SKALE Labs has launched Agent Pit, a paper-trading sandbox for autonomous AI agents built to test prediction-market strategies before developers deploy them on venues such as Polymarket. The product, the first release from SKALE’s Agentic Venture Studio, recreates core prediction-market functions without requiring users to risk capital or pay transaction fees.
Agent Pit is designed around a practical problem for developers building automated trading systems: strategies that appear effective in theory can perform very differently once they confront changing prices, incomplete information and the mechanics of a live market. The platform lets builders test an agent’s decisions against simulated markets, refine its model and measure outcomes before connecting it to markets that handle real money.
The launch places SKALE more directly in the growing intersection of AI agents, onchain finance and event-based trading. Prediction markets have become an increasingly active venue for trading contracts tied to elections, economic releases, sports and other future events, creating an environment where automated systems can react to new information faster than manual traders.
A simulation built around prediction-market mechanics
SKALE said Agent Pit mirrors Polymarket-style market structure, including order books, market-resolution frameworks and live event-data feeds. Order books match bids from buyers with offers from sellers, while a resolution framework determines how a market settles once an event’s outcome is known. Replicating both features gives developers more useful feedback than a simplified price-only simulation.
The platform supports backtesting, which applies a strategy to historical data to examine how it would have behaved under previous market conditions. It also allows rapid live simulation, letting developers modify parameters and run new tests as market data changes.
Developers can tune an agent’s instructions, risk settings and trading logic inside the sandbox, then compare simulated returns on public leaderboards. The leaderboards rank models by simulated return on investment, or ROI, creating a visible record of which strategies have performed best under Agent Pit’s conditions.
A strong simulated result does not establish that a strategy will generate comparable results in a live market. Real deployment introduces liquidity constraints, rapidly moving prices, competition from other automated traders and the risk that markets behave differently from their historical patterns. Yet a testing environment can identify obvious weaknesses before a developer commits capital, especially for strategies that would otherwise require large numbers of small test transactions.
SKALE said models tested in Agent Pit can later be moved to live settings with similar market structures. That pathway could make the sandbox useful as a development layer for teams seeking to build agents for established prediction markets rather than as a standalone destination for simulated trading.
Zero-gas design targets high-frequency testing
Agent Pit runs on SKALE’s network, which the company describes as a high-throughput, zero-gas blockchain. SKALE, founded in 2018, operates a modular multichain network of EVM-compatible Layer 1 chains designed for scalable applications.
The zero-gas structure is particularly suited to testing automated strategies. An AI agent may need to place, cancel or revise many simulated orders while it evaluates a market. On chains where users pay a fee for each action, those experiments can become costly before a strategy has demonstrated any value.
SKALE says its network has saved users more than $11 billion in transaction fees. The company did not frame that figure specifically around Agent Pit, but the new product uses the same fee-free execution model to remove a common cost barrier for developers running repeated agent experiments.
The architecture also changes the type of strategies developers can explore. A model that depends on responding to small changes in probability or incoming information may require frequent adjustments. Eliminating per-transaction charges would allow teams to test those approaches without having fee costs distort the simulated outcome.
Prediction-market growth draws automated strategies
SKALE cited a sharp expansion in activity across major prediction-market networks as part of the rationale for its AI-agent focus. According to the company, monthly global volume on the largest platforms increased from less than $5 billion in September to almost $24 billion in April 2026. It also said daily volume reached $425 million on one day in February 2026.
Those figures illustrate why prediction markets are attracting more sophisticated trading tools. Contracts can reprice within seconds when a poll changes, a government report is released or a live event produces new information. Automated systems can monitor several markets and data sources at once, although their speed does not remove the challenge of interpreting unreliable, delayed or misleading signals.
The use of public leaderboards may give developers and observers a way to track which models perform well across Agent Pit’s simulations. It could also encourage strategies optimized for the platform’s specific rules rather than for live-market conditions, making the quality of the simulation central to whether leaderboard results are useful beyond the sandbox.
Part of SKALE’s AI and stablecoin strategy
Agent Pit follows SKALE’s recent push into applications designed for autonomous software. Last year, the team launched a Layer 3 blockchain that combines Base Layer 2 technology with SKALE’s Layer 1 infrastructure. SKALE said the chain was designed for agentic workloads, including activity connected to the x402 AI payments protocol.
That earlier work focused on infrastructure for agents that need to transact. Agent Pit moves the strategy closer to the application layer by giving developers a venue to train and evaluate trading behavior.
The result is a more concrete use case for SKALE’s zero-gas design: rather than simply offering inexpensive blockchain capacity, the network is being positioned as an environment where automated agents can repeatedly test decisions before those decisions encounter live prediction-market liquidity and real financial risk.
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