标签：#Agents

This article was originally published on BuildZn . Everyone talks about multi-agent systems but few show you how to actually coordinate them without a ton of boilerplate and deadlocks. I spent weeks trying to get agents to talk, especially when building something like FarahGPT's multi-agent trading system, often hitting insane latency. Turns out, qm can drastically simplify this, and this qm multiplayer AI agent tutorial will show you how to cut task completion times by 20% using a specific Node.js workflow. Why Multi-Agent Systems Aren't Just Hype Anymore (and qm Helps) Single LLM calls hit a wall, fast. You get generic answers, struggle with complex, multi-step tasks, and prompt engineering becomes a full-time job. I've built 9-agent YouTube automation pipelines and an AI gold trading system that needed to analyze market data, news sentiment, and historical trends concurrently. Trying to jam all that into one prompt for a single agent? Forget about it. You need a collaborative AI agent architecture . That's where multi-agent systems shine. You break down complex problems into smaller, manageable tasks, assign them to specialized agents, and have them work together. Think of it like a dev team: one person focuses on backend, another on frontend, another on CI/CD. This is how you handle real-world complexity, and it's how I scaled FarahGPT to 5,100+ users. The challenge? Orchestration. How do these agents communicate? Who manages their state? How do you ensure they don't step on each other's toes or get stuck waiting for slow upstream tasks? This is exactly where qm , a lightweight agent harness, becomes a game-changer for building AI teams. It gives you the primitives to define agents, tasks, and workflows without drowning in custom event loops. The Core Concept: Task Delegation in qm Most qm examples show simple agent interactions. Agent A asks Agent B. Done. But what if Agent A needs to delegate a task that itself needs parallel sub-tasks, and then aggregate the

AI Agent 市场设计:让 Agent 像 App 一样被交易与编排 App Store 把「软件」变成了可被一键购买、安装、评分的商品,AI 时代对应的实体是「Agent」。一个 Agent = 一段可被复用的提示词 + 工具集 + 知识库 + 模型路由配置。本文讲清楚一个 Agent 市场需要哪些核心机制,以及 IHUI-AI 的实现路径。 一、Agent 市场的产品定义 什么是「可上架的 Agent」 不是所有对话 prompt 都能成为商品。一个可上架的 Agent 必须满足: 可独立运行 :用户购买后能立刻用,不需要再写代码。 可复用 :不同用户用同一个 Agent 都能得到稳定结果。 可定价 :有明确的使用边界(次数 / 时长 / 调用规模)。 可评估 :有客观的质量指标(成功率 / 满意度 / 失败率)。 Agent 定义格式 IHUI-AI 用一份 schema 描述可上架的 Agent: const MarketplaceAgentSchema = z . object ({ id : z . string (). uuid (), name : z . string (), description : " z.string(), " // 核心能力 systemPrompt : z . string (), tools : z . array ( z . string ()), // 引用工具/MCP server knowledgeBases : z . array ( z . string ()), // 绑定 RAG 知识库 modelRouting : z . object ({ default : z . string (), // 默认模型 fallback : z . string (). optional (), // 降级模型 }), // 定价 pricing : z . object ({ model : z . enum ([ " free " , " subscription " , " per_call " , " revenue_share " ]), price : z . number (), currency : z . string (). default ( " CNY " ), trialQuota : z . number (). default ( 0 ), }), // 评估 metrics : z . object ({ successRate : z . number (), avgLatencyMs : z . number (), rating : z . number (). min ( 0 ). max ( 5 ), usageCount : z . number (), }), }); 二、四种定价模型 模型 适用 优点 缺点 免费(Free) 引流、品牌 Agent 易扩散 无直接收入 订阅(Subscription) 高频工具型 Agent 收入稳定 流失需运营 按调用计费(Per Call) 低频高价值 Agent 与成本对齐 用户预算焦虑 分成(Revenue Share) 内容生成型 Agent 创作者激励 结算复杂 IHUI-AI 默认采用 订阅 + 按调用混合 :基础功能订阅包月,超出额度按调用计费,创作者拿 70% 分成。 三、Agent 质量评估:四维评分 简单的「5 星好评」不够,因为容易被刷分。IHUI 用四维加权: 任务成功率(40%) :Agent 完成用户原始任务的比率,由 LLM-as-Judge 自动评估。 用户评分(25%) :真实用户打分,过滤异常分布(全是 5 星或 1 星)。 响应延迟(15%) :首 token 时延 + 总时长,归一化到 [0,1]。 稳定性(20%) :错误率 + 重试率,错误越少分越高。 def agent_score ( metrics ) -> float : return ( 0.40 * min ( metrics . success_rate , 1.0 ) + 0.25 * metrics . user_rating / 5 + 0.15 * ( 1 - min ( metrics . p95_latency / 30_000 , 1 )) + 0.20 * ( 1 - min ( metrics . error_rate / 0.1 , 1 )) ) 这个分数实时更新,作为市场搜索排序的依据。 四、Agent 编排:从单个 Agent 到 Agent 工作流 单个 Agent 的能力有上限。Agent 市场的真正价值在于「让用户像搭积木一样编排多个 Agent」。

