Ron Mitchell

Quick summary AI “agents” — autonomous assistants that can research, outreach, update systems, and generate reports — are moving from labs into everyday business tools. Instead of one-off scripts, platforms now let teams build or customize agents that run repeatable tasks: qualify leads, draft personalized emails, schedule meetings, extract insights from customer conversations, and produce […]

Story summary AI agents — software that can act on your behalf to complete multi-step tasks — are moving from labs into everyday business tools. Large vendors and startups now offer “private copilots” that connect to company systems (CRM, calendar, email, reporting tools) and perform workflows: qualify leads, draft outreach, update records, run recurring reports,

Short summary AI “agents” — software that can act on behalf of users across apps, data sources, and workflows — have crossed an important threshold. After a few years of experiments, more companies are moving agents into production to handle routine decisions: qualifying leads, drafting proposals, updating CRMs, and generating regular reports. These agents combine

Quick summary AI agents — autonomous workflows powered by large language models — are no longer a niche experiment. Over the past 18 months we’ve seen platforms and low-code builders (think Copilot-style tools, LangChain frameworks, and AutoGPT patterns) make it practical for businesses to create agents that: – pull and enrich CRM data, – draft

What’s happening AI has moved beyond chat. “AI agents” — autonomous workflows that combine large language models with your CRM, BI tools, and automation platforms — are now practical for everyday business tasks. Instead of a person copying data between systems or writing the same weekly report, an agent can qualify leads, update records, generate

What’s happening – Over the past year we’ve seen a rapid shift: AI agents—software that can act on your behalf, make decisions, and carry out multi‑step tasks—are moving from proof‑of‑concepts into real business workflows. – These agents can do things like: qualify leads, draft and A/B test outreach, summarize meetings, update CRMs, generate sales proposals,

Quick summary AI-powered agents — software that can take multi-step actions (research, write, update systems, schedule meetings) — have moved from proof-of-concept demos into everyday business use. Sales teams use agents to qualify leads and draft personalized outreach. Operations teams run agents to automate monthly reports and reconcile data across systems. Low-code agent builders and

Quick summary AI agents — autonomous software that can read email, query your CRM, run searches, and take actions — are no longer just lab experiments. Over the past year major platforms and dozens of startups have made it easier to build agents that connect to enterprise systems, run multi-step workflows, and produce live reports.

Summary AI agents — systems that can perform multi-step tasks on their own (think: research, draft emails, update CRM records, and produce reports) — have moved from experiments to everyday business tools. Vendors and open‑source frameworks have matured the tech, and companies are shipping integrations that connect agents to calendars, CRMs, data warehouses, and workflow

Quick summary Autonomous AI agents — small, purpose-built systems that chain together LLM prompts, data lookups, and task actions — are no longer a research novelty. Over the past year we’ve seen platforms and enterprise toolsets make these agents easier to deploy and safer to manage. That means teams can automate multi-step work like sales