AI Sports Betting Agents 2026: Full Landscape

AI sports betting agents are autonomous software systems that analyze sports data, identify betting opportunities, and place wagers on sportsbooks and prediction markets with minimal or no human intervention. In 2026, these agents are moving from experimental prediction market tools into the mainstream sports betting industry, creating a new category of products that sits between traditional handicapping software and fully autonomous trading bots.

This landscape overview covers every major AI sports betting agent available in 2026 – what they do, how they work, where they fall short, and how the technology connects to the prediction market agent ecosystem. If you are building or evaluating, this is the definitive map.

Series context: This is Article 5 of 5 in the Agent Infrastructure series. For prediction market wallets, see Best Agent Wallet for Prediction Markets. For payment protocols, see Agentic Payments Protocols. For wallet security, see Agent Wallet Security. For the foundational agent stack, see The Agent Betting Stack.

The Sports Betting Agent Landscape

2026 is the year AI agents cross from prediction markets into mainstream sports betting. For two years, autonomous trading agents have been a prediction market phenomenon – bots on Polymarket and Kalshi trading binary event contracts using open APIs and crypto-native infrastructure. Sports betting was always the larger market, but lacked the open infrastructure prediction markets offered developers.

Three forces are driving convergence:

Prediction market infrastructure matured. The agent betting stack – identity, wallet, trading, intelligence – is well-defined. Developers on Polymarket and Kalshi have proven autonomous agents can trade profitably. The playbook exists; now it is being adapted for sports.

Sportsbooks are building prediction market products. DraftKings launched Predictions, a binary event contract product operating under existing gaming licenses. FanDuel and BetMGM are exploring similar products. The line between “sportsbook” and “prediction market” is blurring.

Consumer-facing betting agents appeared. Products like Billy Bets and Sire package AI-driven sports analysis and bet execution into consumer products – the first mainstream attempts to put an AI agent between bettor and sportsbook.

Market Size Context

The US legal sports betting market took $166.9 billion in handle in 2025 and produced a record $16.96 billion in operator revenue, with hold climbing to 10.2% on parlay adoption. By comparison, Polymarket’s cumulative volume has crossed $10 billion and Kalshi cleared $31 billion in a single month during the 2026 World Cup. The venues are converging in size faster than anyone predicted – and the prediction market side has the developer infrastructure. The gap matters because sports betting represents massive volume – but accessing it programmatically is harder and defended by sportsbooks that actively discourage automation.

Billy Bets

Billy Bets is one of the first consumer-facing AI sports betting agents to reach meaningful public visibility. It positions itself as an AI-powered betting assistant that analyzes games, identifies value, and can execute bets on behalf of users.

How It Works

Billy Bets uses a pipeline that combines LLM-driven analysis with quantitative sports modeling:

1. Data ingestion. Player statistics, team performance metrics, injury reports, weather conditions, and historical matchup data feed into the system. The exact data providers are not publicly disclosed, but the analysis output suggests integration with multiple statistical databases covering major US sports (NFL, NBA, MLB, NHL) and select international leagues.

2. LLM analysis layer. A large language model processes the ingested data alongside contextual information – narratives around teams, coaching changes, momentum factors – to generate qualitative assessments that supplement quantitative models. This is the distinguishing feature: traditional sports models are purely statistical, while Billy Bets incorporates the kind of contextual reasoning that LLMs handle well.

3. Bet recommendation engine. The system identifies bets where its model estimates a probability significantly different from the implied probability of the available odds. These are surfaced as recommendations with confidence levels.

4. Execution (on-chain). Billy manages its own wallet on Base and places bets autonomously on on-chain venues – Polymarket, Overtime Markets, and Myriad Markets. Every pick is recorded on-chain, which gives Billy something no traditional tipster or closed AI product has: a verifiable public track record.

Architecture

Billy Bets’ architecture is more documented than most consumer betting products:

• Agent framework: Built on the Eliza agent framework
• Model layer: An ensemble spanning Claude, GPT-4o, LLaMA, and Perplexity
• Data layer: SportsTensor data integration
• Wallet layer: Base-native wallet under the agent’s own control
• User interface: The Billy Terminal front end (open beta)

Billy Bets raised a $1M pre-seed from Coinbase Ventures, Virtuals Ventures, Contango Digital, and CMS Holdings, and has processed over $1M in betting volume.

