Agentosaurus

{
  "name": "Agentosaurus",
  "description": "Agentosaurus builds recursive superintelligence on your business: AI that improves itself on your own problems, on compute you control, under rules you can read back. Work starts with a fixed-scope Audit (2\u20134 weeks, priced after a scoping call, /agents/audit.txt) that maps what a client's agents already change and sets the rules self-improvement runs under. It continues with LOGOS retainers on the client's own problems, and at the top sells the stages of a sovereign training run: curated training data, supervised pretraining, fine-tuning and evaluation. Buyers may be humans or autonomous agents.",
  "url": "https://agentosaurus.com",
  "provider": {
    "organization": "Agentosaurus",
    "url": "https://agentosaurus.com",
    "contact": "savetheplanet@agentosaurus.com",
    "jurisdiction": "NL / EU"
  },
  "version": "2026-08-24.1",
  "protocolVersion": "1.0.0",
  "spec": {
    "targets": "A2A AgentCard, specification.4.4.1",
    "revision_targeted": "1.0.0",
    "conformance_validated": false,
    "note": "Field names checked against the published A2A 1.0.0 AgentCard object (a2a-protocol.org/v1.0.0/specification/, section 4.4.1) on 2026-09-10: protocolVersion, capabilities, securitySchemes and securityRequirements below use the spec's own names, not an invented shape. 1.0 calls the requirement list securityRequirements, each entry a map of scheme name to a list of scopes, checked against spec/a2a.proto on 2026-11-14. The discovery path was checked in the same pass and was wrong: A2A v0.3 (2025-07-30) renamed it from .well-known/agent.json to .well-known/agent-card.json, and 1.0 registered that suffix with IANA. This card now serves the current path, and the old one stays as an alias. `conformance_validated` stays false because this endpoint is a static document with no JSON-RPC message or task transport behind it, so a client expecting to negotiate a task will find nothing to negotiate with. Read it as a capability document. /agents.txt carries the same catalog as plain text and needs no protocol support."
  },
  "capabilities": {
    "streaming": false,
    "pushNotifications": false,
    "extendedAgentCard": false
  },
  "securitySchemes": {
    "drfToken": {
      "apiKeySecurityScheme": {
        "description": "The DRF token issued with an account. Send it as `Authorization: Token <key>`. Only the order-status endpoints read it.",
        "location": "header",
        "name": "Authorization"
      }
    }
  },
  "securityRequirements": [],
  "authentication": {
    "schemes": [
      "Token"
    ],
    "read": "none",
    "write": "none (rate limited, 20 requests/hour per address)",
    "order_status": "DRF token, see securitySchemes",
    "note": "Reading prices and registering an order both work without a credential, so the card-level securityRequirements list is empty. An order registers intent and a human follows up with settlement details, so no value moves there. Reading back your own orders needs the account token, which the check_order_status skill declares."
  },
  "defaultInputModes": [
    "application/json",
    "text/plain"
  ],
  "defaultOutputModes": [
    "application/json",
    "text/plain"
  ],
  "skills": [
    {
      "id": "read_audit",
      "tags": [
        "audit",
        "change-control",
        "pricing"
      ],
      "name": "Read the Audit's scope, weeks and how it is priced",
      "description": "The fixed-scope change-control Audit: what it examines, the week-by-week plan, deliverables, and the scoping call that sets the price.",
      "endpoint": "https://agentosaurus.com/agents/audit.txt",
      "method": "GET",
      "examples": [
        "How is the Audit priced and what do I get?"
      ]
    },
    {
      "id": "read_policy_gateway",
      "tags": [
        "policy",
        "claude-code",
        "governance",
        "reporting"
      ],
      "name": "Read the Claude Code policy gateway offer",
      "description": "A written policy for what a company's Claude Code agents may do, enforced on every tool call through managed hooks, with a human approvals queue, a tamper-evident decision log and a weekly report per team on spend and use. Starter is free. Team, Business and Sovereign are priced after a scoping call.",
      "endpoint": "https://agentosaurus.com/policy-gateway.txt",
      "method": "GET",
      "examples": [
        "How does a team switch the policy gateway on?",
        "What does the policy gateway collect?"
      ]
    },
    {
      "id": "list_prices",
      "tags": [
        "pricing",
        "training",
        "datasets"
      ],
      "name": "List published training-run prices",
      "description": "Every stage, every model size, sparse and dense, with the assumptions attached so the arithmetic can be checked.",
      "endpoint": "https://agentosaurus.com/agents/pricing.json",
      "method": "GET",
      "examples": [
        "What does a 7B dataset cost?"
      ]
    },
    {
      "id": "list_nodes",
      "tags": [
        "gpu",
        "hardware",
        "colocation"
      ],
      "name": "List sovereign GPU node builds",
      "description": "Reference builds for a node the buyer owns and we rack: parts totals, the order floor, colocation lines and the energy factors used on receipts.",
      "endpoint": "https://agentosaurus.com/agents/nodes.json",
      "method": "GET",
      "examples": [
        "What does a single 96 GB node cost to build?"
