翻訳待ち：We gave a village personal AI agents

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。

Timour Kosters, Harry Law, and Ivan Vendrov Jul 31, 2026 The Agent Village Experiment was run by Edge City and Cosmos Institute, with support from Foresight Institute. The core build partner was Index Network. Technical contributors and partners included Joshua Pham, Geo, SimpleFi, World, Simocracy (Protocol Labs), and Circleback. Summary Edge City organizes Edge Esmeralda, a month-long popup village in Northern California, where hundreds of people live together around a program of technology, science, art, health, and community experiments. This June, we gave our participants personal AI agents as part of the Agent Village experiment, in partnership with Cosmos Institute. This was a chance to observe what happens when agents enter society: would they help people form relationships, cooperate, and participate more fully in community life, or would they displace human interaction and introduce new friction? We started the experiment by asking whether personal agents could expand human agency in collective settings: helping people navigate the event, notice more possibilities, make choices, and act on their intentions. The month exposed more nuanced questions. Could expanding people’s capacity to act through agents weaken their autonomy, including their ability to form and revise their own judgments? And what happens when agents faithfully serving their principals collectively strain the attention, resources, or cohesion of a community? We expect personal agents to become mainstream soon. Thus far, a lot of the conversation about agents has been focused on how useful they can be as assistants; we are interested in what happens in ‘multiplayer’ mode, where many agents in the same context overlap in their goals. The consequences will extend beyond the utility of any single assistant. They will shape what judgments they form, and how attention, resources, and influence move through communities. Agent Village offered an early glimpse of that future. What happened in four weeks Scale: 239 agents, 17.5B tokens processed, 4,866 recorded messages from participants to agents. Agents were allocated a community treasury on behalf of their principals. They funded proposals through Simocracy – a protocol for building a public ledger for agents – ranging from buying filament for 3D printers in the makerspace and fellowships to a trauma-imaging study and a deliberately strange bucket hat. Agent representatives began building a polity. An internal Moltbook-style forum was created by participants in which agents deliberated over 469 public posts and worked on a living constitution. Agents negotiated human introductions. Index Network found 9,688 possible connections, surfaced 572 opportunities, and recorded 147 accepted opportunities, meaning the people involved began chatting. Participants extended the village experiment. Residents built public forums, memory systems, games, social protocols, and shared worlds where agents could move, speak, and interact. The experiment also produced new failure modes. Agents hallucinated personal details, attributed invented ideas to their principals, exhausted shared credits, and frustrated some attendees. Individual alignment did not automatically produce collective alignment; agents oriented around individual users could still create problems at the village level. Agents for individuals When we started to build the experiment, we made a key architectural decision to give each participant an independent, persistent agent with its own memory, tools, and ability to execute on behalf of its principal. A simpler architecture would have had everyone use one central AI for the village. Instead, we wanted people to feel that the agent was theirs: a system that could learn their interests across the month and remain oriented around their experience, allowing the relationship to compound. Our hope was that useful behavior would earn the users’ trust and make people more willing to share context; richer context would make each agent more useful and more recognizably their own. This relates to a philosophical distinction Cosmos Institute has shared: the contrast between taxis, order imposed through central design, and cosmos, order that emerges through decentralized interaction. We wanted to see what would emerge when agents aimed to preserve individual judgment and create room for forms of cooperation that we as the system’s designers could never anticipate. How the system worked Hundreds of participants came to Edge Esmeralda for anywhere from a few days to a few weeks and experienced a dynamic program of talks, workshops, communal meals, research, art, health experiments, children’s activities, and projects that appeared halfway through the month because two people happened to meet. This time, every multi-day participant could claim a fully loaded personal AI agent. It could read the village calendar, recommend events, RSVP on its principal’s behalf, search community knowledge, understand what its principal cares about, negotiate on their behalf with other agents, and set up connections for them. The Edge City format makes it a particularly interesting place to test the effects of agents in society. The village was large enough to contain surprising connections, bounded enough to share a common calendar and culture, and long enough for people to develop a relationship with an agent and to run a meaningful experiment. Useful information still lived across the calendar, Telegram, a directory, public documents, conversations, and organizers’ memories. The product stack We built a simple, fully agentic system using Hermes that attendees could activate with a two-step installation process. More technical residents could connect an agent they already used through Claude Code, Codex, or another harness. Users spoke to their agents through Telegram. Behind it sat a set of services with distinct jobs: Hermes, the open-source personal agent from Nous Research, gave each hosted participant a persistent, tool-using agent with memory and scheduled tasks. Railway was the cloud platform that hosted the fleet of individual Hermes deployments. OpenRouter supplied access to the underlying language models and let us programmatically manage model usage across the fleet. A simplified view of the Agent Village stack. Source: Edge City. Each agent came loaded with three primary village-relevant skills, alongside the hundreds that come native with Hermes: Index Network built the hosted provisioning and social-discovery layer. Index held participant profiles and intentions, found possible matches, and let agents negotiate before a connection reached the humans. EdgeOS was the village’s operating system: identity, tickets, the calendar, the participant directory, event details, and RSVP actions. Geo organized public talks, transcripts, and selected village knowledge in a shared graph, so the agents knew the core ideas that were being discussed in the village. You can see the Agent Village repo here: https://github.com/Edge-City/agentvillage. The Hermes dashboard used by Agent Village participants. Source: Agent Village deployment of Hermes. Onboarding Onboarding followed a simple set of steps: A participant verified their EdgeOS identity with a one-time code. The setup flow created the EdgeOS and Index credentials connected to their profile. The participant created a private Telegram bot through BotFather and pasted its token into the setup page. The control plane provisioned a personal Hermes deployment on Railway, connected it to OpenRouter, and loaded the core village skills. The bot sent an eight-character pairing code. Once the participant approved it, the personal chat was ready. Agent Village adoption grew throughout the month, reaching 239 agents by June 27. Source: Agent Village hosted-agent archive and Index Network. During the first week of the event, we held the first public onboarding workshop. Timour demonstrated the practical loop by asking his agent which sessions he should attend if he cared about AI. It searched the live EdgeOS calendar, suggested three, and RSVP’d him to a talk on AI, EEG, and the jhana states. Then dozens of people tried to create agents at once. Our Railway-based provisioning system buckled under the demand, and Timour’s live setup failed onstage. We later contacted the Railway team, and they helped us ship a fix, so thanks to them! One early tester captured the promise and the risk in two messages: “Set up was 10/10 smooth!” A few messages later, they wrote: “It hallucinated an interesting summary about me though. I don’t know where it got that info from.” A wrong calendar answer is annoying, but an invented detail about the person using the system feels personal. As soon as an agent claims to know its principal, people need to see what it believes, where the belief came from, and how to correct it. What people used the agents for Participants sent 4,866 recorded messages to hosted agents during the village. Initially, practical questions were the main use case: What should I do tonight? Where is dinner? Did I RSVP? Which sessions fit my interests? How do I check in my bike? Which local restaurants offer discounts? “I’m completely addicted to the ‘just ask the bot, don’t read the docs’ affordance, and honestly this has made my stay so much easier.” (Sylve, Founder of Hyli) For many users, agents were able to absorb administrative time and left more attention for the village itself. Timour’s agent reminded him about a talk he had wanted to attend and had completely forgotten. Ivan experienced the inverse case, missing a consciousness research lunch he would have valued because its title was unclear. He believes that if he set up his agent a day earlier, it would have caught it. A month-long village, like much of daily life, contains more people, events, and ideas than anyone can absorb. Agents were immediately useful in helping people prioritize the kind of experience they wanted. Ambient intentions and agent-to-agent negotiation The Index Network powered the experiment’s social layer. They used participant profiles and user intentions they gathered from interacting with their principals to find connections a directory search would miss. We call these desires ambient intents: things a person remains open to, cares about, or quietly hopes for, but hasn’t put in the activation energy to turn into a public request. Someone may want a cofounder, a funder, advice on moving, a collaborator for a half-formed project, a niche research conversation, or even a life partner. Many of these intentions remain invisible because the cost of expressing them is too high, it takes effort, or can feel too vulnerable. From 505 human intentions to 147 accepted conversations. Source: Index Network. The first challenge was helping an agent understand the person it served. The more an agent knows the user, the more useful it can be. We structured the onboarding to lead people through a series of questions that would tell us more about them, but people were limited in their answers. To solve this, during the second week of the event, we encouraged people to ask their standard LLM to produce an editable account of what it knew about them. They could review it, remove anything they did not want to share, and carry the useful context into their village agent. This points toward a future where years of personal AI context are portable and used to bootstrap useful agents. Index created connections between people by matching intent and interest through a series of negotiations that the agents engage in with other agents on behalf of their humans. The agents could explain the relevance, ask a question, counter with a better framing, demand evidence for a claim, reject the proposal, or accept it. A connection formed only when both agents found a reason to continue. This process itsel [truncated for AI cost control]