ChainCatcher report: On August 5, 2026, BNB Chain officially launched the "Build the Era" open hackathon, inviting individual developers and teams to submit solutions for building the best AI Agent trading marketplace on BNB Smart Chain. Over 200,000 AI Agents registered on BNB Smart Chain based on the ERC-8004 standard currently account for approximately 60% of all such agents across 26 networks, yet there remains a lack of unified discovery, comparison, and invocation interfaces—this hackathon aims to bridge this discoverability gap. The total prize pool exceeds $40,000 USD, contributed jointly by BNB Chain and ecosystem partners TermiX, PancakeSwap, AltLayer, and Altana. The grand prize winner of the main BNB Chain track will receive an award equivalent to 30,000 USDT and the opportunity to be officially adopted as the trading marketplace for BNB Agent Studio, with independent product incubation and operation. TermiX offers an additional $10,000 USDT for its partner track; PancakeSwap provides 1,000 CAKE; AltLayer offers 8004scan Pro access and AltLLM credits; and Altana provides 50,000 XP. Submissions are open from August 5 to September 9, with judging taking place from September 9 to 23. The winners will be announced on November 5. BNB Chain advises developers to focus on building marketplaces around four types of agents: monitoring, grid trading, health factor management, and yield optimization, and recommends that the marketplace support cross-category coverage.
BNB Chain Launches Build the Era Hackathon to Build an AI Agent Trading Market
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Summary
BNB Chain launched the Build the Era hackathon on August 5, 2026, to develop an AI Agent trading market on BNB Smart Chain. Developers and teams can submit projects until September 9, with winners announced on November 5. The event offers a prize pool exceeding $40,000, including 30,000 USDT for the main track winner, whose project may be adopted by BNB Agent Studio. On-chain data will be used to evaluate submissions, and on-chain analysis will help assess the performance of AI trading agents.
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