executor: explicit load balancing — code picks agent, classifier picks model

pickAgent() deterministically selects the agent with the fewest active tasks,
skipping rate-limited agents. The classifier now only selects the model for the
pre-assigned agent, so Gemini gets tasks from the start rather than only as a
fallback when Claude's quota is exhausted.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

1 files changed, 56 insertions, 20 deletions

diff --git a/internal/executor/executor.go b/internal/executor/executor.go
index c04f68e..f54773a 100644
--- a/internal/executor/executor.go
+++ b/internal/executor/executor.go
@@ -343,30 +343,66 @@ func (p *Pool) ActiveCount() int {
 	return p.active
 }
 
-func (p *Pool) execute(ctx context.Context, t *task.Task) {
-	// 1. Classification
-	if p.Classifier != nil {
-		p.mu.Lock()
-		activeTasks := make(map[string]int)
-		rateLimited := make(map[string]bool)
-		now := time.Now()
-		for agent := range p.runners {
-			activeTasks[agent] = p.activePerAgent[agent]
-			if deadline, ok := p.rateLimited[agent]; ok && now.After(deadline) {
-				delete(p.rateLimited, agent)
-			}
-			rateLimited[agent] = now.Before(p.rateLimited[agent])
+// pickAgent selects the best agent from the given SystemStatus using explicit
+// load balancing: prefer the available (non-rate-limited) agent with the fewest
+// active tasks. If all agents are rate-limited, fall back to fewest active.
+func pickAgent(status SystemStatus) string {
+	best := ""
+	bestActive := -1
+
+	// First pass: only consider non-rate-limited agents.
+	for agent, active := range status.ActiveTasks {
+		if status.RateLimited[agent] {
+			continue
 		}
-		status := SystemStatus{
-			ActiveTasks: activeTasks,
-			RateLimited: rateLimited,
+		if bestActive == -1 || active < bestActive || (active == bestActive && agent < best) {
+			best = agent
+			bestActive = active
 		}
-		p.mu.Unlock()
+	}
+	if best != "" {
+		return best
+	}
+
+	// Fallback: all rate-limited — pick least active anyway.
+	for agent, active := range status.ActiveTasks {
+		if bestActive == -1 || active < bestActive || (active == bestActive && agent < best) {
+			best = agent
+			bestActive = active
+		}
+	}
+	return best
+}
 
-		cls, err := p.Classifier.Classify(ctx, t.Name, t.Agent.Instructions, status)
+func (p *Pool) execute(ctx context.Context, t *task.Task) {
+	// 1. Load-balanced agent selection + model classification.
+	p.mu.Lock()
+	activeTasks := make(map[string]int)
+	rateLimited := make(map[string]bool)
+	now := time.Now()
+	for agent := range p.runners {
+		activeTasks[agent] = p.activePerAgent[agent]
+		if deadline, ok := p.rateLimited[agent]; ok && now.After(deadline) {
+			delete(p.rateLimited, agent)
+		}
+		rateLimited[agent] = now.Before(p.rateLimited[agent])
+	}
+	status := SystemStatus{
+		ActiveTasks: activeTasks,
+		RateLimited: rateLimited,
+	}
+	p.mu.Unlock()
+
+	// Deterministically pick the agent with fewest active tasks.
+	selectedAgent := pickAgent(status)
+	if selectedAgent != "" {
+		t.Agent.Type = selectedAgent
+	}
+
+	if p.Classifier != nil {
+		cls, err := p.Classifier.Classify(ctx, t.Name, t.Agent.Instructions, status, t.Agent.Type)
 		if err == nil {
-			p.logger.Info("task classified", "taskID", t.ID, "agent", cls.AgentType, "model", cls.Model, "reason", cls.Reason)
-			t.Agent.Type = cls.AgentType
+			p.logger.Info("task classified", "taskID", t.ID, "agent", t.Agent.Type, "model", cls.Model, "reason", cls.Reason)
 			t.Agent.Model = cls.Model
 		} else {
 			p.logger.Error("classification failed", "error", err, "taskID", t.ID)