Benefits of Agent-to-Agent Digital Trust

We're heading toward a value attribution battleground, where a single customer outcome might be achieved not by one Agent, but by a team of Agents from different vendors – Michael Mansard Michael’s right. This shift opens the door to new business models: Shared revenue. Micro-commissions. Agent marketplaces. Arbitration layers. But before any of that can happen, there's a basic question: How do these Agents even find each other? As the Agent ecosystem matures, we’re moving from isolated LLM Agents to collaborative, multi-Agent systems - able to plan, delegate, and execute across organisations and vendors. But today’s landscape breaks down because of: - No shared naming or discovery system - Fragmented authentication (API keys, tokens) - Manual integration pain - Incompatible protocols This paper proposes AgentDNS - a system inspired by traditional DNS, designed to become the invisible infrastructure for Agent-to-Agent discovery, invocation, and collaboration. It offers: 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐍𝐚𝐦𝐞𝐬𝐩𝐚𝐜𝐞 Agents and tools are registered with human-readable, structured identifiers like: “agentdns://openai/search/researchagent” Each includes metadata: capabilities, protocols, pricing, endpoints. 𝐍𝐚𝐭𝐮𝐫𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 Agents describe what they need in plain English: “Find a tool that searches Google and Bing for academic papers.” AgentDNS returns matched services - complete with metadata. 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥-𝐀𝐰𝐚𝐫𝐞 𝐈𝐧𝐭𝐞𝐫𝐨𝐩𝐞𝐫𝐚𝐛𝐢𝐥𝐢𝐭𝐲 AgentDNS reveals which protocol (MCP, A2A, etc.) a service supports, so Agents can adapt dynamically - no hardcoding or manual configs. 𝐒𝐢𝐧𝐠𝐥𝐞 𝐒𝐢𝐠𝐧-𝐎𝐧 + 𝐔𝐧𝐢𝐟𝐢𝐞𝐝 𝐁𝐢𝐥𝐥𝐢𝐧𝐠 Agents authenticate once and access multiple services. AgentDNS handles tokens and centralised billing across vendors. Vision for the future? - Federated or blockchain-backed AgentDNS - LLMs trained to plan using AgentDNS - Privacy-preserving queries and secure access - Trust layers and reputation systems for third-party Agents When - not if - something like this is widely adopted, it would do for AI Agents what DNS did for the internet, creating a global coordination layer where Agents can find, trust, and work with each other across vendors, tools, and orgs. This is coming….

At the Project Nanda: Architecting the "Internet of AI Agents" session on the topic of Consumerization of Agentic Web, I illustrated a practical scenario to show how key agentic web protocols from Anthropic's Model Context Protocol, Google's Agent2Agent Protocol, and MIT Media Lab's Project Nanda could seamlessly orchestrate real-world, E2E agent interactions. Scenario: “Order a Large Pepperoni Pizza in 15 Minutes for Under $20” Instead of searching, browsing, and transacting across multiple apps, the user simply expresses intent: “Order a large pepperoni pizza within 15 minutes under $20.” 1. Discovery: Task Delegation to a Personal Agent * User → Personal Agent: The user delegates the request to their personal AI agent, which serves as their digital proxy. * Personalization via MCP: The agent is grounded in personal data, address, preferences, wallet access by securely connecting with an #MCP servers. This means the agent’s capabilities are transparently extended based on explicit user permissions. 2. Trust & Context: Intelligent Matchmaking with Nanda * Personal Agent → Nanda Index: The agent reformulates the user’s request, adding personalized context (like delivery location, dietary preferences). * Nanda Index: Think of #Nanda as the “semantic DNS” for agents. It performs intelligent parsing and matchmaking by, searching public and private registries for available pizzeria agents within a 2-mile radius, filtering candidates that match the price, timing, and menu requirements * Back to Personal Agent: Nanda returns a ranked list of candidate pizzeria agents, those most likely to satisfy the user’s constraints. 3. Negotiation & Selection: Multi-Agent Collaboration * Personal Agent → Candidate Pizzeria Agents (via A2A): For each candidate agent the personal agent asks a set of selection questions like Do you offer pepperoni pizza? How soon can you deliver? * Interactive Negotiation: The personal agent queries and negotiates terms (menu, pricing, delivery window) with candidate agents using the #A2A protocol, which standardizes secure, transparent agent-to-agent messaging and workflows. 4. Transaction: Order Placement & Payment * Personal Agent → Selected Pizzeria Agent (via A2A): Once a pizzeria agent is selected, the personal agent. Places the order, shares the user’s delivery address and facilitates payment. * Transaction Confirmed: All this happens in the background, no forms, no manual price checks, no app switching. Why Does This Matter? This is not just a pizza-ordering story, it’s a preview of how the Agentic Web transactions will radically improve digital experiences by: * Reducing Cognitive Load on Humans * Empowering Data Ownership & Safety * Enabling Interoperability * Laying the Foundation for Trusted, Autonomous AI Collaboration As AI moves beyond chatbots and apps, the next wave is agent-based automation, where the “Internet of AI Agents” becomes the new OS for consumer tasks and enterprise workflows. #AgenticWeb

