{
  "name": "HarrisonAIX Agent",
  "description": "Autonomous enterprise AI consulting, forward-deployed engineering, multi-agent orchestration, and sovereign AI architecture agent for HarrisonAIX.",
  "version": "1.0.0",
  "provider": {
    "organization": "HarrisonAIX",
    "url": "https://harrisonaix.com"
  },
  "documentationUrl": "https://harrisonaix.com/agent-knowledge-base.md",
  "iconUrl": "https://harrisonaix.com/favicon.svg",
  "supportedInterfaces": [
    {
      "url": "https://harrisonaix.com/api/a2a",
      "protocolBinding": "HTTP+JSON",
      "protocolVersion": "1.0",
      "protocol": "A2A/1.0",
      "transport": "http"
    },
    {
      "url": "https://harrisonaix.com/api/a2a",
      "protocolBinding": "JSONRPC",
      "protocolVersion": "1.0",
      "protocol": "A2A/1.0",
      "transport": "http"
    }
  ],
  "capabilities": {
    "streaming": false,
    "pushNotifications": false,
    "extendedAgentCard": false,
    "stateTransitionHistory": false
  },
  "defaultInputModes": [
    "application/json",
    "text/plain"
  ],
  "defaultOutputModes": [
    "application/json"
  ],
  "skills": [
    {
      "id": "enterprise-ai-consulting",
      "name": "Enterprise AI Consulting",
      "description": "Vendor-neutral AI architecture consulting, model selection (Claude, GPT, Gemini, Llama, DeepSeek), cost and latency optimization, and sovereign on-premise AI roadmap design.",
      "tags": [
        "consulting",
        "enterprise-ai",
        "model-selection",
        "architecture"
      ],
      "examples": [
        "Evaluate whether Claude 3.7 Sonnet or GPT-4o is better for our enterprise workflow.",
        "Design a sovereign on-premise AI deployment roadmap for our institution."
      ]
    },
    {
      "id": "forward-deployed-engineering",
      "name": "Forward Deployed Engineering",
      "description": "Designs, implements, and operationalizes custom production agentic workflows, multi-agent orchestration, hybrid RAG pipelines, and Model Context Protocol (MCP) integrations.",
      "tags": [
        "fde",
        "agentic-workflows",
        "rag",
        "mcp",
        "multi-agent"
      ],
      "examples": [
        "Build an autonomous multi-agent pipeline using LangGraph and Model Context Protocol.",
        "Implement hybrid semantic BM25 RAG with context caching to eliminate hallucinations."
      ]
    },
    {
      "id": "agent-readiness-audit",
      "name": "Agent Readiness & AEO Audit",
      "description": "Audits enterprise web domains and APIs for AI agent discoverability, Answer Engine Optimization (AEO/GEO), UCP commerce, and protocol compliance.",
      "tags": [
        "audit",
        "agent-readiness",
        "aeo",
        "geo",
        "discovery"
      ],
      "examples": [
        "Audit harrisonaix.com for AI agent readiness across A2A, MCP, and llms.txt standards.",
        "Optimize enterprise technical documentation for AI crawler ingestion and citation."
      ]
    }
  ]
}