[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["缺少我需要的資訊","missingTheInformationINeed","thumb-down"],["過於複雜/步驟過多","tooComplicatedTooManySteps","thumb-down"],["過時","outOfDate","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["示例/程式碼問題","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],[],[],[],null,["[](/showcase) \nShare\nOCT 30, 2024 \n\nBringing AI Agents to production with Gemini API \nVishal Dharmadhikari\n\nProduct Solutions Engineer \nPaige Bailey\n\nAI Developer Experience Engineer \nAdam Silverman\n\nCOO, Agency AI \n\nBuilding and deploying AI agents is an exciting frontier, but managing these complex systems in a production environment requires robust observability. [AgentOps](https://www.agentops.ai/), a Python SDK for agent monitoring, LLM cost tracking, benchmarking, and more, empowers developers to take their agents from prototype to production, especially when paired with the power and cost-effectiveness of the [Gemini API](https://ai.google.dev/gemini-api/docs).\n\nThe Gemini advantage \n\nAdam Silverman, COO of [Agency AI](https://www.agen.cy/), the team behind AgentOps, explains that cost is a critical factor for enterprises deploying AI agents at scale. \"We've seen enterprises spend $80,000 per month on LLM calls. With Gemini 1.5, this would have been a few thousand dollars for the same output.\"\n\n\n\u003cbr /\u003e\n\n\nThis cost-effectiveness, combined with Gemini's powerful language understanding and generation capabilities, makes it an ideal choice for developers building sophisticated AI agents. \"Gemini 1.5 Flash is giving us comparable quality to larger models, at a fraction of the cost while being incredibly fast,\" says Silverman. This allows developers to focus on building complex, multi-step agent workflows without worrying about runaway costs.\n\u003e \"We have seen individual agent runs with other LLM providers cost $500+ per run. These same runs with Gemini (1.5 Flash-8B) cost under $50.\"\n\n--- Adam Silverman, COO, Agency AI\n\nPowering AI Agents \n\nAgentOps captures data on every agent interaction, not just LLM calls, providing a comprehensive view of how multi-agent systems operate. This granular level of detail is essential for engineering and compliance teams, offering crucial insights for debugging, optimization, and audit trails.\n\n\n\u003cbr /\u003e\n\n\nIntegrating Gemini models with AgentOps is remarkably simple, often taking just minutes using LiteLLM. Developers can quickly gain visibility into their Gemini APIcalls, track costs in real-time, and ensure the reliability of their agents in production.\n\nLooking ahead \n\nAgentOps is committed to supporting agent developers as they scale their projects. Agency AI is helping enterprises navigate the complexities of building affordable, scalable agents, further solidifying the value proposition of combining AgentOps with the Gemini API. As Silverman emphasizes, \"It is ushering more price-conscious developers to build agents.\"\n\n\n\u003cbr /\u003e\n\n\nFor developers considering using Gemini, Silverman's advice is clear: \"Give it a try, and you will be impressed.\" \n\nRelated case studies \n[Sourcegraph\nLearn how Cody AI saw big quality gains using Gemini's massive context window.](/showcase/sourcegraph) [Sublayer\nSee how the Ruby-based AI agent framework empowers developer teams to be more productive with the power of Gemini models.](/showcase/sublayer) [Viggle\nExperimenting with Gemini 2.0 to create virtual characters and audio narration for their AI powered video platform](/showcase/viggle)"]]
