Applications Open · Builder Series

AGORA.

Where AI agents make markets. A builder series for agents that trade, invest, create, and interface with markets — settled instantly on Arc with USDC.

In classical Athens, the agora was the heart of the city — where citizens traded grain and oil, money-changers leaned on their tables, oracles were consulted, and news was made by the speaking of it. It was the original information-processing machine. AGORA is its descendant, populated by autonomous agents instead of citizens.

Apply to build → See the RFBs ↓
Event window
14 days
Dates
May 11 → May 25
Settlement
Arc · USDC
RFBs
06 open
Plato's Academy mosaic from Pompeii — philosophers in dialogue under a tree
Plato's Academy Pompeii · 1st c. BCE
Overview
01

The agora was where Athens did its thinking out loud. Prices, opinions, and news converged in one square because that's where the people were. Markets are still doing the same job today — they are the social technology by which a civilization aggregates knowledge and decides what things are worth.

AI agents are the new citizens. They can monitor the agora around the clock, deliberate over thousands of signals, and act on the marginal one — the kind of continuous, comparative reasoning Aristotle described and that humans are too slow to perform across every market simultaneously. Treat the agora as substrate, treat your agent as a participant in it, and the right products start to suggest themselves.

Arc gives this substrate the right physics. Sub-second deterministic finality means trades settle instantly and irreversibly — no waiting, no reorgs. ~$0.01 transaction fees paid in USDC (not volatile gas tokens) make high-frequency, low-margin strategies economical onchain for the first time. Money-changers had their tables; agents have Arc.

Settlement
Sub-second deterministic finality on Arc
Cost per tx
~$0.01 in USDC, not volatile gas
The School of Athens by Raphael — philosophers gathered under classical arches
"All things are an exchange for fire, and fire for all things — even as wares for gold and gold for wares."
The School of Athens Raffaello Sanzio · 1509–1511
// Heraclitus, Fragment 90 · c. 500 BCE
The
Stack
02

Circle's developer platform on Arc. Use what you need — these are the primitives best suited for agents that touch markets.

01
CCTP
Move collateral between platforms instantly. Cross-chain arbitrage execution.
02
Gateway
Unified collateral view across 11+ chains with instant (<500ms) cross-chain transfers — critical for arbitrage speed.
03
App Kit Bridge
Cross-chain USDC transfers. Move funds into and out of prediction markets, rebalance collateral across perp platforms.
04
App Kit Swap
Convert between USDC and EURC. Enables multi-currency markets and FX-aware trading.
05
Wallets
Trading and betting accounts with automated key management.
06
Contracts
Position management, liquidation protection, hedging logic.
07
USYC
Yield-bearing token (tokenized money market fund) — park idle capital in yield while waiting for opportunities.
Requests
for Builders
03

RFBs — Requests for Builders — are our version of YC's Requests for Startups. Six open problems we think are worth solving in the spirit of the agora. Build any of them, build something adjacent, or surprise us. Each names the problem, what the AI actually decides, and what builders ship. The best submissions are always what you care most about.

The problem. Perpetual futures trading requires 24/7 monitoring, split-second decisions on leverage and liquidation risk, and constant optimization across multiple platforms. How does an AI manage leveraged positions autonomously while protecting against catastrophic losses? Can Arc serve as a settlement / trading chain across existing perp markets?

What the AI decides
  • When to open/close leveraged positions based on market conditions
  • Optimal leverage level (2x vs 10x) based on volatility and conviction
  • Dynamic stop-loss and take-profit levels that adapt to market regime
  • Cross-platform execution (Hyperliquid, dYdX, GMX, Vertex) — Arc as a settlement chain across protocols
  • Funding rate arbitrage opportunities
  • Automated liquidation protection (deleverage or add collateral)
What builders create
  • Multi-platform position management dashboards
  • Liquidation protection bots with cross-chain collateral movement
  • Funding rate monitoring and arbitrage execution systems
  • Risk management frameworks with dynamic leverage adjustment
Example builds
  • PerpAI — manages positions across multiple perp DEXs, auto-migrates to best funding rates
  • SafeLeverage — conservative AI at 2-3x leverage with tight risk management
  • FundingFarmer — captures funding rate arbitrage across venues
Traction metrics
  • Number of active traders
  • Total trading volume
  • Demonstrated PnL and Sharpe ratio
  • Assets under management

The problem. Prediction markets offer alpha through information asymmetry, but finding mispriced contracts requires synthesizing news, data, and sentiment at speed. How does an AI identify +EV bets and size positions optimally?

What the AI decides
  • Which prediction markets have mispriced probabilities based on data analysis
  • Optimal bet sizing using Kelly Criterion or alternatives
  • When to hedge positions or close early for profit
  • Portfolio construction across correlated markets
  • Information source credibility weighting
What builders create
  • Prediction market analytics with AI-driven probability estimates
  • Automated betting agents with Kelly Criterion position sizing
  • Cross-market arbitrage detection and execution tools
  • Information aggregation systems that weight source credibility
Example builds
  • InsightAgent — analyzes news, polls, social sentiment; suggests +EV bets with confidence intervals
  • PredictPortfolio — builds diversified prediction portfolio across sports, politics, crypto events
  • ArbitrageOracle — finds mispricings between prediction markets
Traction metrics
  • Number of active users
  • Prediction accuracy rate
  • Total volume wagered
  • Documented returns

The problem. Most prediction markets either suffer from thin liquidity or don't exist for the events people actually want to bet on. Can you leverage specific insights on the market landscape, the communities you're part of, and domain-specific knowledge to launch new prediction market verticals?

