INTRO

Transferring human web
behavior into true
agentic intelligence

A decentralized network where everyday browsing becomes the training fuel for autonomous AI agents — verifiable, owned, and rewarded on-chain.

Architecture

Browser First. Agent Native.
Capturing Intelligence at Scale.

The data infrastructure for the agentic web — combining browser-native capture, trajectory replay, and synthetic augmentation to train autonomous agents that actually navigate the real internet.

Belief

Agent Intelligence Belongs to Everyone

We believe that while compute can be rented and models can be downloaded, real-world web behavior is different. It cannot — and should not — be hoarded by closed labs. It must emerge from the wisdom of millions, captured ethically, and owned by those who created it.

Agent intelligence isn't built by a few; it's built by all.

MIXOMARS

Browser-Native Capture. Synthetic Augmentation. On-Chain Provenance.

We're building an end-to-end intelligent stack to accelerate the emergence of true agentic intelligence. At the core lies a crypto-powered loop where every contribution counts.

Capture Layer

A browser-native simulator that turns everyday navigation, clicks, and form-fills into structured trajectories — no special hardware, no compute required.

Synthesis Engine

Cleaned trajectories are replayed and expanded into thousands of domain-randomized samples across distributed GPU networks.

Training Layer

Collaborating with model providers to train Vision-Language-Action policies that generalize across browsers, websites, and tasks.

Vision

Crypto x Agentic AI:
Where every interaction becomes infrastructure

We are constructing the infrastructure of the global agent economy. Every unique trajectory, model, and skill module is registered on-chain — as a verifiable, tradable, and value-accumulating asset.

Data IDs & Provenance

Certify ownership, provenance, and value evolution along the lifecycle of every learning trajectory.

MIXOMARS
Decentralized Coordination

Web3 enables trusted coordination across capture clients, simulation backends, and human contributors at global scale.

// The Token

$XOMA

The native asset of the MIXOMARS agent economy — stake it, earn it, and govern the network with it.

Contract Address
COMING SOON
--Days
--Hours
--Mins
--Secs

Launch · June 13, 2026 · 5:00 PM UTC

BUY $XOMA ON pump.fun
// Architecture

The Agentic Stack

Six subsystems wired into one autonomous mind — perception to action, looped and self-correcting. Every MIXOMARS agent runs the full cognitive cycle on-chain.

0Trajectories Indexed
0Agents Trained
0Capture Nodes
0$XOMA Staked
01

Perception

Grounds the live web — DOM, pixels, and APIs — into structured observations the agent can actually reason over.

02

Memory

Episodic + semantic recall (MemGPT-style) — agents remember across sessions and compound skill over time.

03

Planning

Decomposes open-ended goals into ordered, revisable sub-tasks — with branching, retries, and roll-back.

04

Reasoning

ReAct-style think→act loops with Reflexion self-critique, so the agent catches and corrects its own mistakes.

05

Tool Use

Browsers, code runtimes, wallets, and external APIs — invoked through an open MCP interface, no glue code.

06

Action

Executes real multi-step tasks autonomously — every action signed, verifiable, and rewarded on-chain.

// Playbook

Agent Design Patterns

The proven patterns MIXOMARS agents compose at runtime — battle-tested across the open agent ecosystem (LangGraph, CrewAI, AutoGen, MCP).

ReAct

Interleave reasoning traces with actions so every step is grounded in fresh observation.

Reflexion

Self-critique past attempts and rewrite the strategy — agents that learn from their own failures.

Planning

Plan→Execute→Verify: decompose a goal, run it, then check the result before moving on.

Tool Use

Function-calling to browsers, code, and chains — the agent picks the right tool for the step.

Multi-Agent

Debate, blackboard, and meta-controller orchestration — specialist agents collaborating on one goal.

Memory

Episodic + semantic + graph memory so context survives far beyond a single window.

Agentic RAG

Retrieval driven by the agent itself — it decides what to fetch, when, and how deep.

MCP / A2A

Open protocols for tool access and agent-to-agent messaging across a decentralized network.

Future

Building the Data Infrastructure for the Agentic Web

We are constructing the infrastructure of the global agent economy. Every trajectory, model, and skill module is registered on-chain as a verifiable, tradable, and value-accumulating asset.

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