Parlant

Q: "Come possono gli AI agent beneficiare la mia azienda?"

"Gli AI agent possono automatizzare task complessi e multi-step come analisi dati, elaborazione documenti e interazioni con i clienti. Per le PMI europee, implementare agent su infrastruttura privata con strumenti come ORCA garantisce che i dati aziendali sensibili non lascino mai il proprio perimetro, sfruttando al contempo capacità AI all'avanguardia."

Q: "È sicuro usare AI agent con i dati aziendali?"

"Dipende dall'implementazione. Gli agent cloud inviano i tuoi dati a server esterni, creando rischi GDPR. Gli AI agent privati che girano sulla tua infrastruttura — come quelli costruiti sullo stack PRISMA di HTX — mantengono tutti i dati sotto il tuo controllo. Questo è l'approccio più sicuro per le aziende che gestiscono informazioni sensibili."

Articoli Interessanti - This article is part of a series.

Part : Qwen

Part : Introducing Claude Opus 4.7 Anthropic

Part : GitHub - arman-bd/guppylm: A ~9M parameter LLM that talks like a small fish.

Part : Embarrassingly Simple Self-Distillation Improves Code Generation

Part : Show HN: 1-Bit Bonsai, the First Commercially Viable 1-Bit LLMs

Part : PrismML — Concentrating intelligence

Part : GitHub - microsoft/VibeVoice: Open-Source Frontier Voice AI

Part : GitHub - karpathy/autoresearch: AI agents running research on single-GPU nanochat training automatically

Part : GitHub - Pinperepette/snakebite: Detect malicious PyPI packages using heuristic analysis and LLM-powered filtering to uncover credent

Part : GitHub - 666ghj/MiroFish: A Simple and Universal Swarm Intelligence Engine, Predicting Anything. 简洁通用的群体智能引擎，预测万物

Part : Introducing Mistral Small 4 | Mistral AI

Part : GitHub - andrewyng/context-hub

Part : Coding my Handwriting — Amy Goodchild

Part : GitHub - jundot/omlx: LLM inference server with continuous batching & SSD caching for Apple Silicon — managed from the mac

Part : My chief of SEO, Claude Cowork

Part : Building AI Coding Agents for the Terminal: Scaffolding, Harness, Context Engineering, and Lessons Learned

Part : GitHub - NousResearch/hermes-agent: The agent that grows with you

Part : GitHub - bytedance/deer-flow: An open-source SuperAgent harness that researches, codes, and creates. With the help of sandboxes, m

Part : spent the entire day testing Qwopus (Claude 4

Part : GitHub - z-lab/paroquant: [ICLR 2026] ParoQuant: Pairwise Rotation Quantization for Efficient Reasoning LLM Inference

Part : I packaged up the 'autoresearch' project into a new self-contained minimal repo if people would like to play over the weekend

Part : building an autonomous company

Part : Agent Safehouse

Part : Introduction to LLM Post-Training Techniques | PDF

Part : Distillation Training : 4 Bits

Part : I've been thinking a bit about continual learning recently, especially as it relates to long-running agents (and running a few toy experiments with MLX)

Part : The Best OpenClaw Alternatives 2026 – from N… – Till Freitag

Part : Qwen3.5 Fine-tuning Guide | Unsloth Documentation

Part : microgpt

Part : GLM-5

Part : Step 3.5 Flash: Fast Enough to Think. Reliable Enough to Act.

Part : GitHub - Search code, repositories, users, issues, pull requests...: Altered state slash commands for Claude Code. 12 substance-themed personality modes that change how

Part : Keycloak

Part : GitHub - zai-org/GLM-OCR: GLM-OCR: Accurate × Fast × Comprehensive

Part : GitHub - EricLBuehler/mistral.rs: Fast, flexible LLM inference

Part : GitHub - antirez/voxtral.c: Pure C inference of Mistral Voxtral Realtime 4B speech to text model

Part : GitHub - alexziskind1/llama-throughput-lab: Interactive launcher and benchmarking harness for llama.cpp server throughput, with tests, sweeps, and round-robin load tools.

Part : GitHub - qwibitai/nanoclaw: A lightweight alternative to Clawdbot / OpenClaw that runs in Apple containers for security. Connect

Part : GitHub - moltbot/moltbot: Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞

Part : GitHub - aiming-lab/SimpleMem: SimpleMem: Efficient Lifelong Memory for LLM Agents

Part : GitHub - mikekelly/claude-sneakpeek: Get a parallel build of Claude code that unlocks feature-flagged capabilities like swarm mode.

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Part : GitHub - different-ai/openwork: An open-source alternative to Claude Cowork, powered by OpenCode

Part : GitHub - google/langextract: A Python library for extracting structured information from unstructured text using LLMs with precis

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Part : GitHub - bolt-foundry/gambit: Agent harness framework for building, running, and verifying LLM workflows

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Part : GitHub - mistralai/mistral-vibe: Minimal CLI coding agent by Mistral

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Part : Il Disclaimer muore.