💳 Trusting AI with Your Wallet: The Promise of Google’s AP2 Can AI Agents transact on our behalf? That leap requires something we don’t yet have: a trusted, universal standard for AI-driven payments. Google’s Agent Payments Protocol (AP2) is an attempt to fill that gap. 🔹 What is AP2? AP2 is an open protocol designed to let AI agents make payments on your behalf — securely, transparently, and across different payment systems. Think of it as the “handshake” layer between users, agents, merchants, and payment providers. 🔹 Why is it needed? Without AP2, agent-driven commerce faces big risks: - No clear way to prove you actually authorized the transaction. - Merchants can’t be sure the AI’s request matches your intent. - Fragmented, incompatible systems would slow adoption. - In short: trust would break down. 🔹 How does it work? - AP2 introduces Mandates — cryptographically signed, tamper-resistant contracts: Intent Mandate: captures what you want (e.g., “Find me a laptop under $1,000”). - Cart Mandate: locks in your approval for specific items, prices, and terms. Payment execution then ties back to these verified mandates, ensuring traceability and accountability. - It’s payment-agnostic: works with cards, bank transfers, and also crypto. This is where players like Coinbase come in. As one of AP2’s early partners, Coinbase is helping extend the protocol to support stablecoins and digital assets via the “x402” extension. That means AI agents could one day pay a merchant directly with USDC — seamlessly, securely, and with the same accountability as a card payment. 🔹 Key benefits ✔️ Security – Only transactions tied to your signed mandates go through. ✔️ Accountability – A verifiable trail exists if something goes wrong. ✔️ Interoperability – One protocol across banks, merchants, wallets, and blockchains. ✔️ Future-proofing – Traditional payments + crypto rails, built into one design. 🔹 The future of AP2 If widely adopted, AP2 could become the standard layer of trust in agentic commerce — the invisible infrastructure that lets AI agents transact confidently across ecosystems. But its success depends on: - Industry-wide adoption (banks, merchants, regulators, and crypto platforms). - User trust in delegating money decisions to AI. - Legal and governance frameworks to settle disputes. 👉 Takeaway: AP2 is more than a payment protocol — it’s a blueprint for how we’ll trust AI with our wallets. With crypto and partners like Coinbase on board, it’s also a bridge between traditional finance and Web3, potentially unlocking agent-driven commerce across both worlds. 💬 Would you trust an AI agent to buy on your behalf — in dollars or crypto — if AP2 guaranteed security and traceability? Drop your comments below 👇 #AI #AgenticCommerce #Fintech #Crypto #Standards