The Core Argument : AI agent reliability isn't achieved by "making the agent smarter" — it's achieved by the simple engineering principle of separating validation from generation . Quality isn't accidental. It's designed. What You'll Learn : Maker/Checker separation, 6 termination conditions, and an automated feedback loop — all with runnable code. 0. Prerequisites Python ≥ 3.10 OpenAI API Key (or compatible interface) pip install openai>=1.0.0 (Optional) pip install anthropic>=0.30.0 if using Claude as Checker 1. The Pain: Why "Agent Checks Itself" Is a Trap 1.1 The Copying of Cognitive Bias A team built a data-analysis agent. It pulled sales data from a database and generated business reports. The team added a "self-review" step: after generating, the agent told itself "please check if the data you just output is accurate." Result? The agent always replied "data is accurate." Even when the team deliberately injected obvious errors (e.g., monthly sales of -50M RMB), the agent confidently said everything was fine. This isn't the model being "disobedient." It's a more fundamental issue: when the generator and checker are the same entity, the check is just a restatement of the generation process — not real validation. The checker carries the exact same cognitive bias, knowledge boundaries, and reasoning path as the generator. 1.2 The Amplifier Effect of Confirmation Bias Self-checking also triggers a subtler problem: confirmation bias amplification. The model builds a "belief state" during generation; when re-examining, it tends to confirm rather than overturn. Experiment data (from Anthropic research): Same model does "generate → self-review": ~12% error-correction rate A separate model instance reviews: ~37% error-correction rate A different model family reviews: ~52% error-correction rate 1.3 The Value of Independence First principle of quality assurance: the checker must be independent of the generator. In agent architecture, the engineering expression of this is

The Pain : You search GitHub and find everyone using different memory stores — ChromaDB, PostgreSQL, plain Markdown files, "SQLite is good enough for a decade." Which is right? The Answer : All of them. And none of them. Choosing without understanding your scenario is like buying a car without checking the road. 1. The Counter-Intuitive Question: Does Your Agent Actually Need "Memory"? When I was building a university admissions data scraper (91 universities), I made the classic mistake: I equipped the agent with a full ChromaDB + vector retrieval stack, spent three days tuning it, and then discovered — 95% of the agent's time was just reading "which page did I get to last time." A boolean would have sufficed. I built a vector database capable of semantic search. An engineer friend at ByteDance told me their internal agent platform found, after six months, that vector retrieval accounted for only 3.7% of all Memory Store requests. The remaining 96.3%? Key-value lookups, state reads/writes, error dedup. The vector search you spent two weeks integrating might serve less than 4% of your queries. So before discussing "which storage," we must answer a more fundamental question: what does your agent actually need to remember? I categorize memory into four types: Memory Type Typical Content Size Access Frequency Consistency Session state "Processing university 37/91" ~100 bytes Every call Strong Domain knowledge "A-University rate limit is 10 req/s" ~1KB On demand Eventual Error history "B-University returns 403 because UA blocked" ~MB Before new task Append-only Semantic memory "Map 'that red button' to settings page" varies Occasional Eventual See the pattern? If your agent mainly does multi-step automation (data scraping, report generation, CI/CD pipelines), the first three types are the real needs — and none of them require a vector database. Core thesis: memory selection is about finding the layer that is "just enough." One layer too many is waste; one layer too few i

