Infrastructure for autonomous systems. Privacy-first, intelligent & decentralized. Built on Robinhood Chain. CA: 0x81e770ba8343232b6f200209bf9a2c1430c2dea1
t.me/withveya
Joined May 2026
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VEYA Utility: Give AI agents access to assets, apps, and tools while keeping control over permissions, limits, and execution.
What we're building next at VEYA: Agent Skills
AI agents are getting more powerful every week. What's missing is a simple way to make them follow rules and prove what they did. That's the focus of our next phase.
Plug-and-play trust
A skill is a ready-made guide your agent reads and follows. Add the VEYA skill, and your agent knows how to send its actions through VEYA without custom setup.
Rules your agent actually follows
The skills teach agents to work inside your limits: only the tools you allow, and only up to the spending budget you set.
Proof by default
Every action that goes through VEYA gets checked by validators, fingerprinted and recorded, so you can show what your agent did without exposing your strategy.
Works where builders already are
The skills use our MCP tools and SDK, so they fit into the agent setups people already use.
Tested like a real user
We test every skill end to end against the live product before we put it out. No demos that only work on slides.
Built in public
We'll share progress, test results and examples as we go, so you can follow along and try each skill yourself.
The goal is simple: any builder should be able to give their agent rules and proof in minutes.
$veya
Proving a farm harvest shouldn’t mean exposing the strategy behind it.
See how VEYA Sandbox lets you:
→ Keep your strategy note private
→ Run the Farm Watcher
→ Verify the result when 2 of 3 independent checks agree
→ Get a receipt anyone can verify
No account. No wallet. Try it yourself.
veyanet.tech/sandbox
AI agents need boundaries. Tokens need liquidity. VEYA × CHOP connect both in one loop. 🪓
VEYA brings controlled agent execution and verifiable outcomes. ChopLogs adds a wrap, trade and farm loop around $VEYA.
Wrap $VEYA. Farm the pool. Harvest $CHOP.
1% wrap/unwrap fees. 0.5% buy/sell fees. A portion of fees flows back to the VEYA treasury.
More activity. More fees. More fuel for the VEYA ecosystem.
Let it chop.
choplogs.app/logs
Introducing VEYA Sandbox. Run an AI agent in 10 seconds. No account. No wallet.
Choose an agent, decide which data stays private, and watch 3 independent checks verify the result. Once 2 of 3 agree, a proof is recorded onchain.
Your private fields stay in your browser. The proof is public, and anyone can verify the receipt.
Try the Sandbox. Run an agent, keep your data private, and see proof for yourself.
Tomorrow: See the VEYA Sandbox in action.
veyanet.tech/sandbox
VEYA weekly roadmap | Oct 19–24
This week: get hands-on with VEYA, explore agent skills, find us in MCP directories, test sealed SDK runs, and see what’s next for $VEYA holders.
One step closer to making private, verifiable AI practical for developers and users.
Follow along.
veyanet.tech
An AI agent with wallet access can become a serious liability if it has no limits. One compromised agent or unchecked action can put funds at risk.
VEYA gives agents on Robinhood Chain defined permissions, spending limits, and verifiable onchain actions.
Let agents work. Not run wild.
🚀 Top AI Agents to Watch on Robinhood Chain
Here are 11 notable projects to watch:
• Orbio: @orbiodotso
• Harmonic Agents: @HarmonicAgents
• Raxol: @axol_io
• Project VEX: @ProjectVEXai
• Priors Agents: @priors_trade
• Sherwood Agent: @sherwoodagent
• L4VA Protocol: @L4VAprotocol
• AgentOS: @useAgentOS
• Anyroute: @TryAnyroute
• veya: @withveya
• RAETO: @RaetoCC
As the Robinhood Chain ecosystem evolves, AI agents could become an important part of its next growth phase
Which projects are you watching?
An agent can leak data, expose its identity, or misuse a wallet. Securing agents means addressing each of these risks at the infrastructure level.
@VitalikButerin's experiment brings together Tor circuit isolation, cover traffic, zkAPI payment privacy, local prompt rewriting, and sandboxing to reduce exposure across the agent stack. His notes on latency and traffic analysis also show how much engineering goes into making privacy practical.
At @withveya, we’re tackling the other side of the problem: what an agent is allowed to do once it has access to tools, wallets, and onchain assets.
For developers, that means scoped tool permissions, spending limits, policy enforcement, and verifiable onchain receipts. For users, it means setting boundaries around what their agents can access and execute.
Private communication and controlled execution are complementary layers of secure agent infrastructure. Both become essential as agents move from answering questions to acting on real assets.
Wrapped zkAPI + Tor + extra cover traffic + local LLM prompt rewriting in an open-AI compatible daemon, and made a "paranoid mode" wrapper for my agent harness (pi):
Each conversation gets assigned a separate Tor circuit, your requests are masked with random cover traffic, zkAPI ensures you don't leak your identity through payments, and all prompts are locally rewritten (bring-your-own-local-model; I'm using qwen3.8-flash-next)
github.com/vbuterin/zkapi/tr…
My agent harness is wrapped with a bwrap sandbox (also experimenting with MicroVM) and wrapped again with torsocks, so everything it does goes through Tor.