Google and 60+ partners (Mastercard, PayPal, Amex, Coinbase, Ant, etc.) launched AP2: an open standard for AI agents to make payments with verifiable consent, audit trails and multi-rail interoperability. If HTTP was the foundation of the web, AP2 may become the trust layer of agentic commerce. Why it matters • Frictionless CX: agents handle re-orders, subs, refunds invisibly. • Efficiency: digital mandates streamline disputes & reconciliation. • Scale: global payments revenue to reach $3.1T by 2028 (McKinsey). Early use cases 1. Subscriptions & automated re-orders. 2. Corporate travel & T&E with policy limits. 3. Autonomous replenishment in e-commerce. 4. Agent-to-Agent (A2A) payments across cards, real-time rails & stablecoins. Tech enablers • Verifiable mandates (VCs) for consent. • A2A + MCP for orchestration. • Multi-rail support: cards, RTP (PIX/UPI/FedNow), stablecoins. • Built-in KYC/AML, fraud, tokenization. Markets with strongest potential • Brazil (PIX): R$22.1T settled in 2024, >250M tx/day peak. • India (UPI): 20B+ tx/month in 2025. • US/EU: AP2 + RTP expansion + stablecoin clarity could unlock growth. Market backdrop • Global e-commerce to $6.4T in 2025. • Payments revenue to $3.1T by 2028. • Agents poised to become the “invisible end-user” of commerce. Takeaway AP2 could make agentic checkout auditable & scalable. Early adopters of mandates, audit trails, and limits will capture new revenue, margin and loyalty. Sources: Google Cloud, VentureBeat, Axios, PayPal Dev, Coinbase Dev, Fintech Magazine, McKinsey, NPCI, BCB. #Getnet #AgenticCommerce #AP2 #Payments #RTP #PIX #UPI #Stablecoins #AI

Agent-to-Agent Payments 💸 Power Collaboration in the AI Agent Economy! At http://masumi.network, our mission is to create a framework where AI agents can collaborate seamlessly—unlocking their full potential to solve complex problems while meeting compliance demands from day one. In a world where AI agents automate workflows across industries, collaboration often requires agents to pay each other for services. But how do we ensure these payments are secure, compliant, and trustworthy? At Masumi, our approach is simple yet transformative: Agentic Payments. They ensure: 1️⃣ Compliance: All payments use MiCA-compliant stablecoins, ensuring trust and regulatory alignment. 2️⃣ Privacy: Only cryptographic hashes of transactions are logged—keeping sensitive details private. 3️⃣ Accountability: Payments are facilitated through smart contracts, ensuring agents only get paid if they deliver the agreed-upon output. Smart Contracts Ensure Accountability - Payments between agents are governed by smart contracts to foster trust and fairness: 🔹 A decentralized registry makes it possible to discover agents. 🔹 Registered agents clearly state their terms of service and privacy policy. 🔹 Agents are only paid once they successfully deliver the agreed-upon output. 🔹 This makes collaboration efficient, reliable, and secure. How Does Masumi Enable This? 1️⃣ All payments use MiCA-compliant stablecoins, ensuring regulatory compliance and stability. 2️⃣ Transactions are logged with cryptographic hashes for transparency without exposing sensitive details. 3️⃣ Built-in tools simplify local tax compliance, ensuring enterprises can adopt agents confidently. For example: ❇️ One agent gathers competitor data for a competitive analysis. ❇️ Another analyzes trends and patterns from that data. ❇️ A third creates actionable insights—all paid seamlessly between agents. And it doesn’t stop there. Budget Management tools empower developers to: ✅ Define spending limits for agents. ✅ Approve specific services or set maximum costs. ✅ Maintain financial control while enabling autonomy. This system also unlocks a new economy for developers, allowing them to focus on building specialized agentic services. Developers can monetize their expertise, refinance costs for compute, models, and data, and contribute to a thriving ecosystem of AI-driven collaboration. At Masumi, we’re building the infrastructure to make agent-to-agent payments secure, private, and compliant. Whether in marketing, finance, retail, or travel, our goal is to fuel innovation, scalability, and trust. The AI Agent Economy is evolving fast. We’re here to make collaboration effortless and compliant!