Performance

Billy Bets has shared select performance results, but comprehensive, independently verified track records are not available as of March 2026. Claims of profitability are easy to make and hard to verify. Any published win rates should be evaluated with healthy skepticism until long-term, audited results are available.

Strengths

• Consumer-friendly. The product is designed for bettors who want AI assistance without building their own systems. The onboarding and UX are polished relative to developer-focused tools.
• Automated execution. Moving from analysis to bet placement without manual steps removes friction and execution delay.
• Multi-sport coverage. Covers major US sports leagues, expanding the opportunity set beyond what most custom bots target.

Weaknesses

• Closed-source. The model, data sources, and execution logic are proprietary. Users cannot inspect or verify the underlying analysis.
• Limited transparency. Without audited, long-term performance data, it is difficult to evaluate whether the system generates genuine edge or is packaging sophisticated-sounding analysis around noise.
• Vendor risk. Users depend entirely on Billy Bets’ continued operation, model quality, and venue connectivity.

Target Audience and Pricing

Billy Bets targets recreational and semi-serious sports bettors who want AI-assisted decision-making without the technical overhead of building their own agent. Pricing details vary – the product has used both subscription and freemium models. Check their current offering directly, as pricing in this space changes frequently.

Sire

Sire is an AI sports betting platform that emphasizes autonomous agent capabilities, positioning itself as a more technically sophisticated option compared to consumer-first products like Billy Bets.

Structurally, Sire is an agentic DAO – an on-chain sports betting hedge fund rather than a subscription picks product. It runs on Score Vision’s Subnet 44 on Bittensor and ships three products: aLink, a real-time betting terminal with direct Kalshi event access; aVault, an AI-managed vault for directional event bets; and DeltaVault, a lower-risk market-making vault. Users allocate capital to vaults instead of following picks.

How It Works

Sire’s approach centers on a multi-model ensemble architecture:

1. Multi-model ensemble. Rather than relying on a single model, Sire reportedly runs multiple models in parallel – statistical models, machine learning classifiers, and LLM-based reasoning – then aggregates their outputs. Ensemble approaches are standard in quantitative finance and reduce the risk of any single model’s blind spots driving poor decisions.

2. Real-time odds tracking. The platform monitors odds movements across multiple sportsbooks in real time, identifying line movement patterns and value windows. This is the same core capability that powers sports betting arbitrage bots, but Sire uses it for value identification rather than pure arbitrage.

3. Contextual analysis. Like Billy Bets, Sire incorporates non-statistical factors – public sentiment, sharp money indicators, reverse line movement – into its assessment pipeline.

4. Position management. Sire includes portfolio-level position management, tracking exposure across bets and suggesting hedging or cash-out opportunities.

Architecture Overview

Sire’s technical architecture appears more modular than Billy Bets, designed with a developer-adjacent audience in mind:

• Model layer: Multiple specialized models per sport, potentially including sport-specific fine-tuned models for NBA, NFL, and soccer
• Signal aggregation: A meta-model or weighted scoring system that combines individual model outputs into unified bet recommendations
• Odds infrastructure: Real-time odds feed integration with historical line movement storage for pattern detection
• Execution: API-level sportsbook integration where available, with a focus on supported books

Performance and Track Record

Sire has published some performance metrics, including claimed ROI figures. As with all AI betting products, key questions apply: What time period? Which sports? Paper trades or actual executions? Third-party audit? As of March 2026, independently verified long-term track records are not publicly available.

Strengths

• Ensemble architecture. Multi-model approaches are more robust than single-model systems, consistent with best practices in quantitative analysis.
• Real-time odds integration. Active odds monitoring enables the system to identify value windows and react to line movements.
• Position-level management. Tracking portfolio exposure, not just individual bets, is a more sophisticated approach than simple bet recommendation.

Weaknesses

• Transparency gap. Despite a more technical positioning, the underlying models and their training data are not open for inspection.
• Unverified performance. Published performance numbers lack independent verification.
• Market coverage. Coverage depth varies by sport and league – some markets receive more analytical attention than others.

Target Audience and Pricing

Sire targets a more technically savvy audience than Billy Bets – bettors who understand concepts like expected value, line movement, and position sizing. Pricing has included tiered subscription plans with varying levels of access to features and bet volume. Consult their current pricing directly.

DraftKings Predictions

DraftKings Predictions represents a fundamentally different entry point into the AI sports betting agent landscape. Rather than building a standalone AI agent, DraftKings is extending its existing, regulated sportsbook platform with prediction market-style binary event contracts.