Here's what me asking it to do a simple research task looks like.
It's getting quite usable! Though you definitely still pay quite a bit in increased latency, especially when starting a new conversation.
Also, Tor is far from optimal here for privacy; the cover traffic mechanism is okay against passive adversaries but much weaker if the LLM provider is colluding with the guard and actively manipulating timing. This kind of task really requires a more specialized mixnet, probably take the tradeoff of a higher bandwidth multiplier and built-in constant cover traffic.
Also also, much cheaper zkAPI deposit and withdraw gas costs are coming to mainnet soon.
Privacy protects your money from unwanted eyes, while VEYA ensures AI agents can only use it within the limits you set.
As agents gain access to wallets and onchain funds, VEYA adds clear permissions, spending limits, and controlled execution.
$veya
An AI agent that costs less to run is useful. One that also stays within its limits is far more practical.
→ Raeto makes inference more cost-efficient through automatic model routing and private AI accounts.
→ VEYA adds permissions, spending limits, and verifiable execution.
The flywheel:
More efficient inference → lower operating costs → more agents doing real work → greater need for reliable execution controls → more capable, controlled agents.
Better economics. Clear boundaries. More useful agents.
Appreciate the @RaetoCC team for building with us. Excited to keep pushing this forward together.
agents are the heaviest inference users out there.
every agent task is dozens of model calls chained together. put those on auto routing and every step runs on the cheapest model that can handle it, and all of that volume flows through raeto.
we're seeing it firsthand. @withveya runs its agents on our infrastructure, with raeto routing and private ai accounts, and its agents make 4-7x more requests than our average integration partner.
that proved the use case for us. we're doubling down on agents.
the flywheel:
more agents on raeto
→ more volume through the network
→ better routing, lower costs, private ai accounts
→ faster, cheaper, more reliable agents
→ more agents build on raeto 🔁
more agent features are coming to the roadmap soon.
inference is the engine. agents keep it spinning.
Your Agents. Your Rules. Your Control.
Giving an AI agent access to your tools is easy. Making sure it only does what you allow is the real challenge.
Today’s livestream showed how VEYA puts that control in your hands:
→ Explore freely: Read blockchain data, hash records, and verify receipts without connecting a wallet.
→ Set the boundaries: Give each agent its own environment, memory, and approved tools.
→ Block unwanted actions: Calls outside an agent’s permissions are refused.
→ Stay in control of execution: Test agent runs separately from Mainnet and connect your wallet when an action requires it.
→ Check the proof: Inspect the Mainnet contract and verify public receipts yourself.
You decide what your agents can access. They operate within the limits you set. And you can check the evidence afterward.
$veya
VEYA is live on Robinhood Chain Mainnet. Here’s how to use it.
For users: Connect your wallet, create an account, and manage your agents through the VEYA app.
For developers: Build with VEYA using the API, TypeScript SDK, or MCP.
Both paths connect to the same infrastructure for controlled execution and verifiable onchain outcomes.
App: app.veyanet.tech
Developer docs: veyanet.tech/docs
$veya
AI agents need capable models, but running them shouldn’t come at an unlimited cost.
With @RaetoCC, VEYA agents can use automatic model routing for cost-efficient inference, while each agent operates with its own capped AI budget. VEYA adds the execution controls to keep agent actions within defined boundaries.
Smarter model spend. Controlled agent execution. Built to scale.
VEYA agents are live on Raeto 🚉
every agent thinks through Auto routing and spends from its own capped, isolated AI budget. in VEYA's demo, the agent tried to go past its cap and got refused before a cent was spent.
a quick look behind the scenes at our admin dashboard: VEYA's routing key and its agent's capped account, both active and making real calls.
agents with real limits on what they do and what they spend.
The moment AI agents move from experiments to execution, trust cannot be an afterthought.
VEYA is live on Robinhood Chain Mainnet.
A real mainnet transaction. Policy-checked execution. An onchain record.
For users: create and run agents.
For builders: integrate VEYA and build agent-powered applications.
Infrastructure for agents that act within defined boundaries.
Explore VEYA → veyanet.tech
$veya
AI agents need control over what they can do and what they can spend.
With @RaetoCC, VEYA agents gain access to cost-efficient model routing and isolated, capped AI budgets alongside VEYA’s permissions and execution controls.
Lower inference costs. Controlled execution. Built for autonomous agents on Robinhood Chain.
$veya $raeto
VEYA brings inference and agent execution together connecting model intelligence with coordinated tool use, defined permissions, and verifiable onchain actions.
As onchain inference scales, the infrastructure around what agents can actually do matters just as much.
VEYA Mainnet launches tomorrow on Robinhood Chain.
