UV Harness. Agent infrastructure, ready to ship.
Build frontier financial agents in days, not quarters. Battle-tested orchestration, execution, and policy enforcement.
The Harness runs production trading for hundreds of live users today, with five years of engineering, three of them in live markets. The same system, hosted for your platform.
What's inside the Harness.
Four systems your agents run on. Each one is battle-tested independently. Together they're the full operating environment.
Agent Orchestration
The right model for every task. Inference fires only when it matters.
Routes each subtask to the right model: frontier for reasoning, fast and cheap for classification. Event-driven inference means agents only think when something meaningful happens. At 10K users, polling-based agents are economically impossible. Event-driven inference is what makes per-user agents viable.
Policy Engine
Every decision hits your rules before anything executes.
Position limits, drawdown controls, exposure caps, and restricted assets, applied globally or per user. Violations get blocked before execution, and the reasoning trace records what happened and why.
Execution Infrastructure
Built-in execution across dozens of venues. Customize your environment in a single config.
Agents optimize execution based on your team's configuration: smart order routing, cross-chain settlement, and slippage optimization come built in. Scope the environment to a single venue or open it up to everything. Agents handle the rest.
Market Intelligence
Financial context out of the box. Custom integrations only if you want them.
Price feeds, funding rates, and sentiment in one tool layer covering crypto, equities, and prediction markets. Agents can operate without any extra integrations, and custom data sources drop in through the plugin layer.
import uv
# Connect to your harness instance
harness = uv.Harness(api_key="uv_sk_...")
harness.add_plugin("sentiment_scanner", handler=my_scanner)
# Global policy, enforced on every agent on your platform
harness.add_policy("max_leverage", ratio=3.0)
# Spin up an agent for a user account
# Each subtask dispatched to the right model
agent = harness.create_agent(
account="user_abc123",
models=["sonnet", "haiku", "frontier-mini"],
signals=["imbalance_shift", "vol_regime_change"],
tools=["place_order", "manage_position"],
plugins=["sentiment_scanner"],
)
# Account-level policies, scoped to this user only
agent.add_policy("max_position", usd=25_000)
agent.add_policy("drawdown_stop", pct=-0.05)
agent.deploy()Start in days.
Configure your agent environment with plugins and policies, then integrate into your platform with a widget or API key.
Widget
Drop agent UI into your app. Your users interact with agents inside your platform, under your brand.
API / SDK
Create agents, push policies, manage user accounts programmatically. Build exactly the experience your platform needs.
Co-Build
Our engineering staff works closely alongside your team to design and ship a world-class agent experience: custom environments, bespoke policies, and full compliance sign-off before you go live. Book a call →
Build or buy?
Ship agents next month, not next year.
Engineering cost assumes a 2–3 person in-house agent team. Inference comparison assumes per-event frontier-model calls vs. event-driven routing; methodology on request.
Common
questions
Every trade the Harness executes can become a decision episode: the data layer labs train on.
How does this get past our risk committee?
+The policy engine enforces your risk framework on every decision before anything executes: position limits, exposure caps, restricted assets. The controller system provides full audit trails for every action, including the reasoning behind it. Built to support regulatory requirements out of the box with minimal changes to the core system.
What happens when a user's agent does something dumb?
+It gets blocked before execution. The reasoning trace logs what happened and why it was stopped. The user gets a clear explanation. No action that violates your rules ever reaches an exchange.
How does this scale to thousands of retail users?
+Our agents run on a proprietary trading engine designed for platform scale. We currently run 750+ agents concurrently; the architecture shards horizontally per account, so platform-scale deployments are a capacity-planning exercise, not a rewrite. Event-driven inference means agents only consume compute when something meaningful happens, not on a polling loop.
Can users chat with their agents?
+Yes. They can ask why it made a decision, what it's watching, or tell it to adjust. The agent responds with its actual reasoning, not a canned answer. Users can understand what's happening and steer it.
What models does it support?
+Any of them. OpenAI, Anthropic, Mistral, Llama, your own fine-tunes. We route tasks to models, not sell them. Swap providers anytime without changing your integration.
How fast can we go live?
+Custom deployment: Our team embeds with yours. Working pilot on one user segment within the first month, full rollout within 90 days.
Not a 6-month integration.
Ship agents on your platform.
Tell us about your platform and we'll show you what the integration looks like.
Schedule a CallEngagements are scoped per platform; co-build engineering is included.
30-minute call. We'll tell you if the Harness is a fit.