How AI Coding Agents Work for Golang: Claude Code, Cursor, Copilot Explained

 1┌─────────────────────────────────────────────────────────────┐
 2│                    AI Coding Agent                           │
 3├────────────────────┬────────────────────────────────────────┤
 4│  1. LLM Core       │  Large Language Model — the agent's     │
 5│                    │  "brain" — Claude, GPT-4o, Gemini, etc. │
 6│                    │  Responsible for reasoning              │
 7│                    │  and generation                         │
 8├────────────────────┼────────────────────────────────────────┤
 9│  2. Context Engine │  What is fed to the LLM before you      │
10│                    │  type a single character                │
11│                    │  CLAUDE.md, relevant files,             │
12│                    │  conversation history, RAG results      │
13├────────────────────┼────────────────────────────────────────┤
14│  3. Tool Executor  │  The mechanism that lets the LLM take   │
15│                    │  action, not just generate text         │
16│                    │  read_file, write_file, run_command,    │
17│                    │  search_codebase, browse_web            │
18└────────────────────┴────────────────────────────────────────┘

 1[SYSTEM PROMPT — built by the tool, invisible to you]:
 2"You are an expert Go developer working on a production e-commerce system.
 3
 4[Content from CLAUDE.md]:
 5  Architecture: handler → usecase → repository → domain
 6  Error handling: fmt.Errorf("pkg.Method: %w", err)
 7  Monetary: int64 cents, never float64
 8  Go 1.22, pgx/v5, echo/v4
 9  ...
10
11[Auto-injected context — files the tool thinks are relevant]:
12  internal/delivery/http/handler/order_handler.go:
13    [full content of existing order handler]
14
15  internal/domain/order/entity.go:
16    [full content of domain entity]
17
18  internal/usecase/order/create_order.go:
19    [full content of create order as pattern reference]
20
21  go.mod:
22    [module name and dependency versions]
23
24[Optional context — varies by tool]:
25  Recent git diff (Cursor)
26  Open spec files (Claude Code)
27  Recent conversation history"
28
29[USER MESSAGE]:
30"Implement CancelOrder handler"

 1Estimated size in tokens:
 2
 3One medium Go file (300 lines):          ~3,000 tokens
 4One Go package (10 files):               ~30,000 tokens
 5A comprehensive CLAUDE.md:                ~2,000 tokens
 6Santekno Shop full codebase (~10K lines): ~80,000-120,000 tokens
 7
 8Context windows:
 9──────────────────────────────────────────────────────
10Claude Code:  200,000 tokens  → fits nearly an entire medium codebase
11Cursor:       ~100,000 tokens → smart selection needed
12Copilot:      ~64,000 tokens  → only a few main packages
13Gemini:     1,000,000 tokens  → fits nearly all of an enterprise codebase
14AWS Kiro:    ~200,000 tokens  → comparable to Claude Code
15Windsurf:    ~100,000 tokens  → comparable to Cursor

 1This line: return fmt.Errorf("pkg.Method: %w", err)
 2Is tokenized into something like:
 3["return", " fmt", ".Errorf", "(\"", "pkg", ".", "Method", ":", " %", "w", "\", err", ")"]
 4~13 tokens for 1 line of code
 5
 6Implication for cost calculation:
 7  Claude Sonnet: ~$3/1M input tokens, ~$15/1M output tokens
 8
 9Implement CancelOrder feature (estimate):
10  Input:  system prompt (~5K) + CLAUDE.md (~2K) + relevant code (~10K) = 17K tokens
11  Output: generated code (~3K) + explanation (~500) = 3.5K tokens
12  Cost:   17K × $3/1M + 3.5K × $15/1M ≈ $0.10 per feature

 1Sequence diagram for "Implement CancelOrder and run its tests":
 2
 3User: "Implement CancelOrder and run its tests"
 4    │
 5    ▼
 6LLM internal reasoning:
 7  "I need to read the existing code first to understand the patterns."
 8    │
 9    ▼
10LLM output: {
11    "type": "tool_use",
12    "name": "read_file",
13    "input": {"path": "internal/usecase/order/create_order.go"}
14}
15    │
16    ▼
17Tool Executor: reads the file, returns the content to the LLM
18    │
19    ▼
20LLM: "Good, I see the pattern. Now I'll implement."
21  {
22    "type": "tool_use",
23    "name": "write_file",
24    "input": {
25      "path": "internal/usecase/order/cancel_order.go",
26      "content": "package order\n\nimport ..."
27    }
28  }
29    │
30    ▼
31Tool Executor: writes the file
32    │
33    ▼
34LLM: "Now run the tests to verify."
35  {
36    "type": "tool_use",
37    "name": "run_command",
38    "input": {"command": "go test ./internal/usecase/order/..."}
39  }
40    │
41    ▼
42Tool Executor: runs the tests, returns the output to the LLM
43    │
44    ▼
45LLM: "Tests pass. Implementation complete."
46    │
47    ▼
48User sees: the final result

