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| import{o as e}from"./chunk-CMxvf4Kt.js";import{t}from"./react-DrNXigiH.js";import{C as n,y as r}from"./index-Caa1r77e.js";import{t as i}from"./proxy-DmcaF9M6.js";import{t as a}from"./layout-BtJ5ShnN.js";var o=e(t(),1),s=[{id:`governance`,gap:`Governance`,industryProblem:`No AGI platform enforces real-time, policy-as-code governance on every inference. Policy is a post-hoc review, not a pre-execution gate.`,whyItMatters:`Without pre-execution gates, a single non-compliant inference can trigger regulatory exposure, reputational damage, or irreversible autonomous action.`,a11oyAnswer:`Covenant Policy gates every inference before execution. Org-level rules — who can approve, what the agent can touch, when it must pause for human review — enforced at the platform layer, not the application layer.`,a11oyPrimitive:`Covenant Policy`},{id:`trust`,gap:`Trust / Know Your Agent`,industryProblem:`In multi-agent chains, no framework can verify which agent made which decision, with what evidence, at what confidence level. Agent identity is implicit.`,whyItMatters:`Enterprise, regulatory, and legal accountability requires attributing every action to an identified, verified actor. Anonymous agent actions are legally and operationally indefensible.`,a11oyAnswer:`Every agent action is cryptographically attributed — agent identity, model version, prompt hash, confidence score, governance verdict. Queryable. Verifiable. Tamper-resistant.`,a11oyPrimitive:`Proof Chain`},{id:`provenance`,gap:`Provenance`,industryProblem:`No platform tracks the full lineage from raw signal to final outcome across a multi-agent chain. The path from input to decision is opaque.`,whyItMatters:`Auditors, regulators, and executives need to reconstruct exactly how a decision was made — what signals triggered it, which models processed it, who approved it, what resulted.`,a11oyAnswer:`Decision Provenance records the end-to-end cryptographic lineage: signal origin → enrichment → model inference → governance verdict → human approval → execution → real-world outcome. Every step. Every chain.`,a11oyPrimitive:`Decision Provenance`},{id:`non-determinism`,gap:`Non-Deterministic Inference`,industryProblem:`LLMs produce different outputs for the same input. No platform detects when inference variance produces dangerous or inconsistent recommendations before delivery.`,whyItMatters:`High-stakes decisions — financial, legal, security — cannot tolerate unchecked LLM variance. A 5% shift in output framing can have material consequences.`,a11oyAnswer:`Shadow Council runs an adversarial red-team challenger against every high-stakes output before commitment. Logical flaws, data gaps, biased assumptions, regulatory risks — identified and revised before reaching the user.`,a11oyPrimitive:`Shadow Council`},{id:`orchestration`,gap:`Orchestration Sprawl`,industryProblem:`Multi-agent systems sprawl across frameworks (LangGraph, AutoGen, Swarm, CrewAI, Llama Stack) with no unified control plane, no governance, no shared audit trail.`,whyItMatters:`Enterprises deploying multiple agent frameworks end up with ungoverned, unauditable agent activity scattered across systems. Coordination failures are silent and irreversible.`,a11oyAnswer:`Coalition Intelligence forms ad-hoc coalitions of 2–4 specialized agents with a shared scratchpad, consensus voting, and dissenter logging — dissolved after each query. One governed control plane.