Ron Mitchell

Quick story Over the last 12–18 months we’ve seen AI “agents” — autonomous assistants that can read data, run tools, and act on your behalf — move from tech demos into real business use. Big platform updates (function-calling, plugins, enterprise copilot products) plus mature frameworks (LangChain, LlamaIndex-style tools) have made it practical to build agents […]

Short summary Autonomous AI agents — tools that take multi-step actions on your behalf (qualify leads, update CRM records, compose outreach, generate reports) — have moved from demos into real business use. Over the past year companies have put agent-style automation into customer service, sales workflows, and operations to save time, reduce manual work, and

Quick story summary AI agents — autonomous, task-focused AI assistants — moved fast from research demos into real business tools in 2024. Major platform vendors rolled out agent frameworks and low-latency, multimodal models that can read documents, query systems, and take actions (update a CRM, send an email, generate a report). Early adopters are using

Quick summary AI agents — autonomous, task-focused workflows powered by large language models and automation tools — are no longer just proof-of-concept demos. Companies are embedding agents into CRMs, service desks, and BI stacks to do real work: qualify leads, draft personalized outreach, update opportunities, and generate real-time sales and ops reports. Why this matters

Quick summary Major vendors and forward-thinking companies are moving beyond single-purpose AI tools to deploy autonomous AI agents that can access systems, run workflows, and produce business-ready outputs. These agents use large language models plus connectors (APIs, CRMs, databases) to do things like qualify leads, assemble executive reports, update records, and coordinate cross-team tasks —

Big picture AI agents — autonomous, task-focused AI that can read systems, run workflows, and report back — have moved from demos to real business deployments. Over the last year we’ve seen major platforms and open-source tools make it much easier to build agents that automate routine work: triaging leads, generating and sending reports, updating

Quick summary AI agents — autonomous, task-focused AIs that can act across apps, email, and CRMs — have moved from demos into real business pilots. Instead of just answering questions, these agents can follow multi-step workflows: qualify leads, book meetings, update CRM records, generate personalized outreach, and build recurring reports. The result: less busywork for

Quick summary AI agents — autonomous, task-focused AI that can read your data, take actions, and talk to systems or people — are moving from proof-of-concept to real production use across industries. Companies are connecting agents to CRM, inventory, and BI systems so agents can qualify leads, automate order updates, produce recurring reports, and triage

Quick summary AI “agents” — software that can perform multi-step tasks (reach out to prospects, schedule meetings, update CRM records, generate reports) — have stopped being just lab experiments. Over the last year more vendors and enterprise apps have shipped agent-style features (think Copilot-like helpers in CRMs and workflow tools), and more companies are running

Quick summary AI agents—autonomous, task-focused AI that can act across apps and data—have moved from lab demos to real business pilots. Companies are using agents to run outreach, prep sales proposals, summarize meetings, and automate recurring reports. The result: faster workflows, fewer manual errors, and more time for high-value work. Why this matters for your