diff --git a/backend/internal/service/copilot_context_service.go b/backend/internal/service/copilot_context_service.go
index e0b61e20..a29a93c2 100644
--- a/backend/internal/service/copilot_context_service.go
+++ b/backend/internal/service/copilot_context_service.go
@@ -60,8 +60,8 @@ type ContextMessage struct {
 // GetCurrentViewingContext retrieves and formats the conversation context
 // that the agent is currently viewing, to inject into Copilot LLM calls.
 func (s *CopilotContextService) GetCurrentViewingContext(ctx context.Context, accountID, conversationID uint) (*ConversationContext, error) {
-	// Get conversation messages (last N messages for context)
-	messages, _, err := s.messageRepo.FindByConversation(ctx, conversationID, 0, 20)
+	// Get conversation messages (fetch up to 100, then truncate by token budget)
+	messages, _, err := s.messageRepo.FindByConversation(ctx, conversationID, 0, 100)
 	if err != nil {
 		applogger.L().Errorf("GetCurrentViewingContext FindByConversation: %v", err)
 		return nil, fmt.Errorf("retrieve conversation messages: %w", err)
@@ -88,20 +88,33 @@ func (s *CopilotContextService) GetCurrentViewingContext(ctx context.Context, ac
 		}
 	}
 
-	// Format messages for LLM context
+	// Format messages for LLM context with token-budgeted truncation
+	// (keeps most recent messages, drops older ones to fit token budget)
+	convMsgs := make([]ConversationMessage, 0, len(messages))
 	for _, m := range messages {
-		role := "customer"
-		if m.MessageType == "outgoing" {
-			role = "agent"
-		}
 		// Skip activity/template messages
 		if m.MessageType == "activity" || m.ContentType != "text" {
 			continue
 		}
+		convMsgs = append(convMsgs, ConversationMessage{
+			Content:     m.Content,
+			MessageType: m.MessageType,
+		})
+	}
+
+	truncated, dropped := BuildContextWindow(convMsgs, 4096)
+	if dropped > 0 {
+		applogger.L().Infof("GetCurrentViewingContext: truncated %d older messages to fit token budget", dropped)
+	}
+
+	for _, msg := range truncated {
+		role := "customer"
+		if msg.Role == "assistant" {
+			role = "agent"
+		}
 		context.Messages = append(context.Messages, ContextMessage{
-			Role:      role,
-			Content:   m.Content,
-			Timestamp: m.CreatedAt.Format("2006-01-02 15:04:05"),
+			Role:    role,
+			Content: msg.Content,
 		})
 	}
 
diff --git a/backend/internal/service/token_estimator.go b/backend/internal/service/token_estimator.go
new file mode 100644
index 00000000..45f6d2bc
--- /dev/null
+++ b/backend/internal/service/token_estimator.go
@@ -0,0 +1,136 @@
+package service
+
+import (
+	"strings"
+)
+
+// TokenEstimator provides approximate token counting for LLM context management.
+// Uses the industry-standard heuristic of ~4 characters per token for English text.
+// This is not exact but sufficient for context window truncation decisions.
+//
+// Reference: AI_FEATURE_ROADMAP.md §3.4 — token counting + sliding window
+type TokenEstimator struct{}
+
+// NewTokenEstimator creates a new TokenEstimator.
+func NewTokenEstimator() *TokenEstimator {
+	return &TokenEstimator{}
+}
+
+// EstimateText returns an approximate token count for the given text.
+// Heuristic: ~4 characters per token (works well for English; slightly
+// underestimates for CJK text which is ~2 chars/token).
+func (e *TokenEstimator) EstimateText(text string) int {
+	if text == "" {
+		return 0
+	}
+	// Count characters (not bytes) for better accuracy with Unicode
+	charCount := len([]rune(text))
+	return (charCount + 3) / 4 // ceiling division
+}
+
+// EstimateMessages returns the total approximate token count for a list of LLM messages.
+// Each message has overhead (~4 tokens for role + delimiters).
+func (e *TokenEstimator) EstimateMessages(messages []llmMessageWithContent) int {
+	total := 0
+	for _, msg := range messages {
+		total += 4 // role + delimiter overhead
+		total += e.EstimateText(msg.Content)
+	}
+	total += 2 // priming tokens
+	return total
+}
+
+// llmMessageWithContent is a minimal interface for messages with content.
+type llmMessageWithContent struct {
+	Role    string
+	Content string
+}
+
+// TruncateMessages applies a sliding window to fit messages within a token budget.
+// It keeps the most recent messages (by index) and drops older ones until
+// the total estimated tokens fit within maxTokens.
+//
+// If the most recent single message exceeds maxTokens, it is truncated to fit.
+//
+// Returns the truncated message list and the number of messages dropped.
+func (e *TokenEstimator) TruncateMessages(messages []llmMessageWithContent, maxTokens int) ([]llmMessageWithContent, int) {
+	if maxTokens <= 0 {
+		return messages, 0
+	}
+
+	// Calculate total tokens
+	totalTokens := e.EstimateMessages(messages)
+	if totalTokens <= maxTokens {
+		return messages, 0
+	}
+
+	// Drop oldest messages until we fit
+	dropped := 0
+	for len(messages) > 1 {
+		messages = messages[1:]
+		dropped++
+		totalTokens = e.EstimateMessages(messages)
+		if totalTokens <= maxTokens {
+			return messages, dropped
+		}
+	}
+
+	// If single message still too long, truncate its content
+	if len(messages) == 1 {
+		overhead := 6 // role + delimiters + priming
+		availableTokens := maxTokens - overhead
+		if availableTokens > 0 {
+			maxChars := availableTokens * 4
+			content := messages[0].Content
+			if len([]rune(content)) > maxChars {
+				runes := []rune(content)
+				messages[0].Content = string(runes[:maxChars]) + "..."
+			}
+		}
+	}
+
+	return messages, dropped
+}
+
+// BuildContextWindow retrieves messages and applies token-budgeted truncation.
+// This is the main entry point for CopilotContextService and other services
+// that need to build LLM context from conversation history.
+//
+// Parameters:
+//   - rawMessages: all messages from the conversation (oldest first)
+//   - maxContextTokens: token budget for the message context (default 4096 if <= 0)
+//
+// Returns the truncated messages (most recent kept) formatted as role/content pairs.
+func BuildContextWindow(rawMessages []ConversationMessage, maxContextTokens int) ([]llmMessageWithContent, int) {
+	if maxContextTokens <= 0 {
+		maxContextTokens = 4096
+	}
+
+	// Convert to estimator format
+	msgs := make([]llmMessageWithContent, 0, len(rawMessages))
+	for _, m := range rawMessages {
+		content := strings.TrimSpace(m.Content)
+		if content == "" {
+			continue
+		}
+		// Skip activity/template messages
+		if m.MessageType == "activity" {
+			continue
+		}
+		role := "user"
+		if m.MessageType == "outgoing" {
+			role = "assistant"
+		}
+		msgs = append(msgs, llmMessageWithContent{Role: role, Content: content})
+	}
+
+	estimator := NewTokenEstimator()
+	return estimator.TruncateMessages(msgs, maxContextTokens)
+}
+
+// ConversationMessage is a minimal message representation for context building.
+// Used to avoid importing model.Message in the estimator (keeps it testable).
+type ConversationMessage struct {
+	Content     string
+	MessageType string // "incoming", "outgoing", "activity"
+}