Seeing the SERA Crypto technology report released by @ SentientAGI, I feel that open source architecture is redefining the performance boundaries of AI agents. Defeated Perplexity Finance and Gemini in the DMind benchmark, falling behind Claude 4.5 and GPT-5 by less than 3%. Ranked first in internal encryption analysis benchmarking, surpassing all existing AI systems - GPT-5 Medium Reasoning, Grok 4, and Perplexity Finance. ————————————————————————— The complex inference loop of traditional ReAct systems leads to high latency, lack of consistency in tool calls, and inability to parallelize multiple API calls. For example, when a user asks about the degree of exposure of Lido to the risk of stETH de anchoring in the next six months. The system needs to coordinate over 50 endpoints, including TVL trackers, staking APIs, on chain traffic providers, and derivative data. SERA performs routing inference through embedded matching. ————————————————————————— The SERA architecture rephrases and embeds each input query, and then compares it with two independent embedding indexes: the tool index and the hint template index The tool index contains descriptions of over 50 endpoints, including market data APIs, TVL trackers, on chain traffic providers, derivative data, social sentiment APIs, and more. The prompt index contains brief descriptions of 11 encrypted query categories and their associated templates. By transferring routing and prompt generation to the embedding layer, SERA avoids ReAct loops and selects the same tool consistently for different query categories, executing multiple tool calls in parallel with an average latency of less than 45 seconds. ————————————————————————— The fully open-source technology stack proves the advantage of architecture over computing power When the @ SentientAGI open-source system outperforms closed source alternatives in terms of cost and latency, the form of AI agents will undergo a qualitative change.