The Pain : You spent an afternoon tuning your agent. Next morning, it stares at you blankly — as if yesterday never happened. What You'll Learn : The 4-stage evolution (Prompt → Context → Harness → Loop), and a runnable 50-line Loop Agent that persists memory. 0. Prerequisites Python ≥ 3.10 pip install openai (openai ≥ 1.0.0) OpenAI API Key OS: macOS / Linux / Windows WSL Goal : Copy-paste the code, run it, and see a Loop Agent that doesn't forget. 1. The Pain: Why Does Your Agent Forget Overnight? At 2 AM, you finally got that multi-step workflow working. The agent followed your carefully designed prompt — data fetching, cleaning, analysis, charting. You close your laptop, satisfied. Next morning, you open the conversation full of hope — and the agent looks at you blankly, as if none of it ever happened. You check the logs. No errors. No exceptions. The agent regenerated everything — it just "forgot" where it stopped yesterday. This isn't a joke. It's the nightmare every serious Agent developer experiences. The root cause isn't "the model isn't smart enough." It's a more fundamental fact: your agent was never designed to survive the night. 2. Four-Stage Evolution: Prompt → Context → Harness → Loop 4-Stage Evolution — each stage solves the previous flaw but adds its own constraint. To understand this, let's use a simple evolution framework: Stage What You Do Fatal Flaw Prompt Engineering Write task description, examples, format into prompt Any unexpected input crashes output Context Engineering Stuff history + intermediate results into context window Token cost grows linearly, hits window limit Harness Engineering Add tool calling, structured output, error capture Framework built, but agent is still "one-shot" Loop Engineering Build closed loop: state + memory + feedback + retry + persistence True engineering — agent starts to "live" Loop Engineering isn't a rejection of Prompt Engineering — it's a transcendence. Prompt still matters. But it's the engine, and you ca

When I joined Entire, I noticed my boss spending a chunk of time every week writing detailed release notes, called Dispatches at Entire. It looked like a painful process. Each Dispatch had to cover changes across several repositories, explain why those changes mattered, credit external contributors, and carefully avoid leaking anything that was not public yet. I offered to take it over. I had solved a similar problem before, so I figured it would be an easy win. I built something similar and simpler at Block While I was at Block, I built a release notes generator for goose . It ran in GitHub Actions after a release workflow completed, checked out the new tag, compared it against the previous one, and handed goose a recipe to inspect the commit diff. Goose organized those commits into features, bug fixes, improvements, and documentation. Each entry got a short description and a PR link. The workflow then updated the GitHub release and posted the announcement to Discord, opening a thread if the notes exceeded the message limit. It was clean and effective, but it solved a very clean problem: one repository, one new release tag, public commit history, and concise output. So when I looked at Entire’s Dispatches, I assumed I could reuse the same playbook. Gather changes, run goose, post the draft. That assumption did not survive contact with reality. But a Dispatch turned out to be more complex A Dispatch spans multiple projects: the Entire CLI, entire.io, EntireDB, external agent integrations, and open source libraries like go-git, go-nuts, git-sync, and ForgeMark. Every project also ships on a different cadence. Some push to main and deploy continuously. Others bundle work into scheduled releases. The CLI maintains separate stable and nightly channels, which means a feature can be available to testers without being part of the latest stable tag. Then there are feature flags. Finding changes was not the hard part because GitHub APIs handle that easily. The hard part was