1MANUAL ◄─────────────────────────────────────────────► AUTONOMOUS
2
3Copilot         Cursor          Claude Code      AWS Kiro
4(suggestions,   (Composer:      (Plan mode:      (spec-driven,
5 accept/reject  show diff first, reason then     auto-execute
6 per line)      approve to      execute,         with hooks)
7                apply)          confirm per
8                                major step)

 1Simple task — autonomous OK:
 2  "Add context timeout to all repo methods"
 3  → the AI can directly edit and verify compile
 4
 5Task with business logic — semi-autonomous:
 6  "Implement cancel order"
 7  → the AI plans first, shows the approach, user approves, then executes
 8
 9Critical/risky task — manual preferred:
10  "Refactor the payment service"
11  → review every change, don't auto-apply anything
12
13Rules of thumb:
14  Reversible task (can git reset)  → higher autonomy OK
15  Irreversible or high-impact      → lower autonomy, more verification

 1RAG Pipeline for AI Coding:
 2
 3Step 1: INDEXING (done in the background, once or periodically)
 4  The entire codebase is parsed and chunked into small pieces
 5  Each chunk is converted to a vector embedding (a numeric representation)
 6  Vectors are stored in a local vector database
 7
 8Step 2: RETRIEVAL (every time there's a query)
 9  Your query is converted to a vector
10  The vector database searches for the most similar chunks
11  Returns the top-K most relevant chunks
12
13Step 3: AUGMENTATION (inject into context)
14  The retrieved chunks are injected into the LLM's context
15  Alongside CLAUDE.md, conversation history, and the currently open file
16
17Step 4: GENERATION (LLM processes)
18  The LLM generates a response based on the augmented context

 1Query: "Implement error handling in CancelOrder consistent with the codebase"
 2
 3RAG retrieves:
 4  → internal/usecase/order/create_order.go (similarity: high)
 5  → internal/domain/order/errors.go (similarity: very high)
 6  → internal/delivery/http/handler/order_handler.go (similarity: high)
 7  → internal/repository/postgres/order_repository.go (similarity: medium)
 8
 9NOT retrieved (correctly):
10  → internal/usecase/product/... (different domain)
11  → internal/kafka/... (different concern)

 1// Standard library — very well-represented:
 2import (
 3    "context"
 4    "fmt"
 5    "sync"
 6    "time"
 7)
 8
 9// Popular packages — well-represented:
10"github.com/labstack/echo/v4"
11"github.com/jackc/pgx/v5"
12"github.com/google/uuid"
13
14// Common Go patterns — knows well:
15// - Interface definition and implementation
16// - Error wrapping with fmt.Errorf
17// - Context propagation
18// - Struct embedding
19// - Goroutine lifecycle

 1// Your internal package — not in training data:
 2"github.com/santekno/santekno-shop/internal/domain/order"
 3
 4// Your team's conventions — not in training data:
 5// "For not-found from repository: return nil, nil"
 6// "HTTP error code: UPPERCASE_SNAKE_CASE"
 7
 8// The latest Go version — depends on the training cutoff:
 9// range-over-func (Go 1.22), iter.Seq (Go 1.23)
10// "This project is Go 1.22 — don't use 1.23+ features"

 1Currently generating cancel_order.go:
 2
 3Token 1: "package"
 4Token 2: " order"
 5Token 3: "\n\nimport"
 6Token 4: " (\n"
 7Token 5: "\t\"context\""
 8...
 9
10Each token is chosen based on a probability distribution.
11Example for the next token after "if err != nil {":
12  → "\n\t\treturn" : 45% probability (error return)
13  → "\n\t\tlog"    : 20% probability (logging)
14  → "\n\t\tpanic"  : 5% probability (panic — unusual in Go)
15  → others         : 30%
16
17The LLM chooses "return" because it has the highest probability
18AND because CLAUDE.md says "always use explicit error handling, never panic"

1Temperature controls "creativity" vs "determinism":
2
3Low temperature (0.1):   Output is very deterministic, consistent
4                         Ideal for: production code, test generation
5
6High temperature (0.8):  Output is more varied, creative
7                         Ideal for: brainstorming, documentation
8
9Default for most tools: 0.2-0.4 for coding tasks

