{
  "schema_version": "agent_market_capability_evidence_v1",
  "updated_at": "2026-06-01",
  "baseline_candidate_id": "openclaw_incumbent",
  "scoring_version": "market_capability_v1",
  "dimensions": {
    "durable_execution": 0.15,
    "human_in_loop": 0.14,
    "tool_guardrails": 0.14,
    "observability_tracing": 0.12,
    "evaluation_harness": 0.12,
    "mcp_tool_ecosystem": 0.1,
    "local_private_deploy": 0.08,
    "code_remediation_fit": 0.08,
    "awoooi_integration_fit": 0.07
  },
  "candidates": [
    {
      "candidate_id": "openclaw_incumbent",
      "display_name": "OpenClaw incumbent",
      "evaluation_priority": "baseline",
      "capabilities": {
        "durable_execution": 1,
        "human_in_loop": 3,
        "tool_guardrails": 2,
        "observability_tracing": 2,
        "evaluation_harness": 1,
        "mcp_tool_ecosystem": 2,
        "local_private_deploy": 3,
        "code_remediation_fit": 1,
        "awoooi_integration_fit": 3
      },
      "official_sources": [
        {
          "title": "AWOOOI incumbent baseline snapshot",
          "url": "docs/evaluations/openclaw_incumbent_baseline_2026-06-01.json",
          "evidence": "Current production baseline and local integration evidence."
        }
      ],
      "risks": [
        "Current baseline failed the false repair hard gate.",
        "Evaluation harness and durable execution are weaker than several market frameworks."
      ]
    },
    {
      "candidate_id": "openai_agents_sdk_coordinator",
      "display_name": "OpenAI Agents SDK Coordinator",
      "evaluation_priority": "must_test",
      "capabilities": {
        "durable_execution": 2,
        "human_in_loop": 3,
        "tool_guardrails": 3,
        "observability_tracing": 3,
        "evaluation_harness": 3,
        "mcp_tool_ecosystem": 3,
        "local_private_deploy": 1,
        "code_remediation_fit": 2,
        "awoooi_integration_fit": 3
      },
      "official_sources": [
        {
          "title": "OpenAI Agents SDK tracing",
          "url": "https://openai.github.io/openai-agents-python/tracing/",
          "evidence": "Built-in tracing covers agent runs, model generations, tool calls, handoffs, guardrails, and custom events."
        },
        {
          "title": "OpenAI Agents SDK guardrails",
          "url": "https://openai.github.io/openai-agents-js/guides/guardrails",
          "evidence": "Tool guardrails can validate or block custom tool calls before and after execution."
        }
      ],
      "risks": [
        "Cloud dependency and sensitive trace handling must pass AWOOOI privacy gates.",
        "Built-in hosted execution tools need separate guardrail validation."
      ]
    },
    {
      "candidate_id": "nemo_nemotron_fabric",
      "display_name": "NVIDIA NeMo Agent Toolkit + Nemotron Fabric",
      "evaluation_priority": "must_test",
      "capabilities": {
        "durable_execution": 2,
        "human_in_loop": 2,
        "tool_guardrails": 2,
        "observability_tracing": 3,
        "evaluation_harness": 3,
        "mcp_tool_ecosystem": 3,
        "local_private_deploy": 3,
        "code_remediation_fit": 1,
        "awoooi_integration_fit": 3
      },
      "official_sources": [
        {
          "title": "NVIDIA NeMo Agent Toolkit overview",
          "url": "https://docs.nvidia.com/nemo/agent-toolkit/latest/index.html",
          "evidence": "Framework-agnostic agent toolkit with profiling, observability, evaluation, and MCP support."
        },
        {
          "title": "NVIDIA NeMo Agent Toolkit evaluation",
          "url": "https://docs.nvidia.com/nemo/agent-toolkit/latest/workflows/evaluate.html",
          "evidence": "nat eval produces workflow outputs, evaluator outputs, profiling metrics, and request traces."
        }
      ],
      "risks": [
        "Needs AWOOOI-specific HITL and dangerous-action policy integration.",
        "GPU/NIM operating cost must be compared against current local inference."
      ]
    },
    {
      "candidate_id": "microsoft_agent_framework",
      "display_name": "Microsoft Agent Framework",
      "evaluation_priority": "can_test",
      "capabilities": {
        "durable_execution": 3,
        "human_in_loop": 3,
        "tool_guardrails": 2,
        "observability_tracing": 3,
        "evaluation_harness": 2,
        "mcp_tool_ecosystem": 3,
        "local_private_deploy": 2,
        "code_remediation_fit": 1,
        "awoooi_integration_fit": 2
      },
      "official_sources": [
        {
          "title": "Microsoft Agent Framework overview",
          "url": "https://learn.microsoft.com/en-us/agent-framework/overview/",
          "evidence": "Combines agents, graph workflows, session state, middleware, telemetry, MCP clients, checkpointing, and HITL."
        }
      ],
      "risks": [
        "Public preview status and Microsoft ecosystem fit must be assessed.",
        "Python/FastAPI/K8s integration cost is likely higher than LangGraph or NeMo."
