Ron Mitchell

Quick summary AI agents — autonomous helpers that can read your emails, pull data from systems, and take actions across apps — have moved past the proof-of-concept stage. Companies are now using them to qualify leads, automate repetitive tasks, and create faster, more accurate business reporting. Instead of one-off chatbots, the new wave of agents […]

Quick summary Over the past year we’ve seen a sharp increase in practical “AI agents” — autonomous software that can read your systems, run tasks, and act on behalf of users. These agents combine large language models with connectors, rules, and tools so they can do things like qualify leads, run recurring reports, update CRMs,

Big picture Autonomous AI agents — software that can plan, act, and learn with minimal human hand-holding — are no longer just a research headline. Over the past year we’ve seen these agents move from experiments into real business workflows: handling repetitive outreach, preparing near-real-time sales reports, and automating multi-step operational tasks that used to

What happened AI agents — the software that can read your systems, make decisions, and act across apps — are moving from experiments into production. Vendors and open-source tools now make it easier to connect agents to CRMs, ERPs, BI tools and document stores. That means AI can not only suggest actions, it can execute

Quick summary AI agents — autonomous, task-focused AI assistants that can read data, talk to systems, and take action — are no longer just research demos. Over the last 12–18 months more organizations have moved from experiments to production pilots that automate routine workflows: qualifying sales leads, updating CRMs, generating monthly reports, and handling first-line

Quick summary AI agents are software “helpers” that can access your tools and data, run multi-step workflows, and make decisions inside set boundaries — for example, compiling a sales follow-up list, drafting personalized outreach, updating your CRM, and scheduling meetings without constant human intervention. Over the past year the technology has moved from proof-of-concept to

Short summary More companies are turning AI agents — autonomous assistants that can research, message, schedule, and update systems — from proofs-of-concept into production. Pilots that once sat in labs are now being used to qualify leads, automate reporting, and handle routine customer tasks 24/7. The result: faster decisions, lower labour costs on repetitive work,

Quick summary Over the past year, a new wave of AI agents — autonomous, task-focused AI tools built with frameworks like LangChain and agent orchestration platforms — moved from labs into real business workflows. Companies are using them to run multi-step tasks: qualify leads, draft and A/B test outreach, update CRMs, generate executive reports, and

Quick summary A new wave of “AI agents” — autonomous tools that can plan, act, and connect to your apps — is moving from labs into real business use. Instead of just generating text, these agents can read your CRM, pull data from finance systems, create reports, send emails, and trigger workflows without a human

Short summary Over the past year we’ve seen a clear shift: AI agents—small, task-focused AI systems that act on your behalf—are moving from tech demos into real business use. These agents can pull data across systems, draft customer messages, update CRMs, generate reports, and trigger downstream actions without constant manual input. For businesses this isn’t