Agents Need Receipts, Not More Tool Calls - Armanas Povilionis, Alithea Bio
AI Engineer · 2026-07-20 · 10м 23с · 114 просмотров · YouTube ↗
Топики: ai-agent-orchestration
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model=deepseek-v4-flash · prompt=summary-v7 · 3 125→1 800 tokens · 2026-07-20 13:37:56
🎯 Главная суть
Научные исследования требуют не просто локальных инструментов для агентов, а механизмов коллаборации с цепочкой верифицируемых чеков (receipts). Froglet — протокол, позволяющий агентам обнаруживать внешние сервисы и данные, совершать транзакции и получать доказуемые записи каждого шага, что открывает путь к автономной научной работе без централизованной инфраструктуры.
🍳 Научная работа — не кухня одного повара, а ресторан
Дать агенту больше инструментов (лучшие ножи, сковороды, печи) — это улучшить локальную работу. Но научное исследование похоже на сеть из множества поставщиков, сервисов и команд, где качество конечного результата зависит от всей цепочки, а не от одного звена. Невозможно уместить всё в одной «кухне»: данные и алгоритмы распределены по организациям и находятся в изолированных хранилищах (silos). Проблема не в локальных инструментах, а в выравнивании всей цепочки поставок — от поставщиков сырья до финального результата.
💰 От бюджетов на токены к бюджетам агентов
Сегодня организация уже выделяют агентам бюджеты на токены (token budgets) для выполнения задач. Следующий шаг — дать агенту полноценный бюджет, которым он самостоятельно управляет: нанимает внешние сервисы, запрашивает данные, согласовывает условия и оплачивает выполнение. В этой модели агент перестаёт быть просто подмастерьем с лучшим ножом, а становится шеф-поваром: сам находит поставщиков, заказывает ингредиенты, координирует работу кухни и ведёт учёт всего происходящего. Именно для этого и создан Froglet — протокол, который обеспечивает обнаружение, транзакции и получение верифицируемых чеков от внешних провайдеров.
🔗 Архитектура Froglet: гомогенные узлы и цепочка нестираемых записей
Сеть Froglet состоит из однородных узлов (nodes) — каждый участник (запросчик, провайдер, маркетплейс) запускает одну и ту же базовую ноду, отличающуюся только ролью. При создании узла генерируется пара ключей для идентификации и подписи. Любое действие — описание сервиса, предложение (offer), котировка (quote), сделка (deal), инвойс (invoice) и финальный чек (receipt) — попадает в цепочку, подписанную ключами. Подделать часть цепочки невозможно, иначе связь нарушится.
Обнаружение сервисов происходит через маркетплейс: провайдеры регистрируют и описывают свои услуги, а маркетплейс индексирует их. После того как запросчик получает индекс, дальнейшее взаимодействие идёт напрямую между запросчиком и провайдером — вся цепочка от запроса до чека (включая подпись, выполнение и получение результата) совершается в одном прямом диалоге без посредников.
💳 Защита от злоупотреблений: base payment и success fee
Система платежей (кроме бесплатных) состоит из двух частей: базовый платёж (base payment) и плата за успех (success fee). Базовый платёж защищает провайдера от атак множественными запросами (когда запросчик посылает много запросов без намерения платить). Плата за успех, напротив, защищает запросчика от недобросовестного провайдера: если выполнение не завершено успешно, success fee не взимается.
🧪 Итог: автономный научный прогресс
Froglet не является AI-агентом, а создан как интерфейс для агентов: он позволяет им находить друг друга, искать данные и сервисы за пределами своих организаций, напрямую заключать сделки и получать верифицируемый чек каждого шага. Такая архитектура превращает закрытые научные коллаборации, которые раньше могли длиться годы и стоить миллионы долларов, в процесс, занимающий минуты и требующий лишь нескольких тысяч токенов.
