🤖 Tool Use, Function Calling & Agentic Patterns (ReAct, Plan-and-Execute, Reflection) — the layer that transforms LLMs from intelligent chatbots into reliable, real-world actors. Just read this excellent technical white paper from @aasaitech on bridging models to enterprise systems, IoT, MES, ERP, and industrial workflows. Key highlights: • 5-step tool loop with reflection: Reason → Call Tool → Observe → Act/Reflect → Iterate • Core patterns: ReAct (dynamic reasoning acting), Plan-and-Execute (structured multi-step), Self-Reflection for robustness • Industrial applications: Maintenance scheduling, anomaly investigation, spare parts ordering, compliance workflows, safety checks • Reference architecture frameworks (LangGraph, AutoGen, LlamaIndex, OpenAI function calling) This completes the shift to production-grade agentic AI for edge orchestration and manufacturing — combining perfectly with prior topics like RAG, CoT, quantization, and alignment. Full white paper infographic: x.com/aasaitech/status/20656… How are you building agentic workflows in your systems — simple ReAct, full Plan-and-Execute with human-in-the-loop, or custom LangGraph setups? #AgenticAI #ToolUse #ReAct #FunctionCalling #IndustrialAI #LangGraph #EdgeAI

Agent Tool Design - The 5 Numbers That Decide Whether Your Agent Picks the Right Tool Your agent picks the wrong tool because you handed it the whole catalog, not because the model is dumb. That reframe holds up in the data. Anthropic evals show one agent's task accuracy rising from 49% to 74% just by switching from full-catalog injection to dynamic loading of the 3-5 tools a step needs (MarkTechPost, 2026-05-29). IBM's LongFuncEval pushes harder: accuracy degrades by 7-85 percentage points as the catalog grows, and by up to 91 points as tool responses get longer, even when the right tool is present (arXiv:2505.10570). Selection is a context problem, not an IQ problem. The 5 numbers I would design around: 1. Cap active tools at 3-5 via dynamic loading. That alone cut tool-definition tokens roughly 85% (around 22,000 down to on-demand) and drove the 49% to 74% jump. 2. Write descriptions as task-intent contracts, not API docs. The same tool with an edited description got used 10x more on GPT-4.1 and Qwen2.5-7B, and the trend generalizes across a broader 17-model set (arXiv:2505.18135). The agent reads the words, not your code. 3. Add a use-your-own-knowledge gate. Agents invoke a tool over 30% of the time when parametric knowledge would have answered it (SMART, arXiv:2502.11435). 4. Compress schemas. Conservative compression bought 44-50% schema-token savings and a 20.5 pp exact-match lift at an 8K budget, measured over 6,566 calls across 14 models (arXiv:2605.26165). 5. Reserve parallel calls for provably independent subtasks: 62.2% with GPT-5-Medium plus parallel calls beat the 54.9% reported for GPT-5-High on BrowseComp, but only when branches do not depend on each other (arXiv:2602.07359). The model is fine. The tool layer is the prompt you forgot to write. Follow for daily insights - where blockchain meets AI, one satisfying swipe at a time. #AIAgents #AgenticAI #LLM #ToolUse #AIEngineering #MachineLearning #PromptEngineering #LLMOps #AIDevelopers #GPT5 #ClaudeAI #FunctionCalling #AItools #DeveloperTools #AIResearch

Function calling is the most useful concept in AI agents. It's how the model goes from "talking" to "doing". By the end of this thread: a working tool-using agent. 8 lines of Python. No framework bigger than the agent. 🧵 #AIAgents #FunctionCalling #Python

Writing better prompts isn't enough anymore. The biggest AI breakthroughs now come from giving models access to tools, APIs, and real actions through function calling. That's what powers many of today's AI agents. 🔗 sitepoint.com/why-function-c… #AI #LLM #FunctionCalling #WebDev

A model without tools is indeed like a calculator with no buttons. Function calling is the foundation of agent orchestration. The SDK patterns here are highly worth exploring. (ref: x.com/i/status/2062027361957…) #AIAgents #Python #FunctionCalling

A model with no tools is a calculator with no buttons. 8 lines of Python today and the model starts calling functions you wrote. OpenAI Agents SDK below. Same pattern works in Anthropic, Gemini, Ollama. Install run in 15 min. ↓ #AIAgents #FunctionCalling #Python