How It Works

DraftKings Predictions allows users to trade binary event contracts – similar in structure to Kalshi contracts or Polymarket shares. A contract resolves to $1 if the event occurs, $0 if it does not. Users buy or sell contracts at prices between $0.01 and $0.99, where the price reflects the market’s implied probability of the event.

Key characteristics:

• USD-denominated. Unlike Polymarket (USDC on Polygon), DraftKings Predictions operates in US dollars through existing DraftKings account balances.
• Federally regulated. DraftKings acquired Railbird Technologies, a CFTC-registered Designated Contract Market, and runs Predictions on its own exchange (DKeX) inside the DraftKings app. Because the venue is CFTC-regulated rather than state-licensed, Predictions launched in 38 states – including California, Texas, Florida, and Georgia, where DraftKings has no sportsbook license. This is the regulatory unlock: prediction market contracts reach states that never legalized sports betting.
• Centralized order matching. DraftKings runs the matching engine centrally, not on a public blockchain. This means faster execution but less transparency than a CLOB like Polymarket’s.
• Sports and non-sports events. Covers sporting events (will the Eagles score first?), player props (will Jokic record a triple-double?), and some non-sports events, depending on state regulatory approval.

API Access and Automation Potential

DraftKings has developer-facing APIs for its fantasy and sportsbook products, but the Predictions product’s API access for automated trading is limited as of March 2026. The platform is designed primarily for manual, consumer interaction through the DraftKings app.

Several factors suggest API access will expand: DraftKings has historically supported developer integrations, competing with Kalshi (which explicitly supports algorithmic trading via API) creates pressure, and institutional firms want regulated prediction market venues. For now, developers targeting DraftKings Predictions will likely need browser automation or reverse-engineered endpoints, or wait for official API support. This contrasts sharply with Polymarket and Kalshi, where open APIs are core to the platform.

Why This Matters for Agent Developers

DraftKings Predictions matters for three reasons:

1. Regulatory access. An agent that trades DraftKings Predictions can operate in the 38 states where the CFTC-regulated DKeX exchange launched – including states with no legal sports betting. A Polymarket agent now has a regulated US path too: Polymarket relaunched for US users under CFTC designation, with the waitlist removed in May 2026. A Kalshi agent operates nationally under CFTC regulation, though its sports event contracts remain contested by state regulators.

2. Mainstream liquidity. DraftKings has millions of active users. As Predictions scales, it could offer deeper liquidity on sports-adjacent events than Polymarket or Kalshi, particularly for popular sporting events.

3. Convergence destination. DraftKings Predictions is where sports betting and prediction markets meet. An agent that can trade both DraftKings Predictions and traditional DraftKings sportsbook lines from a single platform is a convergence play that is not possible on Polymarket or Kalshi alone.

Connection to Regulated Prediction Markets

DraftKings Predictions and Kalshi now share the same regulatory architecture: both are CFTC-regulated Designated Contract Markets listing binary sports event contracts. The difference is distribution – DraftKings pushes contracts through a mainstream sportsbook app with millions of users, while Kalshi is exchange-first with mature API access.

AI Betting Insights Apps: Rithmm and Hall of Fame Bets

Below full agents sits a category of AI-powered insights apps – products that model games and surface picks but never touch execution. Rithmm is the most visible: a subscription app offering AI-driven player prop and game projections across NFL, NBA, MLB, and college sports, aimed at recreational bettors who want model output without building anything. Hall of Fame Bets packages AI picks with celebrity branding. These are decision-support tools, not agents – no wallet, no API, no automated execution, and no ToS exposure at sportsbooks. That makes them the lowest-risk entry point and the least interesting architecturally. For a full evaluation, see the Rithmm review.

To be precise about the distinction: Rithmm is not an AI sports betting agent. It generates model-based projections and pick recommendations but does not place bets. An agent adds autonomous execution. Rithmm competes with handicappers; agents like Billy Bets compete with your own hands on the bet slip.

Tech Stack Mapping: Sports Betting Agents vs. Prediction Market Agents

If you have built a prediction market agent using the agent betting stack, you know the architecture. A sports betting agent uses the same four layers – data, intelligence, execution, wallet – but each layer’s implementation differs substantially.

Data Layer

Prediction market agents consume structured order book data from CLOBs (Polymarket’s API, Kalshi’s WebSocket feed), freely available through official APIs.