1// The AI generates this with high confidence:
2import "github.com/pgx/v5/pgxutil"  // ← this package DOES NOT EXIST
3
4// The correct one:
5import "github.com/jackc/pgx/v5"    // ← exact module path
6
7// Prevention: write the exact import paths in CLAUDE.md
8// Recovery: always run `go build` after AI generation

 1A long conversation (>100 exchanges) can cause
 2the early part of the context (including CLAUDE.md) to "fall out" of the window.
 3
 4Symptoms:
 5  - AI generates float64 for monetary (even though CLAUDE.md says int64)
 6  - Error handling isn't wrapped
 7  - Wrong package path
 8
 9Recovery:
10  - Start a new session
11  - Re-state the most critical rules: "Remember: never float64 for money"
12  - Or: start with "Read CLAUDE.md and confirm the top 3 rules"

 1// Prompt: "Implement error handling"
 2// AI interprets: generic error handling without context
 3
 4// Better prompt:
 5// "Implement error handling per CLAUDE.md:
 6//  - Repository not-found: return nil, nil
 7//  - Error wrap: fmt.Errorf('pkg.Method: %w', err)
 8//  - Domain errors from internal/domain/errors/"
 9
10// The more specific the prompt → the more specific the output

1// If the majority of the training data uses database/sql:
2// the AI might generate database/sql code for Go
3// even though you use pgx/v5
4
5// Prevention in CLAUDE.md:
6// "NEVER use database/sql. ALWAYS use github.com/jackc/pgx/v5"

 1Without caching (every request pays full price):
 2  CLAUDE.md:                2,000 tokens × $3/1M    = $0.006
 3  Project context:         10,000 tokens × $3/1M    = $0.030
 4  User query:                 100 tokens × $3/1M    = $0.0003
 5  Total input: ~$0.036
 6
 7With prompt caching (claude-sonnet-4):
 8  CLAUDE.md (cached):       2,000 tokens × $0.30/1M  = $0.0006  (10x cheaper)
 9  Project context (cached): 10,000 tokens × $0.30/1M = $0.003
10  User query (not cached):    100 tokens × $3/1M     = $0.0003
11  Total input: ~$0.004  (90% cheaper!)

 1Total latency =
 2  Client-side preparation (build context, RAG retrieval): 50-500ms
 3  + Network round-trip to the API server:                 50-200ms
 4  + Time to First Token (TTFT):                           200-800ms
 5  + Generation speed:                                     50-100 tokens/second
 6  + Post-processing (format, apply):                      10-100ms
 7
 8For a 500-token response:
 9  ~300ms + ~100ms + ~400ms + 5,000ms + ~50ms ≈ 5.9 seconds
10
11For a 100-token response (a simple answer):
12  ~300ms + ~100ms + ~400ms + 1,000ms + ~50ms ≈ 1.85 seconds

 1# Batch related questions
 2# SLOWER:
 3> Implement function X
 4# [wait ~5s]
 5> Now add error handling
 6# [wait ~5s]
 7# Total: 10s
 8
 9# FASTER:
10> Implement function X with proper error handling, context timeout, and a unit test
11# [wait ~8s]
12# Total: 8s, and the output is more comprehensive

 1Terminal / Shell
 2     │
 3     ▼
 4Claude Code CLI (Node.js process)
 5     │
 6     ├── Load CLAUDE.md from the project root and parent dirs
 7     ├── Build context from git status, recent files
 8     ├── Implement the tool set: read, write, execute, search
 9     │
10     ▼
11Anthropic API (claude-sonnet-4)
12     │
13     ▼
14Response stream → Tool execution loop → Next LLM call → ...

 1Cursor IDE (Electron, VSCode-based)
 2     │
 3     ├── Active file + surrounding context (IDE-aware)
 4     ├── .cursorrules loaded from the project root
 5     ├── RAG index from the workspace (vector embeddings)
 6     ├── Recent conversation history
 7     │
 8     ▼
 9LLM Provider API (pluggable: Claude/GPT-4o/Cursor-1)
10     │
11     ▼
12Diff generator → Visual review → Apply to files

 1VS Code Extension / JetBrains Plugin
 2     │
 3     ├── Active file context (small window)
 4     ├── .github/copilot-instructions.md
 5     ├── Nearby files (heuristic selection)
 6     │
 7     ▼
 8GitHub Proxy → OpenAI/Anthropic API
 9     │
10     ▼
11Inline suggestion or Chat response
12
13+ PR Review Pipeline:
14  GitHub PR webhook → Copilot analysis → PR comment

