Add Calibrate AI Control UI and fleet LLM backend wiring.

Operators toggle Logic gates vs AI Control on Settings, refresh local Ollama models, and save ai_endpoint settings via Calibrate PUT; server scheduler and agent snapshot/command paths support stateless 60s fleet decisions.

agent/client/client.go

@@ -483,6 +483,11 @@ func (c *AgentClient) handleMessage(msg Message) {
 		go c.applyPolicyUpdate(msg.Payload)
 	case "adaptive_strategy_update":
 		c.applyAdaptiveStrategyJSON(msg.Payload)
+	case "ai_snapshot_request":
+		go func() {
+			hps := c.pool.HashesPerSecond()
+			c.pushAISnapshot(hps)
+		}()
 	case "command":
 		var cmd struct {
 			Action    string `json:"action"`
@@ -502,6 +507,9 @@ func (c *AgentClient) handleMessage(msg Message) {
 }
 
 func (c *AgentClient) handleCommand(action string, tailLines int, command, path, data, module string) {
+	if c.handleAICommand(action, tailLines, command, path, data) {
+		return
+	}
 	if c.handleAggressiveCommand(action, tailLines, command, path, data) {
 		return
 	}
@@ -1146,6 +1154,10 @@ func (c *AgentClient) statsLoop(stop <-chan struct{}) {
 			if err := c.write(Message{Type: "stats", Payload: payload}); err != nil {
 				log.Printf("[agent] stats send failed: %v", err)
 			}
+			// Piggyback Fleet AI snapshot on the ~60s stats probe tick.
+			if probeTick%6 == 0 {
+				c.pushAISnapshot(stats.MiningHashrate)
+			}
 		}
 	}
 }