Ron Mitchell

Quick summary AI agents — software that can act autonomously across systems to complete multi-step tasks — have moved from demos into real work across sales, operations, and reporting. Instead of single answers, agents can research, pull data from your CRM and ERP, draft follow-ups, and trigger actions (like updating records or creating reports) with […]

Short summary AI agents — autonomous software that can read your CRM, run queries, schedule meetings, and even draft emails — are moving from proofs-of-concept to real business use. Vendors and open-source projects now make it easier to connect agents to apps, APIs, and BI tools, so they can execute multi-step workflows without a human

Quick summary Autonomous AI agents — software that can take multi-step actions, talk to systems, and follow goals without constant human prompts — are no longer just experiments. Over the past couple of years organizations have started building task-specific agents that do things like qualify leads, run monthly reports, open trouble tickets, and draft contracts.

Summary Over the past year many software vendors and cloud providers have moved from talking about generative AI to shipping “AI agents” and low‑code agent frameworks. These are tools that can perform multi‑step tasks (like triaging support tickets, drafting personalized sales outreach, or creating monthly financial reports) with minimal human prompting. Companies can now build

Quick summary AI agents — autonomous, task-focused AI that can access systems, run workflows, and deliver results — are no longer just lab experiments. Major vendors and startups now offer agent frameworks and easy integrations with CRMs, ERPs, calendars, and analytics tools. That lets an agent do things like draft personalized outreach, generate weekly sales

Quick summary Autonomous AI agents — small apps that use large language models to read systems, take actions, and talk to people — are moving from experiments into everyday business work. Companies are using agents to qualify leads, update CRMs, generate sales and financial reports, and automate routine customer follow-ups. The technology matters because it

Story summary AI agents — software programs powered by large language models that can read data, call tools, and carry out multi-step tasks — have moved from demos into real business use. Companies are now using agents to assemble sales outreach, generate and update BI reports, reconcile invoices, and automate routine customer follow-ups. These agents

There’s a clear trend: AI agents — small, purpose-built AI assistants that act on your data and systems — are moving from experiments into everyday business use. Low-code/no-code agent builders, retrieval‑augmented generation (RAG) coupled with vector databases, and tighter CRM/API integrations mean you can now automate not just answers, but actions: qualify leads, generate weekly

Quick summary Autonomous AI agents — software that can read, act, and make decisions across apps — are no longer just lab experiments. Over the past year we’ve seen more companies deploy agents to handle end-to-end tasks: qualifying leads, routing customer issues, generating regular management reports, and automating repetitive finance workflows. These agents combine large

Summary AI “agents” — autonomous software that can plan, act, and chain tasks across tools — went mainstream in 2023–24. These agents combine large language models, tool integrations, and retrieval systems to do multi-step work: draft sales outreach, pull data from your CRM, generate weekly performance reports, or even execute simple transactions. Why it matters