Ron Mitchell

Quick summary AI agents — autonomous software that can read, act, and learn across systems — are moving from experiments into real business use. Over the past year we’ve seen low-code agent platforms, better orchestration tools, and built-in connectors from major cloud providers that let companies deploy agents for tasks like lead follow-up, scheduling, document […]

Quick summary AI “agents” — autonomous programs that can act across apps, pull data, and complete multi-step tasks — moved from R&D demos into real business use in the past year. Companies are using agents to assemble reports, enrich sales leads, run follow-ups, and automate routine decisions. The tech is maturing fast: platforms now make

Quick summary AI agents — software that can act autonomously on your behalf (think: qualify leads, update CRMs, draft outreach, and build reports) — are no longer just lab demos. Major AI platforms now make it much easier to connect agents to enterprise data and business systems, and to build low‑code agents that follow rules

Summary Autonomous AI agents—software that combines large language models, retrieval (RAG) from your data, and APIs to act on tasks—are moving from proof-of-concept to real business use. Instead of just answering questions, these agents can triage leads, generate and send reports, book meetings, and trigger workflows across systems. The tech still needs guardrails, but it’s

Quick summary AI “agents” — systems that can carry out multi-step tasks, call tools, and act on your behalf — are no longer just proofs of concept. Between improved large language models, retrieval-augmented workflows, and low-code agent frameworks, businesses can now build agents that draft outreach, update CRMs, generate routine reports, and trigger follow-up actions

The story (short): Over the past year businesses have moved beyond experimenting with chatbots and prototypes. Companies are now deploying AI agents — models connected to your CRM, calendar, inventory, and reporting systems — to do frontline work: qualify leads, book demos, draft proposals, and generate weekly sales reports. These agents use retrieval-augmented generation (RAG)

Short summary AI “agents” — autonomous systems built on large language models that can call tools, fetch data, and act on behalf of users — are no longer just demos. With better model tool-use, richer APIs, and more ready-made connectors to CRMs, ERPs and BI tools, companies are starting to deploy agents for real work:

Quick summary The last year has seen a clear shift: autonomous AI agents — tools that can complete tasks, make decisions, and interact with systems — are moving from proofs-of-concept into real business use. Companies are using them to qualify leads, generate proposals, automate routine workflows, and produce near-real-time reports. The result: faster cycle times,

The story (short) AI “agents” — autonomous LLM-driven assistants that can act on your behalf across systems — have moved from research demos into practical business tools. Over the last 18 months we’ve seen mature agent frameworks, better connectors to CRMs and data warehouses, and more enterprise-grade guardrails. Companies are shipping agents for lead qualification,

What’s happening Autonomous AI agents—tools like AutoGPT, LangChain-based agents, and the agent features built into major platforms—have moved from demos to real pilots. These agents can research, pull data from multiple sources, run workflows, and produce reports or take actions with minimal human prompting. Businesses are testing them for tasks from lead qualification and CRM