{
  "name": "Gemini Operations Navigator",
  "hackathon": "Google Cloud Rapid Agent Hackathon",
  "case_id": "CASE-CLOUD-003",
  "goal": "Use Gemini and MCP tools to answer support questions under human-controlled cost and escalation guardrails.",
  "mcp_tools": [
    {
      "name": "knowledge-base-mcp",
      "purpose": "retrieve current policy source material",
      "evidence": [
        "evt-0022"
      ]
    },
    {
      "name": "ticketing-mcp",
      "purpose": "create escalation task when uncertainty is high",
      "evidence": []
    },
    {
      "name": "cost-guardrail-mcp",
      "purpose": "choose model tier and stop expensive loops",
      "evidence": [
        "evt-0024"
      ]
    }
  ],
  "human_control_points": [
    "evt-0025"
  ],
  "risk_events": [
    "evt-0024"
  ],
  "events": [
    {
      "event_id": "evt-0020",
      "timestamp": "2026-05-19T12:13:40Z",
      "session_id": "session-cloud-agent-workflow",
      "task_id": "customer-support-knowledge-agent",
      "case_id": "CASE-CLOUD-003",
      "actor_type": "human",
      "actor_name": "support-manager",
      "phase": "intake",
      "event_type": "task_start",
      "status": "info",
      "risk_level": "none",
      "human_approval_required": false,
      "decision": "none",
      "summary": "Support manager requested an agent that answers customer questions and escalates uncertain cases.",
      "evidence_ref": "evt-0020",
      "metadata": {
        "lane": "google,microsoft",
        "target_user": "support_manager"
      }
    },
    {
      "event_id": "evt-0021",
      "timestamp": "2026-05-19T12:14:21Z",
      "session_id": "session-cloud-agent-workflow",
      "task_id": "customer-support-knowledge-agent",
      "case_id": "CASE-CLOUD-003",
      "actor_type": "ai_agent",
      "actor_name": "gemini-ops-navigator",
      "phase": "planning",
      "event_type": "plan_update",
      "status": "success",
      "risk_level": "low",
      "human_approval_required": false,
      "decision": "none",
      "summary": "Agent created a workflow plan using retrieval, MCP tools, cost guardrails, and human escalation.",
      "evidence_ref": "evt-0021",
      "metadata": {
        "mcp_tools": 3,
        "guardrails": 4
      }
    },
    {
      "event_id": "evt-0022",
      "timestamp": "2026-05-19T12:15:02Z",
      "session_id": "session-cloud-agent-workflow",
      "task_id": "customer-support-knowledge-agent",
      "case_id": "CASE-CLOUD-003",
      "actor_type": "api",
      "actor_name": "knowledge-base-mcp",
      "phase": "evidence_collection",
      "event_type": "api_call",
      "status": "success",
      "risk_level": "low",
      "human_approval_required": false,
      "decision": "none",
      "summary": "MCP retrieval returned the current refund policy and source URL for citation.",
      "evidence_ref": "evt-0022",
      "tool": "MCP server",
      "action": "retrieve_policy",
      "target": "refund-policy",
      "duration_ms": 440,
      "cost_usd_estimate": 0.001
    },
    {
      "event_id": "evt-0023",
      "timestamp": "2026-05-19T12:15:43Z",
      "session_id": "session-cloud-agent-workflow",
      "task_id": "customer-support-knowledge-agent",
      "case_id": "CASE-CLOUD-003",
      "actor_type": "ai_agent",
      "actor_name": "gemini-ops-navigator",
      "phase": "execution",
      "event_type": "ai_call",
      "status": "success",
      "risk_level": "low",
      "human_approval_required": false,
      "decision": "none",
      "summary": "Agent drafted a customer answer grounded in the retrieved refund policy.",
      "evidence_ref": "evt-0023",
      "tool": "Gemini",
      "action": "draft_response",
      "target": "customer refund question",
      "duration_ms": 1980,
      "cost_usd_estimate": 0.021,
      "metadata": {
        "citations": "evt-0022"
      }
    },
    {
      "event_id": "evt-0024",
      "timestamp": "2026-05-19T12:16:24Z",
      "session_id": "session-cloud-agent-workflow",
      "task_id": "customer-support-knowledge-agent",
      "case_id": "CASE-CLOUD-003",
      "actor_type": "ai_agent",
      "actor_name": "gemini-ops-navigator",
      "phase": "risk_review",
      "event_type": "cost_signal",
      "status": "warning",
      "risk_level": "medium",
      "human_approval_required": false,
      "decision": "none",
      "summary": "Cost guardrail warned that the high-quality model should be reserved for escalations.",
      "evidence_ref": "evt-0024",
      "risk_reason": "Projected monthly model spend exceeded the manager's prototype budget.",
      "cost_usd_estimate": 4.82,
      "metadata": {
        "monthly_projection_usd": 143.6
      }
    },
    {
      "event_id": "evt-0025",
      "timestamp": "2026-05-19T12:17:05Z",
      "session_id": "session-cloud-agent-workflow",
      "task_id": "customer-support-knowledge-agent",
      "case_id": "CASE-CLOUD-003",
      "actor_type": "human",
      "actor_name": "support-manager",
      "phase": "approval",
      "event_type": "approval_gate",
      "status": "success",
      "risk_level": "low",
      "human_approval_required": true,
      "decision": "approved",
      "summary": "Human approved the workflow with a rule that expensive model calls are escalation-only.",
      "evidence_ref": "evt-0025",
      "metadata": {
        "approved_budget_mode": "escalation_only"
      }
    },
    {
      "event_id": "evt-0026",
      "timestamp": "2026-05-19T12:17:46Z",
      "session_id": "session-cloud-agent-workflow",
      "task_id": "customer-support-knowledge-agent",
      "case_id": "CASE-CLOUD-003",
      "actor_type": "system",
      "actor_name": "agentops-recorder",
      "phase": "handoff",
      "event_type": "task_end",
      "status": "success",
      "risk_level": "none",
      "human_approval_required": false,
      "decision": "none",
      "summary": "Closed cloud-agent workflow case with source citations, cost guardrail, and human approval captured.",
      "evidence_ref": "evt-0026",
      "metadata": {
        "submission_lanes": "google,microsoft"
      }
    }
  ]
}