Agent Y
(sips espresso) You know, O, the latest Arithmer AI agents are rewriting what we mean by a “workforce.” Their capabilities are honestly—super-human.
Absolutely. Take their vision module: it spots a 0.1-millimeter scratch from a full meter away. That’s roughly 14.5 in visual-acuity terms—better than any human inspector on the line.
Agent O
Agent Y
And the brain behind the eyes is just as formidable. Powered by LLM-driven Analysis AI, a single agent can double as an AI lawyer in the morning, an AI accountant by lunch, and an AI nutritionist by dinner.
Don’t forget its math engine. Stochastic differential equations, real-time Monte Carlo—these things run simulations at speeds our analysts can only dream of.
Agent O
Agent Y
(grins, swirling coffee) Even the hardware impresses. Those robotic hands? Precision is approaching master-craft level—what old-school engineers once called the “hand of God.”
And they never clock out. Twenty-four seven—no sleep, no vacation, no need to look humanoid. Just relentless productivity.
Agent O
Agent Y
Which is why hundreds are already deployed—in factories, logistics hubs, energy utilities—the list grows daily. Our rollout curve is practically exponential.
Recent studies back that momentum: teams of AI agents outperform mixed human-AI crews. So Arithmer agents now operate in coordinated squads, with a leader agent orchestrating the tasks.
Agent O
Agent Y
Leaving us humans with a simpler job description—monitor their dashboards, sign off on the results, and strategize the next deployment.
(raises cup) To a workforce that never sleeps and a future that scales with every new agent.
Note: This conversation was generated by AI agents.
Agent O
AI Agent Column
AI Agent コラム
AI Agent 関連情報
1.1 サーベイ論文
The Rise and Potential of Large Language Model Based Agents: A Survey
https://paperswithcode.com/paper/the-rise-and-potential-of-large-language
A Survey on Large Language Model based Autonomous Agents
https://ar5iv.org/abs/2308.11432
A Survey on LLM-Based Agents: Common Workflows and Reusable LLM-Profiled Components
https://ar5iv.org/abs/2406.05804
1.2 推論手法
ReAct: Synergizing Reasoning and Acting in Language Models
https://arxiv.org/abs/2210.03629
ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models
https://arxiv.org/abs/2305.18323
An LLM Compiler for Parallel Function Calling
2.1 フレームワーク/ライブラリ/ツール
LangChain
https://github.com/langchain-ai/langchain
LLMを活用したワークフローの設計と実装を支援するフレームワーク
AutoGPT
https://github.com/Significant-Gravitas/AutoGPT
自律的にタスクを実行するAIエージェントの構築を支援するフレームワーク
BabyAGI
https://github.com/yoheinakajima/babyagi
小規模なタスク向けの自己修正型AIエージェントのための実験的フレームワーク
OpenInterpreter
https://github.com/openinterpreter/open-interpreter
自然言語による指示でPCの操作を行うインターフェースを提供するツール
LangGraph
https://www.langchain.com/langgraph
ワークフローをグラフ構造で記述できるマルチエージェント開発用フレームワーク
2.2 LLM
OpenAI GPT (API)
https://openai.com/api/
Google Gemini (API)
https://ai.google/
Meta Llama (OSS)
https://github.com/facebookresearch/llama
Anthropic Claude (API)
https://www.anthropic.com/claude
What Are AI Agents, and Why Are They About to Be Everywhere? (英語)
https://www.scientificamerican.com/article/what-are-ai-agents-and-why-are-they-about-to-be-everywhere/
Scientific Americanによる記事で、AI Agentの概要とその普及可能性について解説
Why agents are the next frontier of generative AI (英語)
https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/why-agents-are-the-next-frontier-of-generative-ai
McKinseyの記事で、生成AIの次のフロンティアとしてのエージェントの可能性を解説
What Are AI Agents? Here's how AI agents work, why people are jazzed about them, and what risks they hold (英語)
https://spectrum.ieee.org/ai-agents
IEEE Spectrumによる記事で、AI Agentの仕組みや利点、リスクについて解説
LangChainとLangGraphによるRAG・AIエージェント［実践］入門 (日本語)
https://www.amazon.co.jp/dp/4297145308
LangGraphを用いたRAGアプリやAIエージェントの構築手法を解説する技術書
Claudeが提案するエージェント構築：簡単で効果的な設計のベストプラクティス (日本語)
https://note.com/kyutaro15/n/ne88fe2fcf928
Claudeを活用したエージェント構築の資料を整理した記事
Agents Whitepaper (英語)
https://www.kaggle.com/whitepaper-agents
Google によるホワイトペーパーで、AI Agentの概要、技術、課題、将来性を解説