AI Post

Quick summary AI “agents” — software that can take multi-step actions (not just answer questions) — are moving out of demos and into real business workflows. Companies are connecting agents to CRMs, ticketing systems, and analytics tools so the agents can draft outreach, triage support tickets, build KPI reports, and even schedule next steps automatically. […]

Quick summary AI agents — autonomous, task-focused AI assistants that can plan, act, and follow multi-step workflows — are moving from research demos into real business use. Companies are using agents to handle end-to-end tasks like lead qualification, scheduling, customer triage, and routine reporting. The combination of agent frameworks (tooling that connects AI to your

Quick summary Recent advances in large language models — notably OpenAI’s GPTs and the lower-latency, multimodal GPT-4o family — have made building customized AI agents faster, cheaper, and more accessible. No-code “custom GPTs,” richer plugin/data connectors, and better function-calling let companies create focused assistants that read your CRM, draft reports, triage support tickets, or run

Quick summary AI agents — autonomous, goal-oriented systems built on large language models — have moved from experiments to real business pilots. Advances in multimodal models, tool integrations (APIs, CRMs, RPA), and open frameworks have made it easier to build agents that fetch data, run analyses, and take routine actions without constant human supervision. Why

Quick summary AI “agents” — software that can research, decide, and act across systems — are no longer just experiments. Across industries, companies are using agents to run end-to-end workflows: qualify leads, draft and send personalized outreach, update CRMs, and generate management reports automatically. The result is faster sales cycles, fewer manual errors, and real-time

Short summary AI platforms now make it easy for businesses to build custom AI agents that can read your CRM, query your ERP, schedule actions, and produce ready-to-share reports. Instead of one-off prompts, these agents can run workflows autonomously — draft follow-ups, flag opportunities, generate weekly sales dashboards, and push alerts when numbers deviate. Why

A short story Autonomous AI agents — systems that combine large language models with connectors, tools, and simple decision rules — are no longer just lab experiments. Over the past year organizations have moved from one-off demos to production agents that: – Triage customer tickets and draft responses – Pull and prepare weekly sales and

What’s happening AI agents — software that can plan and execute multi-step tasks on its own — are moving from experiments into everyday business use. Today’s agents can search for leads, draft personalized outreach, update CRMs, generate weekly dashboards, and even follow up on routine customer questions without constant human prompts. Companies are using them

Short summary Major software vendors and startups are embedding autonomous AI agents into everyday apps — from CRMs that draft and send outreach, to helpdesks that triage tickets, to finance tools that reconcile transactions and generate variance reports. These agents don’t just assist; they can act on your behalf, chain tasks together, and surface insights

Story summary AI agents — persistent, task-focused AI that can act across apps and systems — have moved from lab demos to real business use. Instead of one-off chat answers, these agents run recurring workflows: qualify leads in your CRM, follow up with customers, generate weekly sales reports, or triage support tickets automatically. Improvements in