Ron Mitchell

The story (short) Autonomous AI agents — small, goal-focused programs that combine large language models with automated actions — are no longer just experiments. Companies are using agents to do real work: qualify sales leads, update CRMs after calls, generate weekly pipeline reports, and trigger follow-up sequences. The shift is that agents can now connect […]

Quick summary – What’s happening: In the last couple of years AI “agents” — models that can take actions, use tools, and work across apps — have moved out of research demos and into real business workflows. Teams are using them for things like prospect research, automated outreach, meeting summaries, pipeline updates, and AI-powered reporting.

Quick summary AI agents — autonomous, task-focused AI that can read your systems, act on instructions, and report back — are moving from demos to real business use. Recent waves of product launches and enterprise pilots show agents handling lead outreach, booking meetings, generating tailored sales playbooks, and running recurring operational reports without constant human

Quick summary AI “agents” — autonomous or semi-autonomous software that completes tasks end-to-end — are no longer just lab demos. Over the past year, major cloud and CRM vendors and a growing set of startups have released low-code agent builders and prebuilt agents that can triage leads, personalize outreach, schedule meetings, update CRMs, and generate

Quick summary AI agents — software that can plan, act, and chain tasks using LLMs and connectors — are moving out of demos and into real business workflows. Over the last year we’ve seen major vendors and startups integrate agent-style assistants into CRMs, help desks, and reporting tools. That means your company can now automate

Quick summary AI “agents” — autonomous tools that carry out multi-step tasks (think: qualify a lead, book meetings, update CRM, and draft follow-up emails) — have moved from demos to real deployments. Major cloud providers and startups now offer agent-builder frameworks that connect language models to business systems, workflows, and APIs. That makes it easier

The story (short) AI “agents” — autonomous workflows built on large language models and connectors to business systems — have moved from experiments into real, revenue-impacting use. Instead of a person copying data between tools or drafting routine emails, companies are piloting agents that qualify leads, update CRMs, generate proposals, and assemble monthly reports automatically.

Quick summary AI agents — software that can act autonomously to complete tasks (for example, qualify leads, run reports, or trigger order approvals) — have moved beyond experiments. In 2025 we saw major cloud and AI platform providers add agent-building tools and low-code connectors, and more teams put agents into live workflows. That shift is

Story summary In recent months we’ve seen a big jump in practical AI agents — tools that don’t just answer questions but connect to your apps (CRM, calendar, Slack), pull company data, and take actions: qualify leads, draft outreach, update records, schedule meetings, and generate reports. These agents combine retrieval (your company data), large models

Quick summary AI agents — small, goal-directed systems that can read data, take actions, and follow up — moved this year from lab demos to real-world use. Cloud providers and toolmakers rolled out agent frameworks and pre-built connectors for CRMs, calendars, inboxes, and databases. That means you no longer need a developer team to start