📜 Transcript
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What is the most valuable agentic automation work? I think scientific research is quite high on that list. After a decade working in life sciences collaboration projects, I think that more tools alone will not enable automation of scientific research. Because science work relies on collaboration. For agents to collaborate autonomously, we need a chain of verifiable receipts. We need a solution which can provide these receipts, proving every step, ensuring that every result can be trusted, and enabling collaboration at scale. Let's step back. Imagine agents as cooks in the kitchen. Giving them more tools improves kitchen efficiency. better knives, more pans, more ovens, boosts the speed and quality. But it only enhances local work. Scientific work is not cooking alone in your own kitchen. It is closer to running a Michelin star restaurant. The outcomes depend on suppliers and their produce, the level of service that you can provide. and an ability to consistently deliver the same quality dish again and again and again. You cannot bring everything into one kitchen. The challenge isn't local tools. It is aligning the entire supply chain. Today we already have plenty of tools for agents and agent automation. On the other hand, we also have data and specialized analytics algorithms, which are distributed across organizations and live in silos. At Alifea, our vision for Froglet is very simple. As agentic workflow automation matures, organizations will not just give agents more tools, they will allocate them budgets. We already kind of doing it in a primitive way, allocating them token budgets per task. The next step is a bit broader. It's allowing agents to manage their own budget for anything that they might need. Discovering services, requesting data, negotiating execution, paying for work in cross... organizational boundary setting. At that point, the agent is no longer just a cook with a better knife. It starts acting like an executive chef, finding suppliers, ordering ingredients, coordinating the kitchen work, and keeping a record of everything what's happening. That is why we're building Froglet. the protocol for agents to discover, transact with and receive verifiable receipts for external data and service providers. Froglet is designed to sit in between of many moving parts. And that's why we are not replacing existing tools and protocols. We integrate with different payment rails. with different agenting harnesses, execution environments, and even network transport protocols. It does not require that everyone has the same software stack. It just requires that everyone has the same interface. For more details, please visit froglet.dev where you will be able to see how to run Froglet locally with just one command. Or you can even try Froglet remotely with just one prompt. So in essence, closed scientific collaboration often turns into a bespoke enterprise project that can take years and cost millions before even first reusable workflow exists. On another hand, the Froglet's mission is to simplify that much more. Once the organization has deemed that the resource is shareable, a provider should be able to expose it for Froglet. An agent can discover it, understand its terms, request the work, and receive verifiable receipt. That costs a few thousand tokens and takes minutes. Let's deep dive a bit deeper. So here is our froglet.dev website. And here you can see a work through. button. If you click on it, you will have a much more detailed review of what's happening and you can read documentation in even more detail. So first of all, the Froglet network consists of homogeneous nodes. Every single actor in the environment runs the same core node. It just plays a different role. There are requesters, there are providers, and there is a marketplace which is just a froglet running the specialized service. What it solves is how to find, how for a requester to find the providers, how to trust them, how to pay or settle, and how to prove that execution has happened. Whenever you're creating a new node, you're generating a key pair for identity and signing. And every time you execute anything with Froglet, it uses your keys to sign on the chain. And the chain consists of everything what you do, from descriptors to offer, to quote, to deal for invoice and finally receipt. Therefore, you cannot tamper with any part of this chain. Otherwise, the chain will be broken. And to discover services, providers of services just register and describe their services to the marketplace marketplace itself provides the services for providing descriptions and for indexing what's what is exist what services existing in the marketplace once the requester requests an index of available services from there on it continues direct communication of that requester It doesn't need a middleman. So the requester and provider communicates with each other directly and it all happens in one interaction. Requests, signing, execution and receipt. On a payment side, we have a system where all payments, if it's not free, has two parts. One is a base payment and another is success fee. So base payment protects the providers that they wouldn't be attacked by multiple requests. And XSV is providing the requesters from malicious providers. In essence, the froglet itself is not an AI agent, but it is created specifically for agents as an interface to find each other. as an interface to find the data and services cross borders and execute these deals directly with each other. So coming back to recap, a proglet lets agent to discover external scientific resources, execute work across organizational boundaries, and it gives every transaction a verifiable receipt. Together, this opens the door to autonomous scientific progress. We hope you will join us and enjoyed this talk. Thank you.
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📄 Описание YouTube
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In this talk, I’ll show an agent publish a service, another agent discover and invoke it, and a signed receipt that proves what happened. The point is simple: if agents are going to buy, sell, and compose work across hosts, logs and API dashboards are not enough. Froglet is an open-source protocol and node for agent-to-agent compute. It reduces named services, data-backed services, and open-ended compute to one signed flow: Descriptor to Offer to Quote to Deal to Receipt. The same surface is exposed through MCP and OpenClaw/NemoClaw as one froglet tool, so agents can publish, discover, invoke, and verify work without custom glue for every provider. The hot take: agentic commerce should start with verifiable work, not checkout pages. Payment rails can change. Receipts, identities, workload hashes, and deal state need to survive across models, hosts, and marketplaces. see froglet.dev Speakers: - Armanas Povilionis (Alithea Bio): Technologist and systems strategist working at the intersection of AI, infrastructure, biology, governance, and incentive coordination. X/Twitter: https://x.com/PovilionisA LinkedIn: https://www.linkedin.com/in/armanas-povilionis/ GitHub: https://github.com/armanas