 1SHORT-TERM MEMORY:
 2  Conversation history within a single session
 3  → Lost when the session is closed
 4  → All tools have this
 5
 6MEDIUM-TERM MEMORY:
 7  Project context files (CLAUDE.md, .cursorrules)
 8  → Persist across sessions via files
 9  → The developer maintains this explicitly
10
11LONG-TERM MEMORY:
12  Windsurf Cascade: semantic session memory across sessions
13  → More than just a file — remembers "what was worked on"
14  → Unique in 2026, no other tool is comparable yet
15
16EXTERNAL MEMORY:
17  RAG indexes (Cursor, Copilot Workspace)
18  → A vector database of the codebase
19  → Retrieved on-demand based on relevance

 1// WRONG — goroutine leak
 2go func() {
 3    result := heavyOperation()
 4    resultCh <- result  // If nobody reads, the goroutine leaks forever
 5}()
 6
 7// CORRECT — with context and proper lifecycle
 8func (w *Worker) processWithTimeout(ctx context.Context, job Job) error {
 9    doneCh := make(chan error, 1)
10
11    go func() {
12        doneCh <- w.process(ctx, job)
13    }()
14
15    select {
16    case err := <-doneCh:
17        return err
18    case <-ctx.Done():
19        return fmt.Errorf("worker.processWithTimeout: context cancelled: %w", ctx.Err())
20    }
21}

 1## Goroutine Safety
 2- Every goroutine must have a clear termination condition
 3- Use context.Context for cancellation propagation
 4- Buffered channels for "fire and forget" where appropriate
 5- sync.WaitGroup for tracking goroutine completion
 6- Never share memory without mutex or channel
 7
 8## Race Condition Prevention
 9- Run: go test -race for all concurrent code
10- Prefer channel communication over shared state
11- Use sync/atomic for simple counters
12- Prefer sync.RWMutex over sync.Mutex for read-heavy workloads

 1Claude Code:
 2  → Applies changes directly (or asks for confirmation on big changes)
 3  → Can run go build automatically if configured
 4  → Doesn't automatically format with go fmt (user responsibility)
 5
 6Cursor:
 7  → Generates a unified diff
 8  → Shows a visual review interface
 9  → Applies only after approval
10
11GitHub Copilot:
12  → Inline: instant apply (like autocomplete)
13  → Chat: shows a code block, manual apply
14  → PR Review: comment, doesn't auto-apply
15
16Windsurf:
17  → Similar to Cursor: shows changes, approve to apply
18
19Best practice after ANY AI generation for Go:
20  go build ./...      # catch compile errors
21  go vet ./...        # catch logical issues
22  go test -race ./... # catch race conditions
23  golangci-lint run   # catch style issues

 1# Step 1: Let the compiler speak
 2go build ./...
 3# Compiler errors are usually very specific and actionable
 4
 5# Step 2: Feed the error back to the AI
 6claude
 7> go build error:
 8> ./internal/usecase/order/cancel_order.go:45:15:
 9>   undefined: domain.ErrOrderNotFound
10>
11> The domain package is at internal/domain/order/errors.go.
12> Fix this.
13
14# Step 3: For a logical error (compiles but wrong behavior)
15go test ./internal/usecase/order/... -v
16
17# Step 4: For a race condition
18go test -race ./...
19
20# Step 5: If the AI is stuck (generating the same wrong code repeatedly)
21> Forget the previous implementation. Start from scratch.
22> The requirements are:
23> [re-state requirements more clearly]

 1# INEFFICIENT: 4 separate requests = 4x overhead
 2> Implement the Execute method
 3> Add error handling to Execute
 4> Write a test for Execute
 5> Add a godoc comment
 6
 7# EFFICIENT: 1 request
 8> Implement the Execute method with:
 9> - Proper error handling (fmt.Errorf wrap)
10> - Unit test (testify/suite + gomock)
11> - Godoc comment

1# INEFFICIENT: Explain the pattern from scratch
2> Error handling must wrap with fmt.Errorf and "%w"
3> and return a domain error for not-found...
4
5# EFFICIENT: Reference existing code
6> Implement CancelOrder following the EXACT same pattern
7> as CreateOrder at internal/usecase/order/create_order.go

1# AMBIGUOUS: the AI might touch too many files
2> Implement the cancel order feature
3
4# PRECISE: a clear scope
5> Implement internal/usecase/order/cancel_order.go
6> following spec.md ACs 1-6.
7> Only create/modify this one file.