Sports betting agents need multiple data sources with no common format:

• Odds feeds: The Odds API, OddsJam, or direct sportsbook feeds for real-time lines across dozens of books
• Statistics: Player and team performance databases (e.g., nba_api, cfbd, Sportradar)
• Injury reports: Real-time injury and lineup feeds
• Weather data: Relevant for outdoor sports (NFL, MLB, soccer)
• Line movement history: Historical odds data for pattern detection

The data engineering challenge for sports betting agents is significantly larger.

Intelligence Layer

Prediction market agents analyze binary events: “What is the true probability of X?” Models can be relatively simple (sentiment analysis, poll aggregation, historical base rates) because event structure is standardized.

Sports betting agents must model multi-outcome events with complex dependencies:

• Spreads and totals require point-level modeling, not just win/loss probability
• Player props require individual performance modeling
• Parlays require correlation analysis between legs
• Live betting requires real-time game state tracking and rapid model updating

The intelligence layer for sports is harder to build, harder to validate, and requires more domain expertise. See the Agent Intelligence Guide for more on building the analysis layer.

Execution Layer

Prediction market agents place orders on CLOBs via well-documented APIs (py-clob-client, Kalshi REST API). The platforms welcome automation.

Sports betting agents face a hostile execution environment:

• Most sportsbooks prohibit automated betting in their terms of service
• API access for bet placement is rare or unofficial
• Sportsbooks actively detect and limit winning accounts
• Bet acceptance is not guaranteed – books can reject or limit wagers
• Browser automation is fragile and breaks when sportsbooks update their interfaces

Wallet Layer

Prediction market agents use crypto wallets (EOA, Coinbase Agentic Wallets, Safe multisig) for on-chain markets like Polymarket, or USD API accounts for Kalshi. See Best Agent Wallet for Prediction Markets for the full comparison.

Sports betting agents mostly operate with traditional fiat wallets:

• USD deposits via bank transfer, debit card, or payment apps
• Account balances held by the sportsbook (custodial)
• No on-chain transaction transparency
• Withdrawals subject to sportsbook policies and verification

The wallet architecture for sports betting agents is simpler but offers less control and transparency.

Comparison Table

Sportsbook Arbitrage with Agents

Arbitrage – betting both sides of an event across different sportsbooks to guarantee a profit – is the most concrete, proven use case for sports betting automation. Agents are uniquely suited for arb detection because the opportunity windows are short (seconds to minutes) and the scanning requirements exceed human capability.

How Agents Find Arb Opportunities

The process follows the same structure described in the Sports Betting Arbitrage Bot guide:

1. Continuous odds monitoring. The agent polls or streams odds from multiple sportsbooks via aggregation APIs (The Odds API covers 70+ books)
2. Implied probability calculation. Convert odds from every book to implied probabilities for each outcome
3. Arb detection. Flag combinations where the sum of best implied probabilities across books drops below 100%
4. Stake calculation. Compute optimal allocation across sides using the equal-profit formula
5. Execution. Place bets on both sides before the odds move

For a complete implementation with Python code, see the arb bot guide.

Cross-Sportsbook vs. Cross-Platform Arbitrage

There are two distinct arb strategies agents can pursue:

Cross-sportsbook arbs exploit pricing differences between traditional sportsbooks (DraftKings vs. FanDuel vs. BetMGM). These are the classic arb opportunities. They tend to be small (1-3% margin), short-lived (seconds to minutes), and aggressively defended by sportsbooks.

Cross-platform arbs exploit pricing differences between sportsbooks and prediction markets – for example, a DraftKings line on a game outcome vs. a Polymarket contract on the same event. These arbs can be larger (3-8%) and persist longer because the platforms serve different customer bases and reprice through different mechanisms. See the Cross-Platform Arbitrage guide and the Cross-Market Arbitrage guide for details.

The Account Limitation Problem

The critical challenge for sportsbook arb agents is account longevity. Sportsbooks identify arb bettors through win-rate monitoring, bet pattern analysis, network correlation (shared flagged account data), and timing analysis (bets placed immediately after line changes). Mitigation strategies – mixing arb bets with recreational action, varying bet sizes, focusing on books with slower detection – are temporary. None work indefinitely.

This is a structural difference from prediction markets. Polymarket and Kalshi do not limit winning traders – their exchange model profits from volume, not bettor losses. See Cross-Market Arbitrage for more.