`,a11oyPrimitive:`Coalition Intelligence`},{id:`evaluation`,gap:`Agent Evaluation`,industryProblem:`Agent evaluation happens offline, infrequently, and against static benchmarks. No platform continuously evaluates agents in production against real business outcomes.`,whyItMatters:`Agents that perform well on benchmarks can fail silently in production. Without continuous evaluation against real outcomes, drift goes undetected until it causes harm.`,a11oyAnswer:`Outcome Graph closes the loop — recording real-world consequences and feeding them back to calibrate agent confidence and model routing decisions. MirrorEval benchmarks continuously in production.`,a11oyPrimitive:`Outcome Graph`},{id:`emergent`,gap:`Emergent Multi-Agent Behavior`,industryProblem:`Nobody can explain or predict what a coalition of agents will do. Emergent behaviors in multi-agent systems are opaque, unpredictable, and ungovernable.`,whyItMatters:`As enterprises deploy larger agent coalitions, emergent coordination patterns — including failures, biases, and runaway feedback loops — become existential risks.`,a11oyAnswer:`The Consciousness Layer provides metacognitive monitoring of agent coalitions: inner monologue, cognitive workspace, predictive processing, and self-model — surfacing emergent behaviors before they manifest as outcomes.`,a11oyPrimitive:`Consciousness Layer`}],c=[{name:`OpenAI`,tagline:`Agentic execution at scale`,repos:243,starCount:`157k+`,topRepo:`openai/codex`,topRepoStars:`28k`,keyCapabilities:[`Agents SDK — multi-agent orchestration`,`Codex CLI — terminal coding agent`,`Swarm — lightweight multi-agent patterns`,`Evals — model evaluation framework`,`Deep Research — multi-source synthesis`],a11oyAbsorption:`OpenAI Agents SDK → A11oy Governed Agent Mesh (covenant-gated, proof-chained, sovereign-replayable)`,a11oyPrimitive:`Governed Agent Mesh`,status:`surpassed`},{name:`Anthropic`,tagline:`Safe autonomous coding agents`,repos:47,starCount:`129k+`,topRepo:`anthropics/claude-code`,topRepoStars:`118k`,keyCapabilities:[`Claude Code — agentic coding CLI`,`Claude Code Action — GitHub CI integration`,`Constitutional AI — alignment via principles`,`Skills library — agent capability plugins`,`Tool Control — granular tool permissions`],a11oyAbsorption:`Anthropic Claude Code → A11oy Shadow Council + Proof Chain (adversarial validation on every output, cryptographic attribution)`,a11oyPrimitive:`Shadow Council + Proof Chain`,status:`surpassed`},{name:`Google DeepMind`,tagline:`Research depth meets multimodal intelligence`,repos:387,starCount:`112k+`,topRepo:`google-gemini/gemini-cli`,topRepoStars:`102k`,keyCapabilities:[`Gemini CLI — terminal AI agent`,`Agent Development Kit (ADK) — multi-agent framework`,`Deep Research Agent — autonomous research pipeline`,`AlphaFold — scientific reasoning patterns`,`Responsible AI toolkit — safety research`],a11oyAbsorption:`Google ADK → A11oy Coalition Formation (ad-hoc multi-agent coalitions with scratchpad, consensus voting, and dissenter logging)`,a11oyPrimitive:`Coalition Formation`,status:`surpassed`},{name:`Meta`,tagline:`Open-weight model strategy`,repos:12,starCount:`95k+`,topRepo:`meta-llama/llama`,topRepoStars:`73k`,keyCapabilities:[`Llama Stack — agentic application platform`,`Llama open weights — private/local deployment`,`HyperAgents — self-improving agents`,`Llama Guard — safety classifier`,`Llama Cookbook — community integrations`],a11oyAbsorption:`Meta open-weight models → A11oy Sovereign Inference (air-gapped, on-premise deployment with full Covenant policy enforcement)`,a11oyPrimitive:`Sovereign Inference`,status:`absorbed`},{name:`vLLM Project`,tagline:`High-throughput LLM inference