AI Post

Quick summary AI agents — software that can understand goals, pull data, and take actions across apps — are moving from labs into real business use. Instead of only answering questions, modern agents can draft and send emails, update CRMs, generate month-end reports, triage support tickets, and trigger workflows across tools. Companies piloting agents report […]

Quick story Over the last year we’ve seen a rapid shift from single-purpose AI tools to “AI agents” — systems that can run sequences of actions, call internal systems, and complete multi-step business tasks (think: pull CRM data, enrich leads, draft follow-up emails, and update forecasts). Vendor offerings (custom GPTs, agent platforms and Copilot-style products)

Quick summary AI “agents” — models that act on your behalf, use tools, and follow multi-step workflows — have moved fast from academic demos to practical business tools. Over the last year you’ve likely seen vendors and startups offering agent frameworks that connect language models to calendars, CRMs, databases, and reporting tools. That means businesses

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,