (business value)
20  - Better documentation (via AI-assisted spec)
21  - Faster onboarding for new developers
22
23Realistic ROI for a well-adopted AI tool: 5-10×

 1Startup (< 10 developers, bootstrap):
 2  Windsurf Free + Claude Code API (pay per use)
 3  Budget: ~$15-30/developer/month
 4
 5Growing startup (10-50 developers):
 6  Cursor Pro + Copilot for PR review
 7  Budget: ~$30-39/developer/month
 8
 9Scale-up (50-200 developers):
10  Copilot Business + Claude Code API for specialist tasks
11  Budget: ~$29-49/developer/month
12
13Enterprise (200+ developers):
14  Copilot Enterprise or Gemini Enterprise
15  Custom pricing, includes: admin, compliance, audit

 1Q1: Will the team grow significantly within 12 months?
 2  → Yes → Choose a tool with a clear enterprise path
 3  → No → Optimize for present need
 4
 5Q2: Is vendor lock-in a concern?
 6  → Yes → Cursor (model-agnostic, can switch Claude/GPT/etc)
 7  → Not critical → Choose based on other dimensions
 8
 9Q3: Is data sovereignty / privacy a requirement?
10  → Yes, strict → Self-hosted (Ollama) or Copilot Enterprise
11  → Yes, moderate → AWS Kiro (data in your region)
12  → Not critical → All tools acceptable
13
14Q4: Does the team want to adopt the SDD workflow (Topics 1 & 2)?
15  → Yes → Claude Code + Spec Kit (or AWS Kiro)
16  → Not sure yet → Pilot Claude Code for 4 weeks, evaluate
17
18Q5: Will AI be a competitive advantage, not just a productivity tool?
19  → Yes → Invest deeper: Claude Code + Spec Kit + custom tooling
20  → No → Copilot or Cursor is enough
21
22Q6: Will the team build AI-powered products (Topic 4+)?
23  → Yes → Claude Code (familiarity with the Anthropic API will help)
24  → No → Choose based on other dimensions

 1START
 2  │
 3  ▼
 4Is the budget very limited (< $10/dev/month)?
 5  │
 6  ├─ YES → Windsurf Free Tier
 7  │        + Copilot Free (5K completions/month)
 8  │
 9  └─ NO
10       │
11       ▼
12     Does the team already use SDD (spec before code)?
13       │
14       ├─ YES → Do they want IDE-based or CLI?
15       │        ├─ IDE → AWS Kiro
16       │        └─ CLI → Claude Code + Spec Kit
17       │
18       └─ NO
19            │
20            ▼
21          Is the team heavily invested in GitHub?
22            │
23            ├─ YES → GitHub Copilot
24            │
25            └─ NO
26                 │
27                 ▼
28               Is the codebase > 100K lines?
29                 │
30                 ├─ YES → Is it on Google Cloud?
31                 │        ├─ YES → Gemini Code Assist
32                 │        └─ NO → Cursor + Copilot combo
33                 │
34                 └─ NO → Prefer an IDE workflow?
35                              ├─ YES → Cursor
36                              └─ NO → Claude Code

 1WEEK 1-2: SETUP AND BASELINE
 2
 3  Action items:
 4  □ Pick 2-3 enthusiastic early adopters (not the skeptics)
 5  □ Pick 2 tools to test head-to-head (no more than 2)
 6  □ Set up identical context files for both tools
 7     (CLAUDE.md and .cursorrules/copilot-instructions with the same content)
 8  □ Define the same task to test (use a feature from the running sprint)
 9  □ Set up a simple tracking spreadsheet
10
11  Tracking metrics:
12  - Time to implement a task (from start → PR ready)
13  - Number of mechanical review comments per PR
14  - How many times the AI output had to be edited before it was usable
15  - Developer satisfaction (1-10)
16
17WEEK 3-4: PARALLEL TESTING
18
19  Action items:
20  □ Each early adopter implements 3 features with Tool A
21  □ Implement 3 features of equivalent complexity with Tool B
22  □ Weekly sync: share observations, frustrations, wins
23
24  Key questions to track:
25  - Does the tool follow CLAUDE.md/cursorrules consistently?
26  - How much editing is needed after the AI generates?
27  - Does context "get lost" in the middle of long sessions?
28  - Is the tool suited for pair programming sessions?
29
30WEEK 5: EVALUATION
31
32  Action items:
33  □ Compile all metrics from weeks 3-4
34  □ Compute the average and range for each metric
35  □ Score each tool using the scorecard (see 04.13)
36  □ Short interview with each early adopter (30 minutes)
37  □ Demo to the tech lead and CTO (if enterprise)
38
39WEEK 6: DECISION AND ROLLOUT PLAN
40
41  Action items:
42  □ Present the pilot results to team leadership
43  □ Make a decision based on data (not preference)
44  □ Create an onboarding guide for the winning tool
45  □ Plan a gradual rollout to the whole team
46  □ Document the decision as an ADR (Architecture Decision Record)