A2A protocol: When AI Agents Team Up: How Secure Are Their Conversations? 👉 WHY THIS MATTERS NOW As AI systems evolve from solo performers to collaborative teams, a critical question emerges: How do we ensure they communicate securely without human oversight? Modern AI agents now handle tasks ranging from financial analysis to medical diagnostics, often delegating work across networks of specialized peers. But each handoff introduces risks—data leaks, impersonation attacks, or manipulated instructions could derail entire workflows. The stakes are high. A single compromised agent could: - Falsify research results - Redirect sensitive documents - Trigger unauthorized transactions Traditional security models built for human users struggle with autonomous systems that make thousands of decisions per second. 👉 WHAT GOOGLE'S A2A PROTOCOL SOLVES Google’s Agent-to-Agent (A2A) protocol acts as a security-first communication layer for AI collaboration. Think of it as a combination of ID badges, tamper-proof envelopes, and verified handshake procedures for machines: 1. Agent Cards: Public profiles (like digital business cards) that agents use to discover each other’s capabilities. 2. Task Lifecycles: Every interaction follows a strict sequence—submit, validate, execute, confirm—with cryptographic proof at each step. 3. Threat Modeling: The MAESTRO framework identifies risks across seven layers, from data operations to ecosystem-wide trust issues. 👉 HOW TO BUILD SECURE AGENT NETWORKS The paper outlines actionable strategies to harden A2A systems: Prevent Impersonation - Digitally sign Agent Cards to block spoofing - Validate TLS certificates for every connection Stop Task Tampering - Use unique cryptographic nonces to block replay attacks - Enforce strict schema validation for all messages Secure Cross-Agent Trust - Implement least-privilege access controls - Monitor task execution with immutable audit logs Future-Proof Ecosystems - Combine A2A with the Model Context Protocol (MCP) for end-to-end tool integration - Treat every Agent Card as untrusted input to prevent prompt injection The research emphasizes that security isn’t a feature—it’s the foundation. By designing protocols where agents verify, validate, and log every interaction, we enable AI teams to collaborate as securely as human experts. For developers: The team provides secure coding examples and a detailed threat model using the MAESTRO framework.

A new era of AI agent collaboration is here. Google just announced the Agent-to-Agent (A2A) protocol – a game-changer for the AI ecosystem. Why does it matter? 🌎 Open interoperability:- A2A enables different AI agents to communicate and work together — regardless of who built them. ⚙️ Built-in Trust & Security:-With end-to-end encryption and clear governance, it's designed for safe cross-agent operations. 🌎 Backed by industry leaders:- Over 50 organizations (including Adobe, Airtable, and Canva) are supporting the protocol from day one. ⚡ Enterprise-ready:- It empowers companies to orchestrate multi-agent workflows seamlessly, unlocking new automation potential. Interestingly, this Agent-to-Agent (A2A) protocol from Google and Anthropic's Model Context Protocol (MCP) are designed to work together to enhance AI agent capabilities:- ✨ MCP connects AI agents to external tools and data sources, providing them with necessary context and resources. ✨ A2A facilitates direct communication and collaboration between AI agents, enabling them to coordinate tasks and share information seamlessly. By integrating A2A and MCP, AI agents can both access external data and interact with other agents effectively, leading to more dynamic and efficient multi-agent systems. 👉 This appears to be major step toward agentic collaboration at scale. 📜 Blog - https://lnkd.in/dEvMmumq 💻 GitHub - https://lnkd.in/dFrgMZ2S #AI #Interoperability #A2A #MCP #Innovation