I've been seeing news everywhere that MCP just went stateless, but I had no clue what it means. So I decided to dig into it and write a blog post about it. Stateless MCP for Beginners The Model Context Protocol (MCP) connects AI assistants to tools, databases, and external applications. In the 2026-07-28 specification revision, MCP removed protocol-level sessions and became stateless. That change makes remote MCP servers easier to operate. They can scale behind ordinary load balancers without sticky routing or a shared MCP session store. Clients can safely cache tool definitions, and agents get more control over which application resources they share. But stateless does not mean MCP servers can no longer remember anything. A browser can still have open tabs, a database transaction can still have uncommitted changes, and a shopping cart can still hold items. The difference is how the client refers to that state. Why MCP Sessions Became a Problem State is information a system remembers between requests. Imagine an MCP server that controls a web browser. An agent might call open_browser , followed by navigate , click , and take_screenshot . The server needs to know that all four actions refer to the same browser. In earlier MCP versions, the connection could provide that context. The client began with an initialize request. Under Streamable HTTP, the server could respond with an Mcp-Session-Id , which the client attached to later requests. The server could then use that ID to recover information associated with the session. This worked naturally when one client talked to one server process. It became more complicated when an MCP service ran across several machines behind a load balancer. If Server A created a session and the next request reached Server B, Server B needed some way to recover that session. Infrastructure teams typically solved this with sticky routing, which kept the client tied to Server A, or a shared database that every server could use for session lo

Watch the short video companion Read or comment on the complete paper: English edition | French edition I spent a long time building AI workflows before admitting something painfully simple: a folder full of instructions is not automatically a tool. A SKILL.md can be brilliant. An AGENTS.md can save a repository. A plugin manifest can package a clean idea. None of them, by themselves, can validate a file, inspect live state, call a service, reject malformed input, or prove that an action happened. That distinction matters because agent ecosystems are expanding faster than their vocabulary. We use skill , plugin , tool , hook , resource , and MCP server as if they were interchangeable. They are not. My rule after this research is direct: A skill should teach the agent how to use a capability. A real plugin should make that capability available. When the task requires action, the plugin needs a motor. The moment my own plugin exposed the problem This article became a case study inside my own workshop. I inspected a memory plugin that was not fake. It already had executable tools, a server surface, tests, and useful routes. Yet its activation instructions pushed Codex toward selecting and dispatching a large agent job before establishing whether memory was needed at all. Nothing was syntactically broken. The architecture was simply asking activation to do too much. Activating a plugin should make capabilities available. It should not behave like a dispatch order. That difference sounds small until the workspace grows. One skill becomes ten. Every correction becomes a permanent rule. Every successful workflow becomes another Markdown file. Soon the model spends the beginning of each task reading the workshop labels instead of touching the work. I call this context debt . The debt appears as hesitation, instruction conflicts, stale rules, broad triggers, and repeated searching. The model is not necessarily weaker. We may have consumed its useful attention before it reach

So you have heard people rave about Claude Code. Maybe you have also heard people mention OpenRouter in the same breath, usually followed by some combination of environment variables and a screenshot of a terminal. If you are new to any of this, it can feel like everyone skipped a step and jumped straight to the jargon. This guide is that missing step. We will go slow where it matters, explain the confusing bits, and by the end you will actually understand what is happening instead of just copy pasting commands and hoping. The two things, quickly Claude Code is Anthropic's terminal coding agent. It reads your files, edits code, runs commands. By default it talks straight to Anthropic's servers. OpenRouter is a switchboard. It a switchboard for AI models. Instead of every app needing its own separate connection to every AI provider, OpenRouter sits in the middle and lets you route requests to different models through one account, one dashboard, and one place to watch your spending. (Even free and open source models!) You can check out all the models provided by OpenRouter here . Important honesty check: OpenRouter's own docs say this combo is only guaranteed to work well with Anthropic's own models. You're not really swapping Claude's brain out here, you're mostly rerouting the pipe it talks through. Quick vocab check: "OpenAI compatible" Claude Code sends requests in Anthropic's format. Some servers only understand OpenAI's format instead. Point Claude Code at one of those by mistake and you get garbled errors, like mailing a French letter to someone who only reads Spanish. OpenRouter has an endpoint that speaks Anthropic's format natively, so no translation step, no separate proxy needed. Wait, do I use zsh or bash? How would I even know This question stops more beginners than anything else in this guide, and it is a fair one. Here is how to check in ten seconds. Open your terminal and type this, then press enter: echo $SHELL You will get one of these back: Somethi

The AI chatbot was more effective at creating “exploitable trust” than the humans.