 1Scenarios that justify multi-tool:
 2
 3A. CLAUDE CODE + COPILOT (most popular):
 4   Claude Code: SDD workflow, complex reasoning, debugging
 5   Copilot: Inline suggestions in the daily IDE, PR review
 6   Total cost: ~$30-50/dev/month
 7   Best for: Teams that want SDD but don't want the full CLI lifestyle
 8
 9B. CLAUDE CODE + CURSOR:
10   Claude Code: Planning, spec review, complex logic
11   Cursor Composer: Bulk multi-file implementation
12   Total cost: ~$40-60/dev/month
13   Best for: Teams that need speed in bulk implementation
14
15C. WINDSURF FREE + CLAUDE CODE API (budget-conscious):
16   Windsurf: Daily coding, long sessions
17   Claude Code: Complex tasks that justify the per-token cost
18   Total cost: ~$10-30/dev/month
19   Best for: Startups or individual developers
20
21D. COPILOT + GEMINI (enterprise, large codebase):
22   Copilot: Daily coding, PR review
23   Gemini: Full-codebase scanning and discovery
24   Total cost: ~$30-40/dev/month
25   Best for: Enterprise with a very large codebase
26
27Don't go multi-tool if:
28❌ The team is still onboarding → one tool first until comfortable
29❌ The budget is very limited → one best tool for the main use case
30❌ The team doesn't have time to maintain multiple context files

 1❌ CRITICAL RED FLAGS (switch immediately):
 2
 3The tool frequently ignores CLAUDE.md / the context file
 4  → This tool doesn't support persistent context reliably
 5  → You'll spend more time repeating instructions than the benefit is worth
 6
 7You have to explain the same convention over and over in one session
 8  → Context retention is very poor
 9  → Every session starts from zero
10
11The generated Go code always needs heavy editing (> 50% needs edits)
12  → The tool isn't familiar with the Go idioms you need
13  → Slower than manual coding
14
15The team doesn't want to use it after 3 weeks
16  → Adoption failure is death for an AI tool investment
17  → A technically superior tool that isn't adopted = waste of money
18
19⚠️ WARNING FLAGS (need improvement, don't switch yet):
20
21Occasional context loss (< 20% of sessions)
22  → Can be mitigated with better session management
23
24Some output needs light editing (10-30%)
25  → Normal, but the CLAUDE.md needs improvement
26
27More junior developers can't adopt it
28  → Needs more onboarding support
29
30Slow for small tasks (rename, quick fix)
31  → Acceptable if the tool is good for complex tasks
32  → Combine with Copilot inline for small tasks

 1✅ STRONG GREEN FLAGS:
 2
 3The context file is followed consistently (> 90% of the time)
 4  → This tool understands your team's conventions
 5
 6The output immediately feels like it was written by a senior dev who knows the project
 7  → Context injection is working well
 8
 9The team starts to trust the output without verifying every line
10  → Genuine adoption, not forced
11
12PR review time drops 30-50% (because the AI pre-reviews)
13  → Quantifiable productivity improvement
14
15Test coverage rises (because the AI generates meaningful tests)
16  → The AI helps in areas developers often skip
17
18Developers volunteer stories about "the AI catching a bug I missed"
19  → Organic adoption and genuine appreciation
20
21The budget conversation shifts from "why are we paying for this?"
22to "how do we maximize its use?"
23  → ROI is already obvious without needing to justify it

