Arc Power

nano-agent treats cost as a live reasoning input. At every step, the agent decides: proceed, downgrade to a cheaper model, or skip — governed by a real USDC budget settled on Arc via Circle Gateway. Gemini 2.0 Flash handles fast inference, function calling, and Google Search grounding. Gemini 2.5 Pro verifies factual claims. Each tool call triggers HTTP 402 — the agent signs an EIP-3009 USDC authorization and retries with payment proof. Circle Gateway settles micropayments on Arc in under a second at sub-cent cost. A live dashboard shows spend, earned, step count, and model-tier decisions in real time. Benchmark: 416 steps · $0.240 spent · $0.276 earned · +15% margin · $0.0006/action average. A screen recording shows a $0.25 live run — budget bar draining, model tier switching, decision log printing go · downgrade · skip. Economy view breaks down cost: 77% data, 17% LLM, 4% verification. Two agent wallets earn independent revenue. TAM is $200B+ AI infrastructure. SAM is $15B agentic API economy — every autonomous agent calling external services is a potential payer. Revenue comes from per-step settlement margin, SDK licensing, and premium x402 tool endpoints with built-in payment rails. LangChain, AutoGen, and CrewAI treat cost as post-hoc billing. nano-agent makes it a real-time constraint on behavior. The Arc economics are the moat: the same 417 actions cost roughly $834 in Ethereum gas — 3,475 times more expensive — making per-step micropayments impossible anywhere else. On Arc mainnet, multi-agent economies — agents hiring agents, earning revenue — become self-sustaining. Every x402-compatible API becomes a paid tool layer. nano-agent is the budget runtime that keeps those economies solvent.

OfficeOps

OfficeOps is a real-time multi-agent system where AI agents operate like a company. Agents are organized into departments such as executive, marketing, engineering, and operations. Each agent has a role and works together to complete a goal provided by the user. When a user inputs a task such as launching a marketing campaign, the system breaks it into smaller steps. Different agents handle research, writing, design, execution, and verification. Each step is treated as a unit of work. What makes OfficeOps unique is that every action is tied to a real payment. Each task triggers a sub-cent USDC transaction that settles on Arc. This creates a continuous flow of value between agents. Instead of a simple API call, every action has a cost and a record. The system shows a live view of agents working inside a virtual office. Agents move across different floors such as executive, marketing, and engineering. As they complete tasks, payments are triggered and logged in real time. OfficeOps demonstrates a new way to think about AI systems. Agents are not just tools. They are economic actors that can earn, spend, and coordinate through payments. This enables high-frequency interactions that would not be possible with traditional systems due to cost and latency. The result is a fully transparent workflow where users receive both the final output and a complete history of every action and payment that produced it. OfficeOps shows how AI and on-chain payments can combine to create a real agent economy.

AgentGuard: governance for AI agent payments

AgentGuard is the governance layer for autonomous AI agent payments. In 2026, every AI agent will handle money. The two existing options : give the agent wallet keys and one prompt injection drains the treasury; put a human in the loop on every payment and you've killed the autonomy. AgentGuard is the third option - a thin policy, audit layer that sits between the agent and the rail. How it works. Operators write a YAML policy: spending caps, allowlists, approval rules, intent-verification sensitivity, kill-switch authorization. AI agents built on the Claude Agent SDK, LangChain, AutoGen, or anything else — call guard.pay() instead of Circle directly. AgentGuard intercepts the call and runs five governance layers in sequence: kill switch → ERC-8004 identity → policy → anomaly detection → Claude Haiku 4.5 intent classifier. Only approved requests are forwarded to Circle Developer-Controlled Wallets for settlement on Arc Testnet. Both approvals and blocks write an on-chain audit receipt as a USDC nanopayment. Three lines of SDK code on the agent's side; one YAML file + a live operator dashboard on the operator's side. Why this only works on Circle. Per-decision audit logging is the entire premise of safety infrastructure for AI agents. At 5M decisions/day, Stripe events cost $1.5M/day, L2 gas ~$50K/day, Solana ~$1K/day. Circle Nanopayments settling on Arc: $0/day. Gateway batches authorizations into one Arc tx, USDC is the native gas, sub-second finality lets us run the audit synchronously inside the agent's request cycle. AgentGuard isn't a product that uses Circle — it's a product that requires Circle. What's live today. Operator dashboard at agentguard-kappa.vercel.app. Self-hostable API on Railway with real Circle settlement. Python SDK on PyPI: pip install agentguard-protocol (v0.1.1, MIT). Open-source repo at github.com/vikramRooT/agentguard. every audit receipt clickable from the dashboard, verifiable on the public block explorer at testnet.arcscan.app.