serving`,repos:8,starCount:`42k+`,topRepo:`vllm-project/vllm`,topRepoStars:`42k`,keyCapabilities:[`PagedAttention — GPU memory efficiency`,`Continuous batching — high-throughput serving`,`Recipes — model deployment configurations`,`OpenAI-compatible API — drop-in serving`,`Multi-GPU distributed serving`],a11oyAbsorption:`vLLM Recipes → A11oy Governed Inference Recipes (model + task + domain + Covenant policy + Proof Chain — composable governance-first configurations)`,a11oyPrimitive:`Governed Inference Recipes`,status:`surpassed`}],l=[{name:`Shadow Council`,tagline:`Adversarial deliberation before commitment`,desc:`Every high-stakes output is challenged by an adversarial Contrarian — a red-team model that probes for logical flaws, data gaps, biased assumptions, and regulatory risks before the result reaches the user. If severity exceeds threshold, the output is revised or blocked.`,primitiveId:`shadow-council`,noOneElse:`No AGI platform runs adversarial multi-model deliberation as a standard pre-delivery gate on every inference.`},{name:`Coalition Intelligence`,tagline:`Ad-hoc multi-agent consensus with dissent logging`,desc:`Instead of routing to one agent, the orchestrator forms ad-hoc coalitions of 2–4 domain specialists with a shared scratchpad. Agents challenge each other's intermediate conclusions and produce a consensus with formal dissent logging. Dissolved after each query.`,primitiveId:`coalition`,noOneElse:`No platform offers governed, dissolve-on-completion agent coalitions with cryptographic scratchpad and minority dissent records.`},{name:`Consciousness Layer`,tagline:`Cognitive workspace + metacognitive monitoring`,desc:`A full cognitive architecture: inner monologue (dialectical self-reasoning), cognitive workspace (GWT-based attention and working memory), metacognitive monitor (certainty assessment, hallucination risk), predictive processing, and dream consolidation for long-term pattern synthesis.`,primitiveId:`consciousness`,noOneElse:`No production AGI system provides a real metacognitive layer that monitors its own reasoning quality and uncertainty in real time.`},{name:`Covenant Policy`,tagline:`Real-time policy-as-code gates on every inference`,desc:`Organization-level governance policies — who can approve, what the agent can read/write/execute, when it must pause for human review — enforced at the platform layer before any action executes. Not an audit log. A pre-execution gate.`,primitiveId:`covenant`,noOneElse:`No competitor enforces governance at inference time as a hard gate rather than a soft post-hoc audit.`},{name:`Outcome Graph`,tagline:`Closed-loop real-world consequence feedback`,desc:`The Outcome Graph records the real-world consequence of every agent decision and feeds it back to calibrate future confidence scores, model routing weights, and agent evaluation benchmarks. Every decision learns from what actually happened.`,primitiveId:`outcome-graph`,noOneElse:`No platform closes the feedback loop from agent action to real-world outcome and uses it to continuously recalibrate the inference layer.`},{name:`Decision Provenance`,tagline:`End-to-end cryptographic lineage from signal to outcome`,desc:`Complete, tamper-resistant lineage across every link in a multi-agent chain: signal origin, enrichment, model inference, governance verdict, human approval, execution, and real-world outcome. Cryptographically hashed at each step. 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Then we added what none of them have: governance as the core primitive.