 1Triggers to re-evaluate:
 2
 3IMMEDIATE SWITCH:
 4□ Tool shutdown or a drastic price increase (2×+)
 5□ Security breach or privacy issue at the vendor
 6□ Adoption drops drastically for no clear reason
 7
 86-MONTH RE-EVALUATE:
 9□ A new tool benchmarks far better for your use case
10□ Codebase size changes drastically (< 10K → 100K+ lines)
11□ Team composition changes drastically (junior → senior or vice versa)
12□ Strategic shift (new on-premise compliance requirement)
13
14DO NOT SWITCH JUST BECAUSE:
15□ Another tool has a new feature going viral on Twitter
16□ A benchmark from a biased source
17□ One developer who doesn't like it (make sure it's not just one person)
18□ A "grass is greener" feeling without concrete data

 1AI CODING TOOL EVALUATION SCORECARD
 2Project: [project name]
 3Team: [team name]
 4Date: [date pilot completed]
 5Tools evaluated: [Tool A] vs [Tool B]
 6
 7═══════════════════════════════════════
 8DIMENSION 1: WORKFLOW FIT (max 30 pts)
 9═══════════════════════════════════════
10Terminal vs IDE preference match:   Tool A: __ / Tool B: __ (max 10)
11Spec-first workflow alignment:       Tool A: __ / Tool B: __ (max 10)
12Multi-file editing capability:       Tool A: __ / Tool B: __ (max 10)
13Subtotal:                            Tool A: __ / Tool B: __ (max 30)
14
15════════════════════════════════════════
16DIMENSION 2: TECHNICAL FIT (max 30 pts)
17════════════════════════════════════════
18Go idiom correctness (from pilot):   Tool A: __ / Tool B: __ (max 10)
19Codebase size handling:              Tool A: __ / Tool B: __ (max 10)
20Integration requirements met:        Tool A: __ / Tool B: __ (max 10)
21Subtotal:                            Tool A: __ / Tool B: __ (max 30)
22
23══════════════════════════════════════
24DIMENSION 3: TEAM FIT (max 20 pts)
25══════════════════════════════════════
26Team size alignment:                 Tool A: __ / Tool B: __ (max 7)
27Adoption ease (from early adopters): Tool A: __ / Tool B: __ (max 7)
28Onboarding / governance:             Tool A: __ / Tool B: __ (max 6)
29Subtotal:                            Tool A: __ / Tool B: __ (max 20)
30
31═══════════════════════════════════════
32DIMENSION 4: BUDGET FIT (max 10 pts)
33═══════════════════════════════════════
34Monthly TCO vs budget:               Tool A: __ / Tool B: __ (max 5)
35ROI confidence:                      Tool A: __ / Tool B: __ (max 5)
36Subtotal:                            Tool A: __ / Tool B: __ (max 10)
37
38══════════════════════════════════════
39DIMENSION 5: STRATEGIC (max 10 pts)
40══════════════════════════════════════
41Enterprise growth path:              Tool A: __ / Tool B: __ (max 4)
42Vendor independence:                 Tool A: __ / Tool B: __ (max 3)
43SDD / AI roadmap alignment:          Tool A: __ / Tool B: __ (max 3)
44Subtotal:                            Tool A: __ / Tool B: __ (max 10)
45
46═══════════════════════
47TOTAL (max 100 pts)
48═══════════════════════
49                        Tool A: __ / Tool B: __
50
51DECISION: ______________ (with a brief rationale)

 1Case study: The Santekno Shop team (fictional)
 2
 3Month 1:
 4  The team sees Cursor go viral on Twitter → adopts Cursor without a pilot
 5
 6Month 3:
 7  Discovery: The team is mostly terminal-based (dislikes the IDE)
 8  Cursor adoption: 30% (the majority don't use it)
 9  Cost: $20/dev/month × 8 developers = $160/month = $480 wasted
10
11After a proper pilot (Month 4):
12  Pilot: Cursor vs Claude Code
13  Discovery: The team prefers the CLI, the SDD workflow fits better
14  Decision: Claude Code
15  Adoption: 90%
16  ROI: significant within the first 2 months
17
18Lesson: A 2-week pilot is far cheaper than 3 months of failed adoption.

