Ron Mitchell

Short summary AI agents — autonomous, LLM-powered helpers that connect to your apps (CRM, calendar, email, docs and reporting tools) and carry out multi-step tasks — are moving from demos into real business use. Companies are shipping agent frameworks and pre-built connectors that let agents do things like qualify leads, fill out CRM records, generate […]

Quick summary AI agents — autonomous, task-focused AI that can read, act, and respond across apps — have moved from experiments into real business use. Companies are using agents to qualify leads, draft and send follow-ups, generate sales reports, and automate routine customer tasks. The result: faster workflows, fewer manual handoffs, and clearer insights from

Summary In the past year we’ve seen a clear shift: AI “agents” — systems that combine large language models, retrieval-augmented knowledge, and workflow orchestration — are moving from experiments into production. Instead of single-prompt assistants, these agents can run multi-step processes, pull live data, update systems (CRMs, ERPs), and hand off to humans when needed.

Short summary Over the past 18 months, major AI platforms and open-source frameworks have made it dramatically easier for businesses to build and deploy autonomous AI agents — small, purpose-built programs that can take actions, talk to systems, and complete tasks on behalf of teams. These agents are showing up in sales, customer success, finance,

Short summary AI “agents” — models that act on your behalf across apps and data — have moved from demos to practical business tools. Advances in models, retrieval (vector stores/RAG), and low-latency APIs let agents handle recurring tasks like lead qualification, CRM updates, meeting follow-ups, and automated reporting. That means less manual work, faster response

Summary AI “agents” — software that can act autonomously (research, decide, and take actions across tools) — are no longer just experiments. Over the past year we’ve seen a wave of agent platforms and pre-built integrations that let companies automate real end-to-end work: qualifying leads, creating draft proposals, updating CRMs, and producing regular performance reports.

Big picture: Over the last year we’ve seen a clear shift — AI agents (automated, multi-step AI workflows) are no longer just demos. More businesses are using them for sales outreach, repeatable ops tasks, and automated reporting. These agents can read systems, call APIs, and take actions — so they can draft emails, update CRMs,

Quick summary Generative AI is moving from chatty prototypes to autonomous agents that act inside your systems — scheduling, following up on sales leads, updating CRM records and producing polished, repeatable reports from raw data. At the same time, generative reporting tools turn queries into visual dashboards and narrative summaries that non-technical leaders can use

Short summary Autonomous AI agents — small, goal-focused AI programs that can act across apps — are moving from labs into everyday business use. Over the last year vendors and startups have shipped agent frameworks and integrations that let these agents read your CRM, send emails, update spreadsheets, generate reports, and trigger automation flows without

Summary AI “agents” — autonomous software that can act on your behalf (draft emails, update CRMs, generate reports, run follow-ups) — are no longer just tech demos. Over the last 18–24 months companies of all sizes have moved from one-off pilots to practical deployments that sit on real business data and integrate with core systems.