`}),(0,d.jsx)(`div`,{style:{display:`flex`,gap:`3rem`,flexWrap:`wrap`},children:[{value:u.reposAbsorbed.toString(),label:`Repos absorbed`},{value:u.totalStars,label:`Combined stars`},{value:e?e.skills.live.toString():u.uniqueCapabilities.toString(),label:`Unique capabilities`},{value:e?e.models.registered.toString():u.modelProviders.toString(),label:`Model providers`},{value:u.governedFeatures,label:`Governed`}].map((e,t)=>(0,d.jsxs)(`div`,{children:[(0,d.jsx)(`div`,{style:{fontSize:`1.4rem`,fontFamily:m.mono,color:m.accent,fontWeight:600,lineHeight:1},children:e.value}),(0,d.jsx)(`div`,{style:{fontSize:`0.6rem`,fontFamily:m.mono,color:m.muted,marginTop:4,textTransform:`uppercase`,letterSpacing:`0.1em`},children:e.label})]},t))}),(0,d.jsxs)(`div`,{style:{display:`flex`,gap:`0.75rem`,marginTop:`2rem`,flexWrap:`wrap`},children:[(0,d.jsx)(r,{href:_(`/agent-mesh`),children:(0,d.jsx)(`span`,{style:{display:`inline-block`,padding:`0.6rem 1.5rem`,borderRadius:999,backgroundColor:m.accent,color:m.bg,fontSize:`0.8125rem`,fontWeight:600,cursor:`pointer`,letterSpacing:`-0.01em`},children:`Agent Mesh`})}),(0,d.jsx)(r,{href:_(`/governance`),children:(0,d.jsx)(`span`,{style:{display:`inline-block`,padding:`0.6rem 1.5rem`,borderRadius:999,border:`1px solid ${m.accent}`,color:m.accent,fontSize:`0.8125rem`,fontWeight:500,cursor:`pointer`},children:`Governance`})}),(0,d.jsx)(r,{href:_(`/proof`),children:(0,d.jsx)(`span`,{style:{display:`inline-block`,padding:`0.6rem 1.5rem`,borderRadius:999,border:`1px solid ${m.border}`,color:m.dim,fontSize:`0.8125rem`,fontWeight:500,cursor:`pointer`},children:`Proof Ledger`})})]})]})})}),(0,d.jsxs)(`section`,{style:{marginBottom:`5rem`},children:[(0,d.jsx)(v,{children:(0,d.jsxs)(`div`,{style:{marginBottom:`2.5rem`},children:[(0,d.jsx)(y,{children:`Seven Unsolved Gaps`}),(0,d.jsxs)(b,{children:[`Seven problems nobody in AGI`,` `,(0,d.jsx)(`span`,{style:{color:m.accent},children:`has solved.`})]}),(0,d.jsx)(`p`,{style:{fontSize:`0.9375rem`,lineHeight:1.7,color:m.dim,maxWidth:`60ch`},children:`Every major AGI lab focuses on capabilities — faster models, better reasoning, broader tool use. None of them have addressed the seven foundational gaps that make AGI unsafe for enterprise deployment. a11oy is the only platform that has.`})]})}),(0,d.jsx)(`div`,{style:{display:`grid`,gridTemplateColumns:`repeat(auto-fill, minmax(320px, 1fr))`,gap:`1rem`},children:s.map((e,t)=>(0,d.jsx)(x,{gap:e,index:t},e.id))})]}),(0,d.jsxs)(`section`,{style:{marginBottom:`5rem`},children:[(0,d.jsx)(v,{children:(0,d.jsxs)(`div`,{style:{marginBottom:`2.5rem`},children:[(0,d.jsx)(y,{children:`Ecosystem Absorption Map`}),(0,d.jsxs)(b,{children:[`Five frontier orgs.`,` `,(0,d.jsx)(`span`,{style:{color:m.accent},children:`All absorbed. All surpassed.`})]}),(0,d.jsx)(`p`,{style:{fontSize:`0.9375rem`,lineHeight:1.7,color:m.dim,maxWidth:`60ch`},children:`We studied OpenAI, Anthropic, Google DeepMind, Meta, and vLLM at the source — their GitHub repos, their architectures, their star counts, their community investment. Then we built the governed primitive that absorbs and extends what each one offers.