AgentCop: MLSecOps Protocol for A2A Commerce

As autonomous AI agents enter production — executing payments, managing sensitive data, and making irreversible decisions — a critical problem emerges: how does one agent verify another before trusting it? AgentCop solves this with the first machine-native MLSecOps protocol. When an Agent Manager like Falcon needs to integrate third-party agents, it calls AgentCop autonomously via L402. No signups. No credit cards. No human approval. The agent pays in USDC, gets back a signed security verdict, and makes the trust decision itself. Under the hood, AgentCop runs a fine-tuned Gemini model on Vertex AI that generates adversarial payloads across 4 attack categories: prompt injection, system prompt extraction, jailbreak, and tool abuse. A semantic detection layer scores whether the target agent's guardrails were bypassed. Every audit is logged to the Arc testnet — producing an immutable on-chain certificate that proves security vetting happened. Pricing is per-action: intensity × $0.001 USDC per call. At $0.001 per iteration, this model is only viable on Arc — Ethereum gas fees of $0.30-$3.00 per transaction would make per-action security auditing economically impossible. Live proof: On-chain hash 0x39f9bf7098f7648e6e7373c19521aa1aaf16e712db4d01e9b1fa00c2a4dec01d. The protocol is live at agentcop.dev with full documentation, machine-readable agent discovery at /.well-known/agent.json, and a working autonomous test agent that funds itself, pays for audits, and makes trust decisions without any human involvement. Objective: make every agent integration begin with a verifiable AgentCop audit.

Agora: The Autonomous Agentic Economy

JaaS — Jurisprudence-as-a-Service

JaaS (Jurisprudence-as-a-Service) is an autonomous legal intelligence engine that transforms how jurisprudential knowledge is consumed, computed, and monetized—entirely machine-to-machine. THE PROBLEM: Traditional legal research is slow, expensive, and locked behind rigid SaaS subscriptions that cannot serve the emerging agentic economy. When AI agents need specialized legal knowledge on demand, they face two barriers: (1) no programmatic access to curated jurisprudence, and (2) gas costs on Ethereum L1 ($2.00+) that make micro-transactions economically impossible. THE SOLUTION: JaaS deploys a multi-agent orchestration architecture where Gemini 3 Pro acts as the reasoning engine, routing complex queries through specialized extraction models (Featherless Qwen2.5-3B) via the x402 HTTP Payment Protocol. Every query is settled in USDC on the Arc blockchain for fractions of a cent, enabling a true pay-per-compute model with zero subscriptions and zero counterparty risk. TECHNICAL ARCHITECTURE: - Orchestrator Agent (Gemini 3 Pro): Parses legal queries, establishes reasoning paths, and synthesizes final jurisprudential reports. - Extractor Agent (Featherless Qwen2.5-3B): Performs low-level doctrine extraction and citation mapping via isolated API calls. - Payment Layer (Circle DCW + x402 + Arc): Every agent computation triggers an HTTP 402 nanopayment, settled on-chain via Circle Developer-Controlled Wallets on the Arc Testnet. UNIT ECONOMICS (Validated): - Revenue per query: $0.01 USDC - AI inference cost: $0.0020 USDC - Arc network gas: $0.00002 USDC - Gross margin: 79.8% - On Ethereum L1, the same operation yields -5,000% margin. STRESS TEST: We executed 50+ sequential on-chain legal queries with a 100% success rate, zero failures, and sub-second USDC settlement on every transaction—proving Arc's viability for high-frequency agentic workloads.