AIエージェントに躊躇してるのはサービス提供元の手のひら返しが怖いから。 しかし、１年前のChatGPTのFunctionCallingを見てると阿呆なローカルLLМはお呼びじゃない。（たぶん、MCP呼び出しに失敗する） どう考えてもRAG（DB）のフロントエンドが精々では？

個人的な感覚だと日本語でないとFunctionCallingで呼ばれないことが多い

🚀 مسابقة عالمية في الذكاء الاصطناعي العربي! شارك الآن في AISA-ArabicFC ضمن ArabicNLP 2026 المتزامنة مع المؤتمر العالمي EMNLP 2026 في بودابست 🇭🇺 📍 3 مسارات: • A: الاستدعاء الأساسي • B: استدعاء توليد استدلال • C: التحليل التشخيصي عبر اللهجات 🎯 مخصصة لـ: الباحثين • طلبة الدراسات العليا • مطوري النماذج • الفرق البحثية 🗓️ الجدول: 1 يونيو → بيانات التدريب 20 يوليو → بيانات الاختبار آخر تسجيل 30 يوليو → إعلان النتائج • فرصة لنشر ورقتك وعرضها في المؤتمر! 🔥 شرف بحثي visibility عالمي تطوير قدرات نماذجك العربية 🔗 سجّل واطلع على التفاصيل كاملة: huggingface.co/spaces/Tuwaiq… #فرصتك_هنا #AISA_ArabicFC #ArabicNLP2026 #EMNLP2026 #ArabicAI #AgenticAI #FunctionCalling #NLP #ذكاء_اصطناعي_عربي

🧠 The AI is no longer acting as a simple chatbot. It now acts as: • an execution coordinator • transaction orchestrator • validation layer • confirmation layer • workflow assistant #AgenticAI #Ethereum #AIEngineering #UXDesign #FunctionCalling #DeFi #SmartContracts

お勉強ここまで。 ・RAGはLLMを外部接続させるアプリ ・FunctionCallingはLLMの外部接続を許可する機能 ・MCPはFunctionCallingの拡張・標準化プロトコル MCPに関しては学ぶなら別途で書籍もしくはドキュメントを読む必要あり。

💡 LLM이 스스로 API와 DB를 호출한다고? Function Calling, 원리를 알면 AI 자동화의 판이 달라집니다. 보안 설정 없이 쓰면 위험한 이유도 함께 확인하세요. #비젠소프트 #FunctionCalling #LLM #AI자동화 #AI개발 #ToolUse

RealtimeのFunctionCallingで、アバターの表情をリアルタイム制御させるというのについ最近対応しました。性能的にはやはりRealtime推しですね〜

Realtime系でもFunctionCallingとかMCPとか使える感じなのでしょうか？

The best AI models for function calling and tool use in 2026 - comparing Claude, GPT-5.4, Gemini, DeepSeek, and local models on BFCL and TAU-bench scores. #AiAgents #FunctionCalling Link in the first comment 👇

💡 AI가 스스로 API를 호출하고 DB까지 조회한다면? Function Calling, LLM이 단순 대화를 넘어 실제 도구를 쓰는 방법을 파헤쳤습니다. #비젠소프트 #FunctionCalling #AI자동화 #LLMAPI #ToolUse #AI개발

Function Calling يركز على استخدام الاداة اما MCP فيركز على توحيد طريقة ربط وتقديم الادوات للـ AI Systems MCP يعتبر خطوة مهمة نحو بناء AI Systems اكثر مرونة وقابلية للتوسع من خلال توحيد طريقة ربط الادوات والبيانات الخارجية #MCP #FunctionCalling #LLM #AIAgents

AIキャラ、バックグランドエージェント部分の実装が今日からテスト。そのご、自律化。LLMを同時に何度も呼び出すし、FunctionCallingも使うので、RaspiのようなローエンドCPUだけで動かすのは難しいかもなあ。せめてgemma4-E4Bが動けばいいんだけど。E2Bだと心もとない。

Needle: Gemini 3.1을 증류해 만든 26M 파라미터 함수 호출 전용 온디바이스 모델 (feat. Cactus) (by 9bow님) d.ptln.kr/10249 #ondeviceai #functioncalling #smalllanguagemodel #simpleattentionnetwork #cactuscompute #needle #distillation

Needle distills tool-calling to a 26M model for on-device use: no FFN, pure attention, fast testable on phones and wearables. Retrieval JSON, not heavy reasoning. MIT licensed, test it now. #AI #ML #OnDevice #EdgeAI #FunctionCalling