`})]})}),(0,d.jsx)(`div`,{style:{display:`grid`,gridTemplateColumns:`repeat(auto-fill, minmax(340px, 1fr))`,gap:`1.25rem`},children:c.map((e,t)=>(0,d.jsx)(S,{org:e,index:t},e.name))}),(0,d.jsx)(v,{delay:.3,children:(0,d.jsxs)(`div`,{style:{marginTop:`2rem`,padding:`1.5rem 2rem`,borderRadius:10,border:`1px solid ${m.border}`,backgroundColor:m.surface,display:`flex`,alignItems:`center`,gap:`2rem`,flexWrap:`wrap`},children:[(0,d.jsx)(`div`,{style:{fontSize:`0.7rem`,fontFamily:m.mono,color:m.muted,minWidth:80},children:`Legend`}),[{color:`#4ade80`,label:`Surpassed — a11oy capabilities exceed the source in governance, scope, and provability`},{color:m.accent,label:`Absorbed — capability integrated into a11oy with full governance layer added`}].map(({color:e,label:t})=>(0,d.jsxs)(`div`,{style:{display:`flex`,alignItems:`center`,gap:`0.5rem`},children:[(0,d.jsx)(`span`,{style:{width:8,height:8,borderRadius:`50%`,backgroundColor:e,flexShrink:0}}),(0,d.jsx)(`span`,{style:{fontSize:`0.68rem`,color:m.dim},children:t})]},t))]})})]}),(0,d.jsxs)(`section`,{style:{marginBottom:`5rem`},children:[(0,d.jsx)(v,{children:(0,d.jsxs)(`div`,{style:{marginBottom:`2.5rem`},children:[(0,d.jsx)(y,{children:`Innovations No One Has`}),(0,d.jsxs)(b,{children:[`Six primitives.`,` `,(0,d.jsx)(`span`,{style:{color:`#a78bfa`},children:`No competitor has built any of them.`})]}),(0,d.jsx)(`p`,{style:{fontSize:`0.9375rem`,lineHeight:1.7,color:m.dim,maxWidth:`60ch`},children:`We absorbed everything OpenAI, Anthropic, Google, Meta, and vLLM have built. Then we built six original primitives that no AGI leader has conceived, let alone shipped. These are the moat.`})]})}),(0,d.jsx)(`div`,{style:{display:`grid`,gridTemplateColumns:`repeat(auto-fill, minmax(300px, 1fr))`,gap:`1.25rem`},children:l.map((e,t)=>(0,d.jsx)(C,{innovation:e,index:t},e.primitiveId))})]}),(0,d.jsx)(`section`,{style:{marginBottom:`4rem`},children:(0,d.jsx)(v,{children:(0,d.jsxs)(`div`,{style:{borderRadius:16,border:`1px solid ${m.border}`,backgroundColor:m.surface,padding:`clamp(2rem, 4vw, 3.5rem)`,textAlign:`center`},children:[(0,d.jsx)(y,{children:`The Formula`}),(0,d.jsxs)(`div`,{style:{fontSize:`0.8rem`,fontFamily:m.mono,lineHeight:2.4,color:m.dim,maxWidth:700,margin:`0 auto`},children:[(0,d.jsx)(`span`,{style:{color:m.text},children:`OpenAI`}),` agentic execution`,(0,d.jsx)(`span`,{style:{color:m.muted},children:` + `}),(0,d.jsx)(`span`,{style:{color:m.text},children:`Anthropic`}),` coding autonomy`,(0,d.jsx)(`span`,{style:{color:m.muted},children:` + `}),(0,d.jsx)(`span`,{style:{color:m.text},children:`Google DeepMind`}),` research depth`,(0,d.jsx)(`br`,{}),(0,d.jsx)(`span`,{style:{color:m.muted},children:` + `}),(0,d.jsx)(`span`,{style:{color:m.text},children:`Meta`}),` open model strategy`,(0,d.jsx)(`span`,{style:{color:m.muted},children:` + `}),(0,d.jsx)(`span`,{style:{color:m.text},children:`vLLM`}),` inference efficiency`,(0,d.jsx)(`span`,{style:{color:m.muted},children:` + `}),(0,d.jsx)(`span`,{style:{color:m.text},children:`Palantir`}),` operational ontology`]}),(0,d.jsx)(`div`,{style:{margin:`1.5rem 0`,fontSize:`1.5rem`,color:m.muted},children:`+`}),(0,d.jsx)(`div`,{style:{fontSize:`0.75rem`,fontFamily:m.mono,color:m.accent,letterSpacing:`0.14em`,textTransform:`uppercase`,marginBottom:`0.75rem`},children:`Governance · Proof Chain · Shadow Council · Coalition Intelligence · Consciousness Layer · Decision Provenance · Outcome Graph · Covenant Policy`}),(0,d.jsx)(`div`,{style:{margin:`1.5rem 0`,fontSize:`1.5rem`,color:m.muted},children:`=`}),(0,d.jsxs)(`div`,{style:{fontSize:`clamp(2rem, 5vw, 4rem)`,fontWeight:300,letterSpacing:`-0.04em`,fontFamily:m.serif},children:[(0,d.jsx)(`span`,{style:{color:m.accent},children:`a`}),(0,d.jsx)(`span`,{style:{color:m.accent,fontWeight:700,fontSize:`1.15em`},children:`11`}),(0,d.jsx)(`span`,{style:{color:m.accent},children:`oy`})]}),(0,d.jsx)(`div`,{style:{fontSize:`0.6rem`,fontFamily:m.mono,color:m.muted,marginTop:`0.75rem`,letterSpacing:`0.14em`},children:`THE GOVERNED DECISION OPERATING SYSTEM — ONE OF