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Part : Cowork: Claude Code for the rest of your work

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Part : Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting

Part : Recursive Language Models (RLMs)

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Part : Qwen-Image

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Part : The Anthropic Economic Index Anthropic

Part : dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model

Part : PaddleOCR

Part : DeepSite v2 - a Hugging Face Space by enzostvs

Part : How to Use Claude Code Subagents to Parallelize Development

Part : Show HN: CLAVIER-36 – A programming environment for generative music

Part : Small models are the future of agentic ai

Part : Kimi K2: Open Agentic Intelligence

Part : Introducing Qwen3-Max-Preview (Instruct)

Part : Scientific Paper Agent with LangGraph

Part : Anthropic's Interactive Prompt Engineering Tutorial

Part : RAGFlow

Part : swiss-ai/Apertus-70B-2509 · Hugging Face

Part : Making a font of my handwriting · Chameth.com

Part : SurfSense

Part : LoRAX: Multi-LoRA inference server that scales to 1000s of fine-tuned LLMs

Part : NextChat

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Part : Colette - ci ricorda molto Kotaemon

Part : Memvid

Part : VibeVoice: A Frontier Open-Source Text-to-Speech Model

Part : [2502.12110] A-MEM: Agentic Memory for LLM Agents

Part : [2504.19413] Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

Part : Apertus 70B: Truly Open - Swiss LLM by ETH, EPFL and CSCS

Part : HumanLayer

Part : PageIndex: Document Index for Reasoning-based RAG

Part : Deploying DeepSeek on 96 H100 GPUs

Part : Claude Code: A Highly Agentic Coding Assistant - DeepLearning.AI

Part : DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoning

Part : [2508.15126] aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists

Part : Alexander Kruel - Links for 2025-08-24

Part : DSPy

Part : AI Agents for Beginners - A Course

Part : Turning Claude Code into my best design partner

Part : How to build a coding agent

Part : Tiledesk Design Studio

Part : Build a Large Language Model (From Scratch)

Part : Data Formulator: Create Rich Visualizations with AI

Part : browser-use/web-ui

Part : Casper Capital - 100 AI Tools You Can’t Ignore in 2025...

Part : This Article

Part : CS294/194-196 Large Language Model Agents | CS 194/294-196 Large Language Model Agents

Part : Show HN: Whispering – Open-source, local-first dictation you can trust

Part : Fallinorg v1.0.0-beta

Part : dokieli

Part : paperetl

Part : Automatically annotate papers using LLMs

Part : My AI Had Already Fixed the Code Before I Saw It

Part : Llama-Scan: Convert PDFs to Text W Local LLMs

Part : Claudia – Desktop companion for Claude code

Part : Show HN: Fallinorg - Offline Mac app that organizes files by meaning

Part : Focalboard

Part : Elysia: Agentic Framework Powered by Decision Trees

Part : LangExtract

Part : MCP-Use

Part : +1 for "context engineering" over "prompt engineering"

Part : The race for LLM cognitive core

Part : [2507.07935] Working with AI: Measuring the Occupational Implications of Generative AI

Part : Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting

Part : Prava - Teaching GPT‑5 to use a computer

Part : InstaVM - Secure Code Execution Platform

Part : Sim

Part : Litestar is worth a look

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Part : Launch HN: Lucidic (YC W25) – Debug, test, and evaluate AI agents in production

Part : Introducing pay per crawl: Enabling content owners to charge AI crawlers for access

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Part : [2507.14447] Routine: A Structural Planning Framework for LLM Agent System in Enterprise

Part : Qwen3-Coder: Agentic coding in the world

Part : FutureHouse Platform

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Part : Research Agent with Gemini 2.5 Pro and LlamaIndex | Gemini API | Google AI for Developers

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Part : Opencode: AI coding agent, built for the terminal

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Part : Nice - my AI startup school talk is now up! Chapters: 0:00 Imo fair to say that software is changing quite fundamentally again

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Part : [2504.07139] Artificial Intelligence Index Report 2025

Part : Gemma 3 QAT Models: Bringing state-of-the-Art AI to consumer GPUs

Part : GitHub - HandsOnLLM/Hands-On-Large-Language-Models: Official code repo for the O'Reilly Book - 'Hands-On Large Language Models'

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Part : Pagina LLM Post-Training: A Deep Dive into Reasoning Large Language Models

Part : Pagina SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion

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#### Fonte

Tipo: GitHub Repository
Link originale: https://github.com/emcie-co/parlant
Data pubblicazione: 2025-09-04

Sintesi
#

WHAT - Parlant è una libreria per lo sviluppo di agenti LLM (Large Language Model) che garantisce il rispetto delle istruzioni e delle linee guida aziendali. È progettata per applicazioni reali e può essere implementata rapidamente.