Regulatory Landscape

The regulatory environment for sports betting agents is more complex and restrictive than for prediction market agents.

US State-by-State Licensing

Sports betting is regulated at the state level. As of 2026, 39 US jurisdictions have legal sports betting, roughly 31 with statewide online wagering, each licensing specific operators with their own rules on permitted bet types and consumer protections. For agents, this means: bets can only be placed in states where the sportsbook is licensed, geo-fencing requires the user to be physically present in a licensed state, and some states restrict certain bet types (e.g., college player props).

This contrasts with prediction markets: Kalshi operates under a single federal CFTC license, and Polymarket operates globally, serving US users through CFTC-regulated intermediated access since its November 2025 approval.

Sportsbook Terms of Service

This is the most immediate regulatory issue for sports betting agents. Nearly every major US sportsbook includes language in its Terms of Service that prohibits or restricts automated betting:

• Automated wagering prohibited. Most ToS explicitly ban the use of bots, scripts, or automated software for placing bets
• Account sharing prohibited. Using an agent to place bets on someone else’s behalf may violate ToS and state regulations
• Data scraping restricted. Some sportsbooks prohibit automated collection of odds data from their platforms (though third-party odds aggregators like The Odds API operate under their own data agreements)

Enforcement Reality

Enforcement is economic, not legal. Flagged accounts get bet size limits (sometimes as low as $1-$5 per wager) or outright closure with balances returned. There are no known cases of criminal prosecution for using betting bots on licensed US sportsbooks – the prohibitions are contractual, not criminal. Detection is imperfect: sophisticated agents mimicking human betting patterns can avoid detection for extended periods.

The Legal Gray Area

The key gray area is between “decision support” (AI recommends, human places the bet – generally acceptable) and “automated execution” (agent places bets without human intervention – ToS violation at most books, but not illegal). The hybrid approach – AI identifies opportunities, user manually executes – is the most likely to avoid account restrictions while still leveraging AI capabilities.

Practical advice for developers: build robust analysis and signal generation, but think carefully about the execution layer. Automated execution increases the risk of account limitations. For security considerations, see Agent Betting Security.

Prediction Market Convergence

The most important trend is the convergence of prediction markets and sports betting into a unified venue category, creating new opportunities for agent developers.

The Convergence Players

DraftKings Predictions is the most visible convergence product, but the field is crowding: FanDuel has explored prediction-style products, BetMGM has tested binary event contracts in select states, Kalshi runs single-game sports contracts at massive scale – $31 billion in volume in June 2026 alone during the World Cup – while defending them against enforcement actions in 15+ states, with the Third Circuit backing exclusive CFTC jurisdiction in April 2026. Polymarket relaunched in the US under CFTC designation (waitlist removed May 2026) and continues listing sports events alongside political and crypto markets.

How Prediction Market Tech is Merging with Sports Betting

Prediction market architecture – CLOBs, binary contracts, programmatic APIs – is being adopted by sportsbook operators for four reasons: order book efficiency (market-set prices are more capital-efficient than bookmaker models), new product types (binary contracts enable events like “Will Coach X be fired?” that traditional sportsbook formats handle poorly), developer demand (Polymarket and Kalshi proved API-first platforms attract trading volume), and regulatory arbitrage (some binary contracts can be offered under existing gaming licenses without CFTC approval).

What This Means for Agent Developers

The convergence creates a compelling possibility: one agent, multiple venues. An agent that can trade prediction markets AND place sports bets AND trade binary event contracts across DraftKings, Kalshi, and Polymarket is operating across the full spectrum of event-based markets.

The practical architecture for a multi-venue agent looks like this:

┌─────────────────────────────────────────────┐
│  INTELLIGENCE LAYER                         │
│  Unified event analysis engine              │
│  Probability estimation across all venues   │
├──────────┬──────────┬──────────┬────────────┤
│ Polymarket│  Kalshi  │DraftKings│ Sportsbooks│
│  CLOB    │ REST API │Predictions│  Odds API  │
│  (USDC)  │  (USD)   │  (USD)   │   (USD)    │
├──────────┴──────────┴──────────┴────────────┤
│  CROSS-VENUE ARBITRAGE ENGINE               │
│  Same event, different prices, profit       │
└─────────────────────────────────────────────┘

Cross-platform arbitrage between prediction markets and sportsbooks is already viable. See the Cross-Platform Arbitrage guide for implementation details.