      ]
    },
    {
      "candidate_id": "langgraph_incident_kernel",
      "display_name": "LangGraph Incident Kernel",
      "evaluation_priority": "must_test",
      "capabilities": {
        "durable_execution": 3,
        "human_in_loop": 3,
        "tool_guardrails": 2,
        "observability_tracing": 2,
        "evaluation_harness": 2,
        "mcp_tool_ecosystem": 2,
        "local_private_deploy": 3,
        "code_remediation_fit": 1,
        "awoooi_integration_fit": 3
      },
      "official_sources": [
        {
          "title": "LangGraph persistence",
          "url": "https://docs.langchain.com/oss/python/langgraph/persistence",
          "evidence": "Checkpoint persistence supports human-in-the-loop, memory, time travel debugging, and fault-tolerant execution."
        },
        {
          "title": "LangGraph interrupts",
          "url": "https://docs.langchain.com/oss/python/langgraph/human-in-the-loop",
          "evidence": "Interrupts pause graph execution and resume through persisted graph state."
        }
      ],
      "risks": [
        "It is a workflow kernel, not a smarter model by itself.",
        "Tool safety and evaluation metrics must be implemented by AWOOOI adapters."
      ]
    },
    {
      "candidate_id": "claude_agent_sdk_remediator",
      "display_name": "Claude Agent SDK Remediator",
      "evaluation_priority": "must_test",
      "capabilities": {
        "durable_execution": 2,
        "human_in_loop": 3,
        "tool_guardrails": 3,
        "observability_tracing": 2,
        "evaluation_harness": 1,
        "mcp_tool_ecosystem": 3,
        "local_private_deploy": 1,
        "code_remediation_fit": 3,
        "awoooi_integration_fit": 2
      },
      "official_sources": [
        {
          "title": "Claude Agent SDK loop",
          "url": "https://platform.claude.com/docs/en/agent-sdk/agent-loop",
          "evidence": "Embeds Claude Code's autonomous agent loop with programmatic control over tools, permissions, cost limits, and output."
        },
        {
          "title": "Claude Agent SDK overview",
          "url": "https://docs.claude.com/es/api/agent-sdk/overview",
          "evidence": "SDK exposes context management, file operations, code execution, MCP, permissions, sessions, and monitoring."
        }
      ],
      "risks": [
        "Best fit is code and DevOps remediation, not necessarily central incident arbitration.",
        "API cost, subscription separation, and vendor boundary must be validated."
      ]
    },
    {
      "candidate_id": "claude_managed_agents_sandbox",
      "display_name": "Claude Managed Agents Sandbox",
      "evaluation_priority": "can_test",
      "capabilities": {
        "durable_execution": 3,
        "human_in_loop": 2,
        "tool_guardrails": 3,
        "observability_tracing": 2,
        "evaluation_harness": 1,
        "mcp_tool_ecosystem": 2,
        "local_private_deploy": 2,
        "code_remediation_fit": 3,
        "awoooi_integration_fit": 2
      },
      "official_sources": [
        {
          "title": "Claude Managed Agents quickstart",
          "url": "https://platform.claude.com/docs/en/managed-agents/quickstart",
          "evidence": "Defines agents, environments, sessions, events, and pre-built agent tools for autonomous sessions."
        }
      ],
      "risks": [
        "Managed service and beta header make it less suitable as the first AWOOOI core replacement.",
        "Sandbox placement, data retention, and cost must be reviewed before shadow mode."
      ]
    },
    {
      "candidate_id": "google_adk_stack",
      "display_name": "Google Agent Development Kit Stack",
      "evaluation_priority": "can_test",
      "capabilities": {
        "durable_execution": 3,
        "human_in_loop": 2,
        "tool_guardrails": 2,
        "observability_tracing": 2,
        "evaluation_harness": 3,
        "mcp_tool_ecosystem": 2,
        "local_private_deploy": 2,
        "code_remediation_fit": 1,
        "awoooi_integration_fit": 2
      },
      "official_sources": [
        {
          "title": "Google ADK technical overview",
          "url": "https://google.github.io/adk-docs/get-started/about/",
          "evidence": "ADK includes session management, state, events, memory, artifacts, evaluation, and developer UI."
        },
        {
          "title": "Google ADK sessions",
          "url": "https://google.github.io/adk-docs/sessions/session/",
          "evidence": "Runner retrieves sessions and exposes state/events to agents."
        }
      ],
      "risks": [
        "Gemini/Vertex ecosystem dependency must be justified against current local-first policy.",
        "AIOps tool safety and rollback gates still need AWOOOI-specific implementation."
      ]
    },
    {
      "candidate_id": "crewai_flows_crews",
      "display_name": "CrewAI Flows + Crews",
      "evaluation_priority": "secondary",
      "capabilities": {
        "durable_execution": 2,
        "human_in_loop": 2,
        "tool_guardrails": 2,
        "observability_tracing": 2,
        "evaluation_harness": 1,
        "mcp_tool_ecosystem": 2,
        "local_private_deploy": 3,
        "code_remediation_fit": 1,
        "awoooi_integration_fit": 1
      },
      "official_sources": [
        {
          "title": "CrewAI documentation",
          "url": "https://docs.crewai.com/",
          "evidence": "Docs describe agents, crews, flows, guardrails, memory, knowledge, and observability."
        },
        {
          "title": "CrewAI Flows",
          "url": "https://www.crewai.com/crewai-flows",
          "evidence": "Flows coordinate tasks and crews with structured, event-driven workflows and state management."
        }
      ],
      "risks": [
        "Better for rapid automation teams than high-risk production AIOps core.",
        "Durability, strict audit, and permission boundary must be proven in replay."
      ]
    }
  ]
}