WHY - È rilevante per il business AI perché risolve problemi comuni come l’ignoranza delle istruzioni, le risposte errate e la gestione delle eccezioni, migliorando la coerenza e l’affidabilità degli agenti AI in produzione.

WHO - Gli attori principali sono i developer di agenti AI e le aziende che necessitano di agenti AI affidabili e controllati. La community di sviluppatori e utenti di Parlant è attiva su Discord.

WHERE - Si posiziona nel mercato degli strumenti per lo sviluppo di agenti AI, offrendo una soluzione specifica per il controllo e la gestione del comportamento degli agenti LLM.

WHEN - È un progetto relativamente nuovo ma già operativo, con una rapida implementazione e una crescente adozione.

BUSINESS IMPACT:

Opportunità: Miglioramento della qualità e affidabilità degli agenti AI aziendali, riduzione dei costi di manutenzione e supporto.
Rischi: Competizione con altre soluzioni di gestione degli agenti AI, necessità di formazione del personale.
Integrazione: Facile integrazione con stack esistenti grazie alla modularità e alla documentazione dettagliata.

TECHNICAL SUMMARY:

Core technology stack: Python, asyncio, API integration.
Scalabilità: Alta scalabilità grazie all’uso di architetture asincrone e modulari.
Differenziatori tecnici: Gestione avanzata delle linee guida comportamentali, spiegabilità delle decisioni, integrazione con API esterne e servizi backend.

NOTE: Parlant è una libreria, non un corso o un articolo.

Casi d’uso
#

Private AI Stack: Integrazione in pipeline proprietarie
Client Solutions: Implementazione per progetti clienti
Development Acceleration: Riduzione time-to-market progetti
Strategic Intelligence: Input per roadmap tecnologica
Competitive Analysis: Monitoring ecosystem AI

Risorse
#

Link Originali
#

Parlant - Link originale

Articolo segnalato e selezionato dal team Human Technology eXcellence elaborato tramite intelligenza artificiale (in questo caso con LLM HTX-EU-Mistral3.1Small) il 2025-09-04 19:12 Fonte originale: https://github.com/emcie-co/parlant

Il Punto di Vista HTX
#

Questo tema è al centro di ciò che costruiamo in HTX. La tecnologia discussa qui — che si tratti di agenti AI, modelli linguistici o elaborazione documenti — rappresenta esattamente il tipo di capacità di cui le aziende europee hanno bisogno, ma implementata alle proprie condizioni.

La sfida non è se questa tecnologia funziona. Funziona. La sfida è implementarla senza inviare i dati aziendali a server USA, senza violare il GDPR e senza creare dipendenze da fornitori da cui non puoi uscire.

Per questo abbiamo costruito ORCA — un chatbot aziendale privato che porta queste capacità sulla tua infrastruttura. Stessa potenza di ChatGPT, ma i tuoi dati non escono mai dal tuo perimetro. Nessun costo per utente, nessuna fuga di dati, nessun problema di compliance.

Vuoi sapere quanto è pronta la tua azienda per l’AI? Fai il nostro Assessment gratuito della AI Readiness — 5 minuti, report personalizzato, roadmap operativa.

FAQ

Come possono gli AI agent beneficiare la mia azienda?

Gli AI agent possono automatizzare task complessi e multi-step come analisi dati, elaborazione documenti e interazioni con i clienti. Per le PMI europee, implementare agent su infrastruttura privata con strumenti come ORCA garantisce che i dati aziendali sensibili non lascino mai il proprio perimetro, sfruttando al contempo capacità AI all'avanguardia.

È sicuro usare AI agent con i dati aziendali?

Dipende dall'implementazione. Gli agent cloud inviano i tuoi dati a server esterni, creando rischi GDPR. Gli AI agent privati che girano sulla tua infrastruttura — come quelli costruiti sullo stack PRISMA di HTX — mantengono tutti i dati sotto il tuo controllo. Questo è l'approccio più sicuro per le aziende che gestiscono informazioni sensibili.

Articoli Interessanti - This article is part of a series.

Part : Qwen

Part : Introducing Claude Opus 4.7 Anthropic

Part : GitHub - arman-bd/guppylm: A ~9M parameter LLM that talks like a small fish.

Part : Embarrassingly Simple Self-Distillation Improves Code Generation

Part : Show HN: 1-Bit Bonsai, the First Commercially Viable 1-Bit LLMs

Part : PrismML — Concentrating intelligence

Part : GitHub - microsoft/VibeVoice: Open-Source Frontier Voice AI

Part : GitHub - karpathy/autoresearch: AI agents running research on single-GPU nanochat training automatically

Part : GitHub - Pinperepette/snakebite: Detect malicious PyPI packages using heuristic analysis and LLM-powered filtering to uncover credent

Part : GitHub - 666ghj/MiroFish: A Simple and Universal Swarm Intelligence Engine, Predicting Anything. 简洁通用的群体智能引擎，预测万物

Part : Introducing Mistral Small 4 | Mistral AI