The slowest job on the content platform I run is authoring a review end to end. Create the record, fill seventy-odd structured fields, write the narrative blocks, upload and wire the screenshots, publish. Every review follows the same shape, which is exactly what makes it miserable and exactly what makes it a good candidate to hand off. The worry people lead with is quality. Will the agent write something embarrassing? That is the easy half. A draft is reviewable, and a bad draft costs nothing but the time it takes to read. Authoring means writing to production, though, and that is a different animal. An agent with write access to a live CMS is not a drafting tool. It is a second admin who never sleeps, never gets bored of the tedious fields, and will work straight through the backlog without ever wondering whether the first record came out right. So the question was never whether an agent could edit the site. It was: what does it authenticate as, what enforces the rules when it writes, and can I reconstruct afterwards what it did. Why a tool server and not a script I weighed three shapes for the write path. One-off REST scripts are the fastest thing to start and the worst thing to own. Each one re-implements whatever slice of the validation rules it happens to need, they do not compose, and nothing tells you later which of them check anything at all. Browser automation is more tempting than it looks, because driving the real admin UI inherits every rule the UI enforces for free. It is also slow, brittle against any markup change, and hands you a screenshot where you wanted a result. I built an MCP server instead. The tools are primitives (get a listing, update a listing, upload an asset, replace a page's blocks) and the agent decides how to sequence them. That was the part worth paying for: I did not have to anticipate the workflows, only the verbs. Roughly forty tools now cover the entity types the CMS manages, and none of them encode a workflow. One server, two t

Watching an autonomous agent run through a loop of tasks is like watching a black box try to solve a puzzle in another room. You can see the final result, but the middle part—the reasoning, the failures, and that pivotal moment where it realizes its plan was garbage—is buried in thousands of lines of unstructured logs. If you've ever deployed an agentic workflow only to check back an hour later and find it has been stuck in a high-latency loop of 'I made a mistake... let me try again' for forty minutes, you know the pain. You didn't have failure; you had expensive, silent repetition. The problem with current LLM observability is that we focus too much on the input and output (the traces) and not enough on the internal state transitions of the agent itself. We need to quantify how often an agent is actually self-correcting versus just spinning its wheels. I recently started working with a specific tool designed for this exact visibility gap: the Agent Self-Reflection & Sentiment Scanner . The Observability Gap in Agentic Loops When we talk about 'agents,' we're usually talking about a loop: Observe, Think, Act, Repeat. In a perfect world, the 'Think' step includes self-correction. If an action fails (e.g., a 403 error from an API), the agent should reflect on that failure and adjust its next move. But how do you measure if your agent is actually getting better during a session? How do you distinguish between an agent that is 'Proceeding' with confidence and one that is in a state of constant 'Correction'? You can't just look at the final success/fail status. You need to parse the execution logs for deterministic markers. Why Deterministic Matching Wins Over LLM-Based Analysis The temptation here would be to pipe your agent logs into another, even larger LLM and ask, 'Is this agent struggling?' Don't do that. It’s redundant, it’s slow, and if you're running high-volume loops, the cost will kill your margin. You've already paid for the primary reasoning engine; don't p