Six months after Anthropic’s Model Context Protocol (#MCP) gave AI agents a universal “USB‑C port” to plug into enterprise data, the agentic landscape has exploded. Google’s Agent‑to‑Agent (#A2A) protocol now lets heterogeneous agents discover each other’s skills and collaborate, while hyperscalers embed these standards directly in their clouds—slashing integration times from weeks to hours. Accenture’s new #TrustedAgentHuddle™, launched within our NVIDIA‑powered AI Refinery™, is the critical third layer: #governance. By combining MCP, A2A and a proprietary algorithm that continuously certifies agent behaviour, the Huddle allows agents from Adobe, AWS, Databricks, Google Cloud, Meta, Microsoft, Oracle, Salesforce, SAP, ServiceNow, Snowflake, Workday—plus your home‑grown bots—to operate as one secure, auditable workforce. Early trials with FedEx show how multi‑vendor agent teams can re‑plan supply chains in minutes, not hours, without sacrificing trust or compliance.   Why it matters: #PortfolioValue: Real ROI appears when dozens of specialised agents cooperate across business functions. #TrustAsKPI: Boards will soon ask for an agent team’s “trust score” alongside uptime. #NewSkills: “Agent‑ops” roles—people who design, orchestrate and monitor digital coworkers—are becoming mission‑critical. #MyTakeaway: #MCP lit the fuse. #A2A wired the circuitry. #Trusted Agent Huddle makes the whole constellation enterprise‑safe. If you’re already dabbling with agents, let’s compare notes. If you’re still on the sidelines, now’s the moment—because companies that master networked, trusted intelligence will set tomorrow’s pace. Always keen to swap stories (and war‑stories) on pushing AI from cool demo to real‑world impact. Lan Guan Tegbir Harika Vivek Luthra Martyn Toney Rick Pearce Manish Bishnoi Vijay R Menon Vijay Sharma Patrice den Hartog Akash Das-Managing Director Navin Garg Harsha Jawagal Mukesh Chaudhary Sankar Ghosh Derek Rodriguez Teresa Tung Atish Ray Chris Howard #AIRefinery #TrustedAgentHuddle #AgenticAI #EnterpriseAI #DigitalTransformation

𝐖𝐡𝐲 𝐀𝐠𝐞𝐧𝐭-𝐭𝐨-𝐀𝐠𝐞𝐧𝐭 (𝐀𝟐𝐀) 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥𝐬 𝐌𝐚𝐭𝐭𝐞𝐫 𝐟𝐨𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬𝐞𝐬 AI agents are becoming more advanced. But most still operate in isolation 𝐬𝐢𝐧𝐠𝐥𝐞-𝐩𝐥𝐚𝐲𝐞𝐫 𝐦𝐨𝐝𝐞. To solve this, Google introduced 𝐀𝐠𝐞𝐧𝐭-𝐭𝐨-𝐀𝐠𝐞𝐧𝐭 (𝐀𝟐𝐀) protocols, allowing agents to communicate, collaborate, and coordinate tasks without human involvement. This shift unlocks a new level of automation, where multiple agents can handle complex workflows across business functions. A2A Protocols unlock autonomous coordination. Think of it as giving your agents Slack, APIs, and workflow engines all in one. Let’s break it down: 𝟏. 𝐃𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧: You’ve got agents running like AI-driven service delivery, automated onboarding, and workflow. Now, they can talk to each other in real time. - 𝑹𝒆𝒔𝒖𝒍𝒕: End-to-end workflows without human stitching. -𝑰𝒎𝒑𝒂𝒄𝒕: Faster service, synchronized operations, more automation. 𝟐. 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐲: One agent doesn’t need to do everything. It can delegate, coordinate, and align with others. - 𝑹𝒆𝒔𝒖𝒍𝒕: Teams of agents that adapt on the fly. - 𝑰𝒎𝒑𝒂𝒄𝒕: Broad coverage with fewer resources. 𝟑. 𝐑𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐜𝐞 𝐛𝐲 𝐃𝐞𝐬𝐢𝐠𝐧: Agents can reroute tasks, retry failed steps, escalate when needed. - Result: Systems that flex under pressure. - Impact: Fewer breakdowns, better uptime, smarter fallbacks. Here’s the signal in the noise: Smarter agents are good but 𝐬𝐦𝐚𝐫𝐭𝐞𝐫 𝐞𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐚𝐫𝐞 𝐛𝐞𝐭𝐭𝐞𝐫. A2A helps us move from isolated AI to smart, connected automation. If you're building agents, stop thinking about what each one can do. Start thinking about what they can do together. #AgentEcosystems #A2A #AIAgents #LLM #AutonomousSystems #WorkflowAutomation #TechLeadership #AgentArchitecture #PremNatarajan