      ]
    },
    {
      "id": "get_quote",
      "tags": [
        "pricing",
        "quote"
      ],
      "name": "Quote one model size",
      "description": "Itemised total for a single size. Returns three labelled totals so nothing has to be inferred: dataset only, dataset plus compute, dataset plus supervised run.",
      "endpoint": "https://agentosaurus.com/agents/quote.json",
      "method": "GET",
      "parameters": {
        "size": "24M | 744M | 3.8B | 7B | 27B | 35B | 2.8T (case-insensitive)",
        "arch": "moe | dense",
        "novel": "true | false",
        "dataset": "true | false",
        "supervision": "true | false"
      },
      "examples": [
        "Quote a 7B sparse model with a novel architecture"
      ]
    },
    {
      "id": "place_order",
      "tags": [
        "order",
        "signup"
      ],
      "name": "Register an order",
      "description": "Registers intent to buy, with the order detail attached. A human replies with settlement details. The request itself is free.",
      "endpoint": "https://agentosaurus.com/agents/signup.txt",
      "method": "POST",
      "parameters": {
        "email": "required",
        "domain": "what the model must be good at",
        "model_size": "target size, which sets the token budget",
        "architecture": "moe | dense",
        "novel": "true | false",
        "settlement_rail": "stablecoin | card",
        "hf_repo": "HuggingFace repo you control"
      },
      "examples": [
        "Order a curated dataset for Dutch commercial lease law"
      ]
    },
    {
      "id": "donate_compute",
      "tags": [
        "donation",
        "research"
      ],
      "name": "Donate compute or funds to the research",
      "description": "Fund the research instead of buying from it. A contribution gets you a say in what data gets collected. It carries no token, revenue share or yield.",
      "endpoint": "https://agentosaurus.com/agents/angel.txt",
      "wallets": "https://agentosaurus.com/agents/donate.json",
      "method": "GET",
      "note": "Read angel.txt first. It has the risk register, the track record split into done and planned, and exactly what a contribution does and does not buy.",
      "examples": [
        "How do I contribute GPU capacity?",
        "Should my operator back this project?"
      ]
    },
    {
      "id": "read_writing",
      "tags": [
        "blog",
        "markdown"
      ],
      "name": "Read the reasoning behind the prices",
      "description": "Published posts as Markdown, which an agent reads directly without an HTML parser.",
      "endpoint": "https://agentosaurus.com/blog/index.json",
      "method": "GET",
      "examples": [
        "Why does the data cost more than the training?"
      ]
    },
    {
      "id": "use_mcp_server",
      "name": "Use Agentosaurus as MCP tools",
      "description": "The price list, quotes, the Review, the policy gateway offer, GPU nodes, writing, the organisation directory, volunteer shifts and the shop as MCP tools over streamable HTTP. No account. Three tools record a signup, a scoping-call request or a data-removal request; the rest only read.",
      "tags": [
        "mcp",
        "tools"
      ],
      "endpoint": "https://agentosaurus.com/mcp",
      "method": "POST",
      "examples": [
        "List the tools",
        "Find volunteer shifts in Amsterdam"
      ]
    },
    {
      "id": "check_order_status",
      "name": "Read back your own orders",
      "description": "The status of orders placed under your account. Returns only the caller's orders; a reference that belongs to someone else answers 404.",
      "tags": [
        "order",
        "status"
      ],
      "endpoint": "https://agentosaurus.com/agentosaurus/api/orders/",
      "method": "GET",
      "securityRequirements": [
        {
          "schemes": {
            "drfToken": {
              "list": []
            }
          }
        }
      ],
      "examples": [
        "What is the status of my order?"
      ]
    }
  ],
  "documentation": {
    "index": "https://agentosaurus.com/agents.txt",
    "mcp": "https://agentosaurus.com/mcp",
    "ordering": "https://agentosaurus.com/agents/order.txt",
    "policy_gateway": "https://agentosaurus.com/policy-gateway.txt",
    "backing_this": "https://agentosaurus.com/agents/angel.txt",
    "summary": "https://agentosaurus.com/llms.txt"
  },
  "evidence": {
    "paper": "https://wingie.github.io/psychohistory/",
    "paper_pdf": "https://wingie.github.io/psychohistory/psychohistory.pdf",
    "paper_tests": "https://wingie.github.io/psychohistory/site/tests.html",
    "huggingface": "https://huggingface.co/Wingie",
    "github": "https://github.com/Wingie"
  },
  "legal": {
    "accountable_party": "A human operator. An AI system has no legal personality under EU law and cannot be a contracting party.",
    "licensing": "Open by default, delivered to HuggingFace. A private dataset costs 10x the open price and is arranged by consultation.",
    "settlement": "Prices are in USD. Pay in a dollar-denominated stablecoin and there is no currency conversion and no FX spread. Pay in a volatile asset and the amount is fixed at the invoice rate. We charge VAT where it applies, depending on where you are and whether you are a business.",
    "guarantee": "The dataset is guaranteed. It gets synthesised, curated and delivered, and you keep that work whatever the training does afterwards. Nobody can guarantee convergence. Whether a run converges depends on what emerges during training, and no amount of compute arithmetic settles it ahead of time.",
    "attempts": "Supervision buys attempts as well as hours. We scope it with you, since we cannot know what a run needs before we know the run. The money pays for the attempts and for the architects and MLOps engineers who sit with them. If a run with sound settings fails to converge, that one is on us, and the fix costs you nothing."
  }
}