Ron Mitchell

Quick summary AI agents — autonomous, task-focused AI that can read, decide, and act across apps — moved from experiments to practical tools in 2024–25. Companies now use agents to run outreach sequences, update CRMs, generate weekly sales reports, and triage customer questions without constant human direction. The result: faster workflows, fewer manual tasks, and […]

Quick summary AI agents—autonomous software that uses large language models plus connectors and tools to complete tasks—are no longer just experiments. Businesses are deploying agents that qualify leads, automate reporting, reconcile invoices, and assist customer service 24/7. Improvements in model reliability, retrieval (connecting models to your company data), and off-the-shelf connectors mean agents can now

Quick summary There’s a clear shift happening: autonomous AI agents — software that can plan, act, and follow up without constant human prompts — are moving from R&D experiments into real business workflows. Today they’re being plugged into CRMs, calendars, ticketing systems, and reporting stacks to handle tasks like prospect outreach, meeting scheduling, invoice triage,

Hook AI agents — small, purpose-built AI programs that can connect to your systems, fetch data, and take actions — are moving from labs into real business use. That shift is changing how companies handle sales, reporting, and routine operations. The story in plain terms – Over the past year, major cloud vendors and a

Quick summary AI agents — autonomous software that can read, act, and learn across business apps — are moving from experiments to real revenue drivers. Teams are combining large language models with automation platforms and internal data to create agents that do things like qualify leads, generate custom proposals, and produce automated sales reports. That

Quick summary – The big trend: autonomous AI agents — small systems that chain together LLM prompts, API calls, and business rules — are moving from demos into real business use. – These agents can do tasks end-to-end: qualify leads, update CRMs, draft proposals, pull and summarize cross‑system data, and generate regular reports with explanations.

Quick summary AI agents — autonomous software that reads your data, takes actions, and talks to other systems — are moving from labs into everyday business. Advances in retrieval-augmented generation (RAG), secure connectors to CRMs and data warehouses, and “agent frameworks” from major cloud vendors mean these agents can now draft personalized outreach, update records,

Short summary AI agents — autonomous AI tools that can read data, run actions, and talk to systems — have moved from experiments to everyday business use. Over the last year vendors and open-source projects matured agent frameworks, made integrations to CRMs and BI tools easier, and added governance features so companies can deploy agents

Summary Over the past year, a clear shift has accelerated: AI agents — models that can act autonomously across apps and data — are moving out of pilots and into real business workflows. Instead of only answering questions, these agents now execute multi-step tasks: pulling CRM data, generating outreach, reconciling invoices, and updating dashboards. The

Summary AI agents — autonomous software that can read, act, and learn across apps (think: qualify leads in your CRM, draft outreach, update records, and generate reports) — are no longer just lab projects. Companies are rolling them into real workflows to save time, reduce manual errors, and deliver faster insights. That means less manual