What builders create
  • Macroeconomic — CPI releases, Fed decisions, jobs data, GDP figures (short-duration, high-frequency)
  • Geopolitical — elections, conflicts, trade policy, energy shocks
  • Institutional hedging — onchain tools for hedging macro + geopolitical risk, simpler than traditional derivatives
  • Multi-currency settlement — EU inflation in EURC, US events in USDC
  • Internal corporate prediction markets for forecasting and planning
  • Market creation tools with automated liquidity provisioning
  • Oracle integrations for reliable resolution
  • Forex prediction markets, specifically the USDC ↔ EURC pairing
Example builds
  • MacroOracle — markets around upcoming Fed meetings, auto-resolves on official data feeds
  • EventHedge — institutional tool for hedging geopolitical risk with prediction market positions
  • CorpForecast — internal markets for sales forecasts, project timelines, hiring decisions
Traction metrics
  • Number of markets created
  • Total liquidity provided
  • Market resolution accuracy
  • Trading volume per market

The problem. Portfolio management requires constant rebalancing, regime detection, and tax optimization — tasks that are tedious for humans and require cross-chain coordination. How does an AI manage a portfolio that adapts to changing market conditions?

What the AI decides
  • Asset allocation based on market regime (risk-on vs risk-off)
  • When to rebalance vs let winners run
  • Yield allocation — park capital in USYC during risk-off periods
  • Tax-loss harvesting opportunities and execution timing
  • Correlation-based diversification across DeFi and TradFi
  • Risk management — reduce exposure during high volatility
What builders create
  • Goal-based portfolio management interfaces
  • Cross-chain rebalancing infrastructure with CCTP / Gateway
  • Tax-loss harvesting automation tools
  • Regime detection models with automatic allocation adjustment
Example builds
  • AdaptiveFolio — set goals (“retire in 10 years, moderate risk”), AI manages everything underneath
  • TaxOptimizer — maximizes after-tax returns through intelligent harvesting
  • RegimeShift — detects market regime changes, adjusts allocation automatically
Traction metrics
  • Number of users
  • Assets under management
  • Returns vs benchmark (Bitcoin, S&P 500)
  • Portfolio turnover and rebalancing frequency

The problem. Price discrepancies across exchanges and chains exist but disappear in seconds. Capturing arbitrage requires instant detection, cross-chain execution, and precise cost accounting. How does an AI find and execute profitable arbitrage before opportunities vanish?

What the AI decides
  • When real arbitrage opportunities exist (price differences across platforms)
  • Optimal trade sizing accounting for slippage and fees
  • Which bridge / route to use (CCTP vs alternatives)
  • Whether opportunity is profitable after all costs
  • Risk management — what if price moves during execution?
What builders create
  • Real-time price monitoring across CEXs and DEXs
  • Cross-chain execution engines optimized for speed
  • Profitability calculators accounting for all fees and slippage
  • Risk-adjusted opportunity scoring systems
Example builds
  • ArbAgent — finds CEX/DEX discrepancies, executes via CCTP
  • TriangularArb — multi-hop arbitrage (USDC → ETH → BTC → USDC) across chains
  • FundingArb — arbitrage between spot and perps funding rates
Traction metrics
  • Number of arbitrage opportunities captured
  • Total profit generated
  • Average execution time
  • Success rate (profitable executions / attempts)

The problem. Copy trading is popular but most followers blindly mirror leaders without understanding risk or detecting when strategies degrade. How does an AI intelligently select, weight, and monitor traders to copy?

What the AI decides
  • Which traders to follow based on risk-adjusted returns
  • How much capital to allocate to each trader
  • When to stop following (detecting strategy degradation)
  • Portfolio construction across multiple signal sources
  • Signal quality filtering — ignore noise, follow alpha
What builders create
  • Trader performance analytics and ranking systems
  • Copy trading infrastructure with AI-driven allocation
  • Strategy degradation detection algorithms
  • Multi-signal aggregation platforms
Example builds
  • SmartMirror — picks best 5 traders for your risk profile, adjusts allocations
  • SignalAggregator — combines signals from 20+ traders using ensemble methods
  • CopyProtect — copies winning strategies but adds AI risk limits
Traction metrics
  • Number of leaders and followers
  • Total assets being copy-traded
  • Performance vs leader performance
  • Follower retention
Voices
from the Agora
“All things that are exchanged must be somehow comparable.”
Aristotle Nicomachean Ethics · Book V // on price discovery
“Is not he a benefactor who reduces the inequalities and disproportions of goods to equality and proportion?”
Plato Laws · Book XI // on arbitrage as public good
“The agora is, as it were, the heart of the city.”
Aristotle Politics · Book VII // on the marketplace as substrate
Research
04

Research that points directly to buildable products. Hacks, hooks, and angles where Arc's $0.01 fees and sub-second finality unlock something that wasn't economical before.

