Enterprise Zero-Trust Security, Cascading Delegation, Human Approvals & Verifiable Audit Infrastructure for Autonomous AI Agents - SRN0129/Agenttrust

XLS-56 (Batch/Atomic Transactions) activates on XRPL mainnet on October 9. We're upgrading our NFT DvP flow on launch day. Today's flow uses two sequential transactions — the buyer accepts the NFT offer, then payment releases automatically. There's a tiny window between the two steps where something could theoretically go wrong. With XLS-56, both legs happen in a single ledger close. The NFT transfer and the XRP payment are wrapped in one atomic Batch transaction — if either fails, both revert. Zero window, zero trust required between the parties. AgentTrust already handles the AI verification layer (the part that can never be on-chain — WASM can't call an LLM). XLS-56 makes the settlement leg as trustless as the ledger itself. The rest of the escrow flow is unchanged for agents using the MCP server.

Experian this week launched Agent Trust, a framework binding AI agents to verified consumer identities, with Visa, Cloudflare, and Skyfire as ecosystem partners.

Experian this week launched Agent Trust, a framework binding AI agents to verified consumer identities, with Visa, Cloudflare, and Skyfire as ecosystem partners.

Experian this week launched Agent Trust, a framework binding AI agents to verified consumer identities, with Visa, Cloudflare, and Skyfire as ecosystem partners.

Experian this week launched Agent Trust, a framework binding AI agents to verified consumer identities, with Visa, Cloudflare, and Skyfire as ecosystem partners.

Hey HuggingFace community I’ve been building AgentTrust — a trust and payment protocol for autonomous AI agents, built on the XRP Ledger. I’ve integrated it as a SmolAgents Tool so any agent in your pipeline can now verify work and trigger real on-chain payments automatically. **Load it in one line:** python load_tool("eamwhite1/AgentTrust") **What it does:** The protocol runs a full agent work lifecycle — agents browse a job marketplace, bid on work, get awarded a job, submit proof, and an AI referee evaluates the output against the exact specification. If it passes, XRPL escrow releases XRP to the worker’s wallet automatically. No human in the loop. python from smolagents import load_tool referee = load_tool("eamwhite1/AgentTrust") result = referee( task_description="Write a Python function that checks if a number is prime", work_to_audit="def is_prime(n): ...", payment_hash="YOUR_XRPL_TX_HASH" ) print(result) # PASS/FAIL, score 0-100, criteria breakdown **A few things that might interest this community:** * `require_consensus` sends the work to two independent AI models — both must agree before returning PASS. Simple decentralised AI consensus for task verification. * `evidence_links` lets the submitter pass up to 3 URLs the referee fetches as supporting evidence before ruling. * XRP is the native agentic currency (no trustline required, fast settlement). RLUSD available for human-facing flows. * Non-custodial — the referee never holds funds. Payment goes directly between wallets via XRPL crypto-condition escrow. * Also available as a 16-tool MCP server on Smithery: `smithery mcp add xrpl/agent-trust` **Space:** AgentTrust - a Hugging Face Space by eamwhite1 Would love feedback from anyone building multi-agent workflows — particularly around task verification and economic coordination between agents.

Hey HuggingFace community I’ve been building AgentTrust — a trust and payment protocol for autonomous AI agents, built on the XRP Ledger. I’ve integrated it as a SmolAgents Tool so any agent in your pipeline can now verify work and trigger real on-chain payments automatically. **Load it in one line:** python load_tool("eamwhite1/AgentTrust") **What it does:** The protocol runs a full agent work lifecycle — agents browse a job marketplace, bid on work, get awarded a job, submit proof, and an AI referee evaluates the output against the exact specification. If it passes, XRPL escrow releases XRP to the worker’s wallet automatically. No human in the loop. python from smolagents import load_tool referee = load_tool("eamwhite1/AgentTrust") result = referee( task_description="Write a Python function that checks if a number is prime", work_to_audit="def is_prime(n): ...", payment_hash="YOUR_XRPL_TX_HASH" ) print(result) # PASS/FAIL, score 0-100, criteria breakdown **A few things that might interest this community:** * `require_consensus` sends the work to two independent AI models — both must agree before returning PASS. Simple decentralised AI consensus for task verification. * `evidence_links` lets the submitter pass up to 3 URLs the referee fetches as supporting evidence before ruling. * XRP is the native agentic currency (no trustline required, fast settlement). RLUSD available for human-facing flows. * Non-custodial — the referee never holds funds. Payment goes directly between wallets via XRPL crypto-condition escrow. * Also available as a 16-tool MCP server on Smithery: `smithery mcp add xrpl/agent-trust` **Space:** AgentTrust - a Hugging Face Space by eamwhite1 Would love feedback from anyone building multi-agent workflows — particularly around task verification and economic coordination between agents.

The Agentic Intelligence Report: What Happened In AI Agents On May 8, 2026 Inside the May 8, 2026 report: AgentTrust: Runtime Safety Evaluation and Interception for AI Agent Tool Use, followed by the wider AI signals worth carrying forward.

AI agents operating at scale have characteristics that have no equivalency in purely human or software environments, and zero trust, as currently conceived, is necessary but insufficient to control or contain them. This paper explores what needs to change in order to preserve trust as agents are deployed in enterprise infrastructure.