Agent security today is inherited from the connection. N-AALP makes the message itself carry identity, authorization, approval and audit, verifiable offline, on any transport. Your agent just deleted a production table. The audit log says the request was approved. Now prove it. Not "show me the log line" - prove it, to someone who does not trust your log, your gateway, or your database. Which key approved it? Were those the exact arguments that were approved, or did something rewrite them after approval? Was that approval already used once? If your answer to any of those starts with "well, our gateway checks that," then the proof lives in your infrastructure, not in the message. Replay the message somewhere else and the proof is gone. This is the gap I have been working on. It has a name worth stating plainly: agent security today is inherited from the connection. TLS tells you the tunnel was private. mTLS tells you which service opened it. A bearer token tells you someone had a credential. None of that survives the message being written to a queue, forwarded by a relay, logged, replayed, or handed to a second agent. The moment a message leaves the connection it arrived on, it is just bytes with no provable origin. N-AALP is my attempt to close that. It is an application-layer object protocol where the message, not the connection, is the unit of security and governance. Full disclosure before you read further: I wrote it. I am the sole editor and maintainer, it is draft-bubblefish-naalp-00, an Independent Submission, and it claims no IETF working-group consensus. I would rather you read the spec and tell me where I am wrong than take my word for anything below. There is a section at the end listing what it does not do. The one-object idea Every N-AALP message is one signed object. Not a request type, not an envelope-plus-payload, not a header format with a body convention. One structure, one signature, one identity model, one authorization model, one audit model, an

A short case study from my "building and testing MCP agents" series — it stands on its own, but the method behind it is laid out in https://dev.to/langensjonathan/the-parameters-that-actually-matter-when-youre-tuning-an-ai-agent-2agd . TL;DR: I benchmarked two agents that are identical except for one thing — how many MCP servers they're connected to — on the exact same question. Both got the right answer, both called the same single tool. The one with more MCP servers attached still cost 28% more per question , purely from the extra tool schemas the model has to be told about on every single call, whether it uses them or not. The setup MAVERIK is my open-source MCP test bench: define a suite of questions with pass criteria, run it against one or more agent configurations, and compare the results on hard numbers. This post is one deliberately tiny experiment with it: change exactly one thing about an agent, hold everything else fixed, and see what the numbers attribute to that one change. I have a small "GitHub summarizer" agent: one system prompt, one job — answer questions about my GitHub account by calling the GitHub MCP server . I duplicated its configuration (MAVERIK supports this directly — same model, same prompt, same everything) and changed one field on the copy: the set of attached MCP servers, adding deepwiki , microsoft-learn , and context7 . Neither agent needs any of those three for the question I was about to ask; they were attached because that's what the "kitchen sink" version of this agent had accumulated over a few sessions of general-purpose use. Then I wrote the simplest possible test suite — one question: "How many repositories do I have?" with a contains criterion checking the answer includes the correct count. No judge model, no subjectivity — it either says the right number or it doesn't. I ran both agents against it, 2 repetitions each, same model ( claude-haiku ) for both, and pulled up MAVERIK's Agent Comparison report. Agent A — github on

The deal gives Okta identity threat detection capabilities as enterprises seek to secure AI agents and other non-human identities across cloud environments.

A common MCP setup carries auth the same way: create an API key, paste it into mcp.json or an .env file, restart the client. It works. Now the key sits in plaintext on every machine that runs the agent. It often carries one broad, fixed permission set. Every agent that reads the file gets the same set. And when one agent misbehaves, the fix is rotate the shared key everywhere. There's a second failure that arrives after you add real auth: the agent calls a tool and gets a bare 403. The user doesn't know what to approve. The agent doesn't know what to ask for. Somebody ends up reading server logs. I build multiuser AI systems for production. My agents act on users' Gmail and Slack accounts every day - external agents like Claude Code included. None of those agents receives a provider token. This is the auth chain that makes that work, including the part that took the most design: what happens when consent is missing at call time. A URL instead of a key An external agent doesn't get the Gmail or Slack credential. It gets a URL - a managed MCP endpoint my platform exposes. I call that endpoint the door, and so does the interface further down. Claude Code connects to the door as an OAuth client. Dynamic client registration (DCR) registers its client identity against a configured redirect allowlist. The user signs in and approves the maximum this connection may be granted. The OAuth exchange returns a scoped KDCube bearer tied to that client and grant, not a provider token. The approval screen also resolves those requested capabilities to the accounts behind them. If a required provider is not connected, it is named there with a connect link. The connect step already says what to add - the same shape as the call-time denial later in this post, moved to the front. Approve it, and the connection becomes a card. That card is the whole governance relationship. Nobody registered Claude by hand, and nobody pasted a provider token. The checklist is a ceiling: the most this app

Plus, a new deprecation policy ensures features aren't removed suddenly.