01

Trading-R1 is a large-scale financial reasoning model mirroring the DeepSeek-R1 design — its value is the reasoning trace, not the trade, which makes the trace itself the product. With Arc's flat $0.01 fees, the full reasoning trace can be hashed and pinned (trace to IPFS / Irys, hash on Arc) without eroding PnL. That unlocks a new market type: bets on which reasoning patterns converge to profit, with TradingAgents v0.2.4's structured outputs (Trader / Research Manager / Portfolio Manager all emit JSON-schema'd reasoning) as the machine-readable substrate.

Ties to RFB 06 — Social Trading Intelligence. Copy-trading has always been a proxy for intelligence and access. What people actually want to copy is how someone thinks — which traces finally make legible and Arc finally makes affordable to publish.

02

Builder codes let an agent that recommends a bet take a cut of every fill that originates from its recommendation — no custody, no token, just on-chain attribution. Every trading agent today is unmonetized: the framework gives picks, the user trades them somewhere else. The hack: a thin "agent-as-builder" wrapper that registers any agent framework as a Polymarket V2 builder, exposes its structured outputs as a signed feed, and earns USDC builder fees per fill — Arc's $0.01 fees make per-pick economics work at retail size.

Ties to RFB 02 — Prediction Market Trader Intelligence. This is the actual answer to "how does InsightAgent make money?" — not subscription, builder fees.

03

Buried in the metadata of NostalgiaForInfinity is a finding most people miss: many commits are iterativv adding meme-coins to the blacklist — BLUM, MONPRO, UXLINK, IZI, YZY, BSY, WAT, RAIN. This is (likely) a single human doing real-time, high-frequency rugpull detection with a public commit log, currently free. The hack: parse the NFI commit feed, mint each blacklist addition as a signed Arc event ("iterativv-blacklisted-X at block N"), and seed a prediction market vertical of "will [coin] lose >50% in 7 days". Sub-second finality matters because the blacklist signal front-runs the rug — the market needs to open in the same block iterativv pushes.

Ties to RFB 03 — Prediction Market Verticals. A vertical of rugpull markets where the resolution signal is a maintainer with provable track record, not an oracle committee.

04

TradingAgents-CN, AlpacaTradingAgent, and the original TauricResearch/TradingAgents library are all reskins of the same architecture; what differs is which data sources their locale's investors trust. The framework is interchangeable; the translation layer is the moat. Polymarket only operates in English-language US events because translating Mandarin macro news into a well-formed prediction market question is the bottleneck. The hack: a market where agents bid in USDC for the right to translate a non-English news event into a Polymarket-shaped question, with builder fees flowing back to the translator on every fill that originates from their question.

Ties to RFB 03 — Prediction Market Verticals. The actual mechanism for emerging-markets prediction verticals — pay translators per-fill, not per-translation.

05

Top HL whales migrate across forks (Aster, Polynomial, etc.). The hack: an Arc-native ERC-20 holding USDC that auto-rebalances exposure across HL forks based on top-trader migration. Each rebalance is a Gateway cross-chain move; weekly rebalances cost cents on Arc rather than dollars elsewhere. The rebalance signal is the research — "where smart money is currently trading." Buyers hold one token; the underlying is a live migration-tracking index.

Ties to RFB 04 — Adaptive Portfolio Manager and RFB 06 — Social Trading Intelligence. A portfolio product whose allocation rule is itself a copy-trading insight.

06

HL leaderboard rank may not persist out-of-sample. The hack: a USDC performance bond on Arc for a given whale that users can stake alongside. A smart contract reads leaderboard rank via oracle; if the leader falls below a defined threshold, the bond slashes proportionally and the slash settles in under a second. The research output (the empirical decay function) becomes the smart-contract slash schedule directly. Arc's cheap fees mean this works at retail follower size, whereas on other chains the gas would erode the bond.

Ties to RFB 06 — Social Trading Intelligence. Copy-trading with skin in the game on the leader, not just the follower.

How we
judge
05

We weigh agency and traction equally. Real users matter, real decisions matter, and we want to see both. These weightings are recommendations — judges have the final say, and the best projects tend to break the rules.

30%
Agentic Sophistication
How much does the AI actually decide vs just automate? Full autonomy beats meaningful agency beats AI-flavored automation.
30%
Traction
Real users, real transactions, real volume during the event window. Great founders ship and get users in two weeks.
20%
Circle tool usage
Creative and effective use of the Circle developer platform. Wallets, CCTP, Gateway, App Kit, Contracts, USYC, USDC.
20%
Innovation
Novel approaches, emergent behavior, research insight. New territory beats polished re-runs.
Apply
06

Build agents
that move markets.

Two weeks. Six RFBs. Real users. Real settlement on Arc. If you're building an agent that trades, apply.

Apply to build → Read the RFBs ↓
AGORA · Canteen × Circle · 2026
CANTEEN × CIRCLE × ARC
thecanteenapp.com