Ron Mitchell

Quick summary – This year major AI vendors pushed “agents” — AI that can act autonomously across apps: read CRM records, pull finance data, generate reports, send messages, and trigger workflows. – In plain terms: instead of asking an AI a question and copying results, businesses can give an agent a job (qualify leads, build […]

Why this story matters right now AI agents — autonomous, goal-oriented software that uses large language models plus tool integrations — are moving from experiments into day-to-day business use. Instead of one-off prompts, these agents can read your CRM, schedule outreach, generate and update reports, and surface anomalies without constant human instruction. That shift makes

Quick summary AI “agents” — task-focused AI that can act across apps (email, CRM, calendar, dashboards) — moved from labs into real business use this year. Vendors and startups made agent builders easier to deploy, and more teams are using agents to do things like qualify leads, schedule meetings, generate sales follow-ups, and create automated

Quick summary AI agents — autonomous assistants that act on your data and systems — have moved from lab demos to real business pilots. Modern agent frameworks and cloud offerings now let companies build agents that can qualify leads, create and update CRM records, run financial or sales reports, and even trigger workflows across apps

Quick summary AI agents — autonomous systems that combine generative models with connectors and business rules — are moving out of limited experiments and into real company workflows. Major vendors have rolled out copilots and tool-enabled models, and open frameworks make it easier for teams to build agents that research, triage, schedule, update CRMs, and

Quick summary AI “agents” — autonomous or semi-autonomous AI tools that can read, act, and orchestrate across apps — have moved from experiments to real business tools. In the past year we’ve seen major platforms and startups add agent frameworks, low-code builders, and prebuilt integrations that make it practical to automate workflows that used to

Quick summary AI agents — small, goal-oriented AI systems that can act across apps and data — are moving from prototypes into real business use. Advances in retrieval-augmented generation (RAG), secure connectors to CRMs and cloud storage, and better orchestration frameworks mean these agents can now complete multi-step tasks: qualify leads, update records, compile reports,

What’s happening Major cloud vendors and startups are rolling out no-code / low-code tools that let companies build AI agents — software that can read your systems, take actions, and carry out multi-step tasks (think: generate a sales forecast, pull CRM data, and email a follow-up). These agents combine large language models, retrieval systems, and

Story summary AI agents — autonomous tools that can speak, act on apps, and fetch answers from your data — are no longer just a curiosity. Over the last 12–18 months we’ve seen more companies move from pilot projects to production use of AI agents for tasks like lead qualification, meeting scheduling, automated reporting, and

Quick summary AI “agents” are software that can carry out multi-step tasks for you — they read data, call apps (CRM, calendar, ticketing), and take actions with minimal human prompts. Over the past year we’ve seen faster, cheaper models, better connectors, and mature orchestration tools that make these agents reliable enough for real business use.