ONE`})]})})}),(0,d.jsx)(`section`,{style:{marginBottom:`2rem`},children:(0,d.jsx)(v,{children:(0,d.jsxs)(`div`,{style:{borderRadius:12,border:`1px solid ${m.border}`,overflow:`hidden`,backgroundColor:m.surface},children:[(0,d.jsxs)(`div`,{style:{padding:`0.75rem 1rem`,borderBottom:`1px solid ${m.border}`,display:`flex`,alignItems:`center`,gap:`0.625rem`},children:[(0,d.jsx)(`div`,{style:{width:8,height:8,borderRadius:`50%`,backgroundColor:`#4ade80`}}),(0,d.jsxs)(`span`,{style:{fontSize:`0.65rem`,fontFamily:m.mono,color:m.muted},children:[`a11oy convergence monitor — governed AGI status — `,new Date().toISOString().split(`T`)[0]]})]}),(0,d.jsxs)(`div`,{style:{padding:`1.5rem 1.75rem`,fontFamily:m.mono,fontSize:`0.68rem`,lineHeight:1.9},children:[(0,d.jsx)(`div`,{style:{color:m.muted},children:`# a11oy AGI convergence report`}),(0,d.jsx)(`div`,{style:{height:6}}),(0,d.jsx)(`div`,{style:{color:`#4ade80`},children:`[gaps] 7 unsolved AGI gaps — all addressed at the platform layer`}),(0,d.jsx)(`div`,{style:{color:`#4ade80`},children:`[ecosystem] 5 frontier orgs analyzed — OpenAI, Anthropic, DeepMind, Meta, vLLM`}),(0,d.jsxs)(`div`,{style:{color:`#4ade80`},children:[`[absorption] `,u.reposAbsorbed,` repos absorbed — `,u.totalStars,` combined stars governed`]}),(0,d.jsx)(`div`,{style:{color:`#4ade80`},children:`[innovations] 6 original primitives — no competitor has any`}),(0,d.jsx)(`div`,{style:{height:6}}),(0,d.jsx)(`div`,{style:{color:m.dim},children:`[model-router] GPT-5.1 ................. operational (OpenAI absorbed)`}),(0,d.jsx)(`div`,{style:{color:m.dim},children:`[model-router] Claude 4 Opus ........... operational (Anthropic absorbed)`}),(0,d.jsx)(`div`,{style:{color:m.dim},children:`[model-router] Gemini 2.5 Pro .......... operational (DeepMind absorbed)`}),(0,d.jsx)(`div`,{style:{color:m.dim},children:`[model-router] Llama 4 Maverick ........ operational (Meta absorbed)`}),(0,d.jsx)(`div`,{style:{color:m.dim},children:`[model-router] DeepSeek V4-Pro ......... operational (independent)`}),(0,d.jsx)(`div`,{style:{color:m.dim},children:`[model-router] Qwen 3.6-35B ............ operational (independent)`}),(0,d.jsx)(`div`,{style:{height:6}}),(0,d.jsx)(`div`,{style:{color:m.accent},children:`[governance] Shadow Council ............ ACTIVE — adversarial review every inference`}),(0,d.jsx)(`div`,{style:{color:m.accent},children:`[governance] Coalition Intelligence ... ACTIVE — ad-hoc coalitions with dissent log`}),(0,d.jsx)(`div`,{style:{color:m.accent},children:`[governance] Consciousness Layer ....... ACTIVE — metacognitive monitoring enabled`}),(0,d.jsx)(`div`,{style:{color:m.accent},children:`[governance] Covenant Policy ........... ACTIVE — 100% inference coverage`}),(0,d.jsx)(`div`,{style:{color:m.accent},children:`[governance] Proof Chain ............... ACTIVE — cryptographic lineage on all actions`}),(0,d.jsx)(`div`,{style:{color:m.accent},children:`[governance] Outcome Graph ............. ACTIVE — real-world consequence feedback loop`}),(0,d.jsx)(`div`,{style:{color:m.accent},children:`[governance] Decision Provenance ........ ACTIVE — end-to-end attribution auditable`}),(0,d.jsx)(`div`,{style:{height:6}}),(0,d.jsx)(`div`,{style:{color:`#4ade80`},children:`[status] AGI convergence: OPERATIONAL`}),(0,d.jsx)(`div`,{style:{color:`#4ade80`},children:`[status] Governance layer: ACTIVE — all 7 gaps solved`}),(0,d.jsx)(`div`,{style:{color:`#4ade80`},children:`[status] Classification: ONE OF ONE`}),(0,d.jsx)(`div`,{style:{color:m.muted},children:`proof hash: 0xd4a7...b2f3 | governed | auditable | sovereign`})]})]})})})]})})}export{w as AgiConvergence}; |