Ron Mitchell

Quick summary AI agents — autonomous, task-focused systems that can browse data, run workflows, and take actions on behalf of users — are no longer an experiment. Over the past 18–24 months, vendors and enterprises have moved from PoCs to production deployments: internal agents that qualify leads and book meetings, finance agents that assemble monthly […]

Summary AI “agents” — autonomous workflows that combine large language models, tool connectors, and your business data — are transitioning from experimental pilots to practical production use. Instead of single-chat assistants, companies are now using agents to run end-to-end tasks: qualify leads, update CRMs, generate weekly reports, triage customer requests, and even coordinate cross-team approvals.

Quick summary AI agents — autonomous workflows powered by large language models that can call tools, read your data, and act without constant human prompts — are moving from research demos to real business use. Frameworks and integrations that let agents connect to CRMs, calendars, databases, and reporting tools have made them practical for tasks

Summary Autonomous AI agents — software that can act, decide, and complete multi-step tasks with little human prompting — have moved beyond flashy demos. Companies are now using agents to draft outreach, triage customer support, update CRMs, and generate automated reports. That shift means AI is no longer just an assistant: it can be an

Quick summary Major vendors and SaaS platforms are adding AI agents — autonomous or semi-autonomous systems that can read data, take actions across apps (CRM, email, calendar), and generate reports. Instead of just answering questions, these agents can qualify leads, draft outreach, update records, and produce recurring performance reports — freeing teams from repetitive work.

What happened (quick summary) – Over the last 18 months we’ve seen a big jump in autonomous AI agents — systems that can use tools, access company data, and complete multi-step tasks without constant human prompting. – These agents combine large language models, retrieval-augmented generation (RAG), and connectors to CRMs, calendars, cloud storage, and APIs

Quick summary – What’s happening: A new wave of AI “agents” — models that act autonomously across apps, pull from company data, and complete multi-step tasks — is moving from experiments into real business use. Advances in multimodal models, connectors (APIs/plugins), and retrieval-augmented generation (RAG) make those agents practical for day-to-day workflows. – Why it

What’s happening Over the last year we’ve seen a rapid shift: autonomous AI agents — software that can carry out multi-step tasks with little human prompting — are moving from demos into real business use. Vendors and automation platforms are adding agent-style workflows that can chase leads, qualify prospects, generate reports, and trigger back-office actions

Quick story – Over the last 18–24 months, “AI agents” — LLM-powered assistants that can take multi-step actions, connect to apps, and automate decisions — moved from prototypes to real business deployments. – Companies are using these agents for sales outreach, customer service triage, automated monthly reporting, and routine back-office work (invoicing, reconciliation, approvals). –

Quick summary Microsoft’s Copilot Studio (announced at Build 2024) makes it much easier for companies to build customized AI “copilots” that connect to your internal systems — Microsoft 365, Power Platform, Azure services and more — and run actions (not just answer questions). Google, OpenAI and cloud providers are following the same pattern: low-code tools,