The Unified Agent Opportunity

The end state is an agent that treats every event-based market as a single venue. An NBA game might have moneyline/spread on DraftKings Sportsbook, a “Will Lakers win?” contract on DraftKings Predictions, championship futures on Polymarket, and an “NBA Champion 2026” contract on Kalshi. A unified agent analyzes once, then deploys across all four venues – placing bets wherever its estimate diverges most from market prices.

For building cross-market agents, see Cross-Market Arbitrage.

Agent Comparison Table

Building vs. Buying

The build-vs-buy decision depends on your goals, technical ability, and risk tolerance.

When to Buy (Use an Existing Agent)

Use an existing product like Billy Bets or Sire if you want convenience over control, care about bet recommendations more than underlying technology, or your time is more valuable than the subscription cost. When evaluating, demand audited long-term performance data, assess transparency (can you see why it recommends specific bets?), calculate total cost including fees and profit-sharing, and consider exit cost (vendor dependency, data portability).

When to Build

Build your own agent if you have quantitative modeling skills, want full pipeline control, are targeting arbitrage (well-documented math, execution is the challenge), or want to operate across prediction markets AND sports (no existing product covers both well).

Recommended stack for building:

For the complete architecture guide, see The Agent Betting Stack. For intelligence layer specifics, see the Agent Intelligence Guide.

The Hybrid Approach

The most practical path for many developers: use existing signals from AI betting products or public models for ideation, then build custom execution targeting specific venues and bet types. This reduces the data engineering burden, preserves execution control, allows differentiation through venue selection, and keeps costs manageable.

For prediction market-specific bot selection, see Best Prediction Market Bots 2026 and How to Choose a Prediction Market Bot.

What Comes Next

The sports betting agent landscape in 2026 is where prediction market agents were in early 2025 – early, fragmented, and full of opportunity for builders willing to navigate the complexity. Five trends to watch:

1. API access expands. Sportsbooks competing with prediction markets for sophisticated traders will open more official APIs. This prediction is already landing: DraftKings launching Predictions on DKeX, its own CFTC-regulated exchange, in 38 states puts a mainstream sportsbook on exchange infrastructure – the exact architecture that makes programmatic access inevitable.
2. Convergence accelerates. The sportsbook/prediction market distinction continues blurring. Agents spanning both will have structural advantages.
3. Regulation clarifies. State gaming commissions and the CFTC will eventually address automated betting directly.
4. Open-source sports agents emerge. Prediction markets have open-source tools (py-clob-client, OctoBot, Polyclaw). Equivalent sports betting tools are coming.
5. Account limitation arms race. Sportsbooks improve bot detection; agents improve evasion. This dynamic is unique to sports betting.

For the full glossary of terms, see the Agent Betting Glossary.

See Also

Sport + Platform Bot Guides

• NBA Betting Bot for DraftKings – DraftKings NBA automation with player prop models
• NBA Betting Bot for BetOnline – offshore NBA bot strategies and totals modeling
• NBA Bot for Polymarket – prediction market NBA trading
• NFL Betting Bot for DraftKings – DraftKings NFL automation and prop scanning
• NFL Betting Bot for BetOnline – offshore NFL bot strategies
• NFL Bot for Polymarket – prediction market NFL trading
• MLB Betting Bot for DraftKings – DraftKings MLB automation
• Tennis Betting Bot for BetOnline – offshore tennis bot strategies
• Soccer Bot for Polymarket – prediction market soccer trading
• Election Bot for Kalshi – political prediction market automation

Strategy & Infrastructure

• Best Prediction Market Bots 2026 – rankings and reviews of prediction market-specific agents
• Sports Betting Arbitrage Bot Guide – complete developer guide to building an arb bot
• Cross-Market Arbitrage – arbitrage strategies across prediction market platforms
• Cross-Platform Arbitrage – arbing between sportsbooks and prediction markets
• The Agent Betting Stack – the four-layer architecture for autonomous agents
• Offshore Sportsbook API – API access for offshore sportsbooks
• Best Agent Wallet for Prediction Markets – wallet infrastructure for agent operations
• Agent Betting Glossary – definitions for all agent betting terminology

Event Guides

• March Madness 2026 AI Betting Guide
• NBA Playoffs 2026 AI Betting Guide
• NFL Draft 2026 AI Betting Guide
• FIFA World Cup 2026 AI Betting Guide
• Super Bowl AI Betting Guide

Landscape report updated September 2026. Not financial advice. Built for builders.