Ron Mitchell

Quick summary Generative AI has moved beyond single-chat apps into task-focused “agents” that can act across systems — read your CRM, pull data from reporting pipelines, draft outreach, and even trigger workflows. Big vendors and startups are building these agents and plugin ecosystems so AI can complete multi-step tasks instead of just answering questions. Why […]

Summary AI agents — autonomous, task-focused AI that can read systems, act, and make decisions — have moved from prototypes into real business use. Low-code builders, tighter CRM/BI integrations, and better governance tools mean companies can deploy agents to handle routine sales tasks, automate reporting, and run operational workflows with less engineering lift than before.

Summary Autonomous AI agents — software that can plan, act, and learn across tools and data — have moved from research demos into real business pilots. Companies are using agents to assemble information, take routine actions, and produce concise reports without constant human prompting. That means faster decisions, fewer manual handoffs, and repeatable automation that

Big picture — the story AI agents — autonomous, task-focused assistants built on large language models — have moved from experiments into real business use. Over the past year, major cloud vendors and a wave of startups have shipped tools that let companies attach agents to their CRM, ERP, and BI systems so those agents

Big idea (short summary) AI “agents” — autonomous tools that can run tasks, talk to software, and act on your data — are crossing from experiments into real business use. Platforms and vendors are building agent frameworks and integrations so these systems can plug into CRMs, ticketing, calendars, and data warehouses. That means AI can

Quick summary AI “agents” — models that can take multi-step actions across apps (email, CRM, spreadsheets, ticketing) — are no longer niche. Over the past 18 months we’ve seen the technology stack mature (agent frameworks, retrieval-augmented generation, connectors to enterprise systems and low-code builders). Companies are using agents to draft proposals, update records, generate executive

Quick summary AI agents—autonomous, goal-oriented programs that can read your systems, take actions, and learn from outcomes—are no longer just demos. Over the past year companies have shifted from proofs-of-concept to production deployments: agents are scheduling meetings, qualifying leads, generating sales outreach, reconciling invoices, and producing recurring management reports. Tools and architectures like retrieval-augmented generation

If you thought AI was just for chat and research, think again. The latest wave of AI agents — systems that can read your data, take actions across apps, and complete tasks end-to-end — is shifting from proof-of-concept projects into real operational use. Companies are using these agents to automate reporting, run outreach, update CRMs,

Quick summary AI agents — autonomous software that can read, decide, act, and follow up — are no longer just a research headline. Over the past year major vendors and enterprise teams have productized agent-style tools that connect language models to your systems (CRM, ERP, ticketing, databases). The result: faster reporting, automated lead qualification, 24/7

The story (short) AI “agents” — autonomous or semi-autonomous AI assistants that complete multi-step tasks — have moved from demos and research labs into real business workflows. Over the past year we’ve seen enterprises and mid-market firms embed agents into sales outreach, account research, invoice processing, and automated reporting. These agents combine large language models,