Ron Mitchell

Short summary (what’s happening) Autonomous AI agents — software that uses large language models plus company data to take end-to-end actions — are moving from proofs-of-concept into real business use. Organizations are using agents to automate tasks like customer follow-ups, purchase-order exceptions, report generation, and first-line IT support. The shift is driven by better LLMs, […]

Short summary Autonomous AI agents—AI programs that can read, decide, and act across apps—are moving from demos into real business use. Paired with Retrieval-Augmented Generation (RAG) and vector databases, these agents can fetch company facts, follow policies, and perform tasks like generating tailored reports, triaging customer issues, or automating parts of sales workflows with far

Autonomous AI agents — AI systems that can plan, act, and follow up on tasks with little human input — are moving from labs into real business use. Over the last year we’ve seen more tools and platforms that let companies deploy agents for tasks like customer triage, sales outreach, procurement follow‑ups, and automated reporting.

Major tech vendors and startups are pushing a new wave of autonomous AI agents — software that can plan, act, and complete multi-step tasks with minimal human hand-holding. From scheduling and customer triage to creating multi-source reports, these agents combine large language models, retrieval-augmented knowledge bases, and workflow automation to handle repeatable, knowledge-heavy work. Why

Short summary The EU’s landmark AI rules are moving from proposal to real-world enforcement. The law creates new compliance duties for AI systems judged “high-risk,” plus transparency requirements for many generative and automated tools. If your company develops, integrates, or sells AI services in Europe — or serves EU customers — you will need to

Quick summary A major trend right now is companies pairing private LLMs with Retrieval‑Augmented Generation (RAG) and vector databases to build secure, accurate, and current AI assistants. Instead of relying only on a general model’s memory, RAG pulls the latest, company‑owned documents (policy, CRM, product specs, reports) into the model’s context. That reduces hallucinations, improves

Quick summary AI “agents” — autonomous or semi-autonomous workflows built on large language models — moved from demos into real business pilots in 2024. Major cloud vendors and startups rolled out tools to design, monitor, and connect agents to apps like CRMs, ERPs, and ticketing systems. The result: businesses can now automate multi-step work (e.g.,

Short summary Autonomous AI agents — small, LLM-driven programs that can read, decide, and act across systems — are moving from labs into real business workflows. Vendors have released agent platforms and low-code tools that let these agents connect to CRMs, ERPs, email, and databases. Companies are using them to draft responses, route requests, extract

Quick summary Generative AI and autonomous AI agents are being combined with robotic process automation (RPA) to create “intelligent process automation.” Instead of simple scripted bots, modern automation can understand documents, decide next steps, interact with systems through natural language, and learn from feedback. Major RPA and cloud vendors are already embedding large language models

Trending now: businesses are accelerating adoption of autonomous AI agents — software that can plan, act, and interact on behalf of users. Driven by improvements in large language models, retrieval-augmented generation (RAG), and agent frameworks (e.g., LangChain-style agents and vendor copilots), companies are piloting agents for customer support, procurement, accounts reconciliation, sales outreach, and internal