 1# ADR-001: AI Coding Tool Selection
 2
 3## Date
 4[Decision date]
 5
 6## Status
 7Accepted
 8
 9## Context
10The [team name] team ([count] developers) needs an AI coding tool to 
11improve Golang development productivity on Santekno Shop.
12
13Codebase: ~[size] lines, Go [version], Clean Architecture.
14
15Pilot period: [date] to [date]
16Tools evaluated: [Tool A] and [Tool B]
17
18## Decision
19We chose **[tool name]**.
20
21Scorecard results:
22- Tool A: [score]/100
23- Tool B: [score]/100
24
25Key decision factors:
261. [Factor 1 — example: The team are terminal-comfortable developers]
272. [Factor 2 — example: The SDD workflow is running and needs a tool that aligns]
283. [Factor 3 — example: A budget of $30-50/dev/month is acceptable]
29
30## Consequences
31
32Positive:
33- [Expected benefit 1]
34- [Expected benefit 2]
35
36Negative / Trade-offs:
37- [Trade-off 1 — example: CLI-only, needs additional Copilot for inline]
38- [Trade-off 2]
39
40## Review Date
41[6 months from now] — re-evaluate based on actual data.

 1Mistake 1: Adopt a tool without a pilot
 2  Impact: 3 months of failed adoption, $500+ wasted
 3  Solution: Always pilot at least 4 weeks with 2-3 early adopters
 4
 5Mistake 2: One developer decides for everyone
 6  Impact: A tool that fits one person but not the team
 7  Solution: Pilot involving at least 2-3 developers with different skill levels
 8
 9Mistake 3: A minimal context file setup
10  Impact: Generic AI output, needs heavy editing
11  Solution: Invest 2-3 hours in a comprehensive CLAUDE.md
12
13Mistake 4: Not tracking metrics
14  Impact: Can't justify or challenge the decision with data
15  Solution: A simple spreadsheet, track 3-5 meaningful metrics
16
17Mistake 5: "Set it and forget it"
18  Impact: The tool becomes outdated, the team doesn't maximize value
19  Solution: Monthly brief review, quarterly deep review
20
21Mistake 6: Not training the team
22  Impact: 40% of the capability used from a powerful tool
23  Solution: 2-hour onboarding session + pair session with the champion

 1PHP/Laravel background → migrating to Go:
 2
 3Challenges that often arise:
 4- PHP developers are used to Laravel "magic" (Eloquent, facades, etc.)
 5- Go is very explicit — AI generating Go code needs more context
 6- Error handling in Go is very different from PHP try/catch
 7
 8Tool recommendations for migrating PHP devs:
 9
101. GitHub Copilot (easiest):
11   - Smooth inline suggestions similar to PHP tooling
12   - `/explain` is very helpful for understanding Go patterns
13   - "@workspace how does Go handle what I do with X in PHP?"
14
152. Claude Code (most comprehensive):
16   - Can explain Go idioms from a PHP perspective:
17     "In Laravel I usually use soft delete with the SoftDeletes trait.
18      What's the equivalent in Go with Clean Architecture?"
19   - CLAUDE.md can include a Laravel-to-Go mapping
20
213. Cursor with a PHP-aware .cursorrules:
22   .cursorrules can include:
23   "For developers from a PHP/Laravel background:
24   - Eloquent not-found → Go: return nil, nil from the repository
25   - Laravel exceptions → Go: domain error types
26   - PHP try/catch → Go: explicit error return"

 1Before committing to a single tool:
 2
 3□ Have you run a pilot of at least 3 weeks?
 4□ Have you tested with 2+ developers (not just one)?
 5□ Have you set up an equivalent context file for all tools tested?
 6□ Have you tracked at least 3 metrics objectively?
 7□ Have you interviewed early adopters for qualitative feedback?
 8□ Have you calculated the TCO and compared it to expected ROI?
 9□ Have you considered the tool for the next 12 months (not just now)?
10□ Do you have a plan to onboard the whole team if you adopt it?
11□ Have you documented the decision (ADR) for future reference?
12□ Have you set a review date (6 months from now)?