Ron Mitchell

Story in a sentence A new wave of AI agents — tools that act autonomously to complete tasks, orchestrate workflows, and surface insights — is shifting from lab experiments into core business systems like CRMs, service desks, and reporting platforms. Why this matters for your business – Speed and scale: Agents can handle routine sales […]

Story summary In the past year we’ve seen AI agents move from demos into real business work. Low-code agent builders and richer connector libraries let these agents read your CRM, create reports, book meetings, and even run simple negotiations — with minimal developer time. Companies are no longer just experimenting; they’re starting pilots that handle

Summary Autonomous AI agents — software that can plan, act, and complete multi-step tasks with little human input — are no longer a novelty. Over the past year we’ve seen growing use of these agents for lead qualification, order routing, expense reconciliation, and automated reporting. Instead of one-off answers, agents can run workflows end-to-end: pull

Quick summary AI agents — autonomous or semi-autonomous AI “workers” that can read, act, and coordinate across apps — are moving from tech demos into real business use. Recent platform releases and low-code agent builders have made it easier for teams to create agents that do things like qualify leads, run outreach sequences, update CRMs,

Quick summary AI agents — persistent, task-focused AI assistants that can draft outreach, qualify leads, update CRMs, and generate reports — have moved from experiments to built-in features in major sales and CRM tools. Vendors like Salesforce, HubSpot and Microsoft, together with LLM platforms (OpenAI, Anthropic, etc.), make it easier for businesses to attach an

Quick summary AI “agents” — autonomous assistants that can act on behalf of users across apps and data sources — are no longer just lab demos. Over the past year we’ve seen these agents plugged into CRMs, calendars, inboxes, and reporting systems so they can run repeatable workflows: qualify leads, schedule demos, update records, generate

Quick summary AI agents — autonomous software that combines large language models with tools, data connectors, and business rules — are no longer just experiments. Companies are increasingly using agents to automate sales outreach, generate operational reports, triage customer requests, and streamline repetitive workflows. These agents connect to CRMs, databases, calendars, and analytics tools to

Quick summary AI agents — software that can autonomously complete multi-step tasks by combining language models, data access, and action plugins — have moved from labs into real business pilots. Major vendors and startups now offer agent platforms that can run sales sequences, generate and update reports, trigger workflows, and coordinate across apps without constant

Summary AI “agents” — autonomous workflows that combine language models, retrieval (RAG), and app integrations — are no longer just research demos. Over the past year the tools and libraries that power agents (think orchestration frameworks, vector databases, and plug‑ins for CRMs and calendars) have matured. That makes it practical for companies to automate routine

Quick summary AI agents — software that uses large language models to carry out multi-step tasks, call APIs, and make decisions — moved this year from demos into real business use. Advances in model capabilities (better reasoning, multimodal inputs) plus agent frameworks (LangChain, agent plugins, platform integrations) mean you can now build assistants that do