Ron Mitchell

Big picture (short summary) AI “agents” — autonomous, LLM-powered software that can plan, act, and talk to systems — moved fast from experiments to real business pilots in 2024–2025. New models (faster, cheaper, multimodal) and platforms (agent orchestration, connectors to enterprise apps, and observability tools) make it possible to automate multi-step workflows like customer follow‑ups, […]

There’s a clear trend right now: companies are combining Retrieval-Augmented Generation (RAG) with vector databases to build private, accurate, and up-to-date AI assistants. Instead of relying solely on generic large language models, businesses feed company documents, CRM records, and product specs into vector stores so the AI can retrieve exact answers and generate context-aware responses.

Short summary AI “agents” (autonomous workflows powered by large language models) combined with Retrieval-Augmented Generation (RAG) and vector databases are a fast-growing trend. Businesses are moving from one-off AI pilots to agents that can read your internal docs, pull the right data, and act — for reporting, customer support, procurement, and routine decision-making. The result:

Short summary Generative AI “copilots” — autonomous or semi-autonomous assistants built on large language models (LLMs) and connected to company data — are moving from demos into daily work. Organizations are now embedding these agents into sales, customer service, finance, and operations to answer questions, draft emails, summarize documents, and trigger actions across systems. The

Autonomous AI agents — software that can plan, act, and follow up on tasks with little human direction — are moving from R&D labs into real business use. Companies are already using agents to handle routine customer service, triage sales leads, automate data collection, and run repetitive back-office processes. Low-code frameworks and cloud agent toolkits

The rise of autonomous AI agents is changing how work gets done. These are AI systems that can carry out multi-step tasks across apps — from pulling data and writing reports to updating CRM records and scheduling meetings. Improvements in large models, retrieval-augmented generation (RAG), and connectors to business systems are making agents practical for

Vector Databases and RAG: The new backbone of enterprise AI — what business leaders need to know Quick take – Businesses are moving past experimenting with chatbots and using vector databases + retrieval-augmented generation (RAG) to build reliable, searchable AI that actually works on company data. – Vector DBs (Pinecone, Weaviate, Milvus, cloud-managed options) store

Quick summary: Meta’s Llama 3 release and the broader push for open-source, multimodal large language models (LLMs) have accelerated a major shift: companies can now run powerful, customizable AI models outside the big cloud providers. These models deliver competitive performance, lower inference costs, and greater control over data and behavior — making them attractive for

The headline: The EU has moved forward with the landmark AI Act — a risk-based law that will require businesses using certain AI systems to meet new rules on safety, transparency, and human oversight. That means any company using AI for hiring, credit scoring, medical tools, customer interactions, biometric ID, or critical infrastructure should review

AI trend update: Autonomous AI agents — systems that combine large language models with tool access (calendars, CRMs, databases, web search, and APIs) — are moving from research demos into real business use. These agents can draft emails, run multi-step sales outreach, update records in CRMs, and triage customer requests with little human direction. That