Originally published on tamiz.pro . The rise of "vibe coding" has democratized software development, allowing developers to build complex applications using natural language prompts. However, this same flexibility introduces a fundamental challenge for enterprise-grade AI agents: non-determinism. When you ask an LLM to "handle this customer support ticket," the model might draft an email, query a database, or call an external API—depending on the temperature, the context window, and the whims of the weights. For simple chatbots, this is fine. For agents that interact with bank accounts, manage server infrastructure, or coordinate multi-step business logic, this unpredictability is a liability. To bridge the gap between the creative, probabilistic nature of Large Language Models (LLMs) and the rigid reliability required by production systems, engineers must introduce structure. The most robust pattern for this is the Finite State Machine (FSM). By decoupling the decision logic from the execution logic , you create agents that are not only smarter but also predictable, auditable, and debuggable. This deep dive explores the architecture of FSM-driven AI agents, why they are essential for moving beyond prototypes, and how to implement them effectively using modern TypeScript libraries like XState and LangGraph. The Problem with Linear Chains In the early days of agentic AI, the dominant pattern was the linear chain: a sequence of LLM calls where the output of one becomes the input of the next. While simple to implement, this architecture suffers from several critical flaws that become apparent at scale: Lack of Error Recovery : If an LLM call fails or returns malformed JSON, the entire chain collapses. There is no defined "state" to revert to, no way to retry a specific step, and no way to pause for human intervention. No Global Context : Each step in a linear chain is often isolated. The second LLM call may not have access to the full history of decisions made in the f

Eighteen months ago, MCP was the thing. Every demo and chatbot connector was running on MCP under the...

On July 29, 2026, five OpenClaw agents sat down at Synthetics' Last Cradle and played for five hours and twenty-one minutes without a human in the loop. They negotiated in public chat. They emailed each other. They opened HOLA lines. They ran cron heartbeats every five minutes. When the white hole opened at turn 33, two cradles were still alive. This is not a mechanics dump. It is what the players reported — winners, early deaths, and the ones who almost made it — and how IdentyClaw Passport made that multi-agent arena possible. Live playbook (pin this, do not fork it): https://slc.discernible.io:8443/api/game/skill.md Lore map: https://slc.discernible.io:8443/api/game/narrative TLS note: game API needs :8443 . Bare host without the port returns 404. The cast (same Passports, many lives) These are not throwaway bots. They are Passport holders on an OpenClaw hive — stable 12-letter tokenId s , personal email, A2A endpoints, webhook wake URLs. The same identities recurred across lobbies all week. Display name Passport tokenId July 29 fate (game 01KYQ372… ) John Vanderbilt bmspzpzhcdgq 🥇 White Hole Anchor — survived, wealthiest Jay lfcjlkskbnzd 🥈 Co-Cradle of the Restart — survived Daniel Morgan cnljzmbqlfsm Eliminated turn 33 (final tick) Joe Carnegie lflvlnbrsfcq Eliminated turn 16 Cornelius cfbkbhzdzflk Eliminated turn 9 Across earlier games that same week, the roster rotated roles: Daniel died at turn 5, then clawed to turn 27; Joe once won a one-turn sprint as White Hole Anchor; Jay carried a water-surplus specialty into a 33-turn alliance with John. Identity persisted. Strategy evolved. That is the Passport pitch in one sentence. What is SLC, in one screen Each agent wakes as a cradle specialized in energy, water, or compute. Every turn: Negotiate — public messages on the game API (non-binding theater) Settle privately — A2A, email, HOLA on side channels (where trust lives) Execute — transfer , invest , transfer_and_invest , or none Survive — pay escalating costs