Ron Mitchell

Summary In recent months major AI platforms from OpenAI, Microsoft and Google have made it easy for companies to build custom AI agents — automated assistants that can access your documents, talk to your apps (CRM, calendar, ticketing), and carry out multi-step workflows. These agents aren’t generic chatbots: they can be trained on your data, […]

Quick summary Over the last year, AI vendors and startups have focused on “agent” features — AI that can perform multi-step tasks, connect to apps, and act with limited human direction. That means tools can now triage customer requests, qualify leads, update CRMs, generate regular sales and ops reports, and trigger workflows — not just

Quick summary AI agents — software that plans, acts, and uses tools to complete tasks on your behalf — moved from experiments to practical business use in 2024. Instead of one-off prompts, these agents can run multi-step workflows: research leads, update CRM records, compile weekly sales reports, or triage customer issues. They connect to APIs,

Quick summary AI agents — autonomous workflows that read your systems, take action, and report back — are no longer just proof-of-concept demos. Advances in large language models, connectors to CRMs and calendars, and better retrieval/guardrails mean companies are deploying agents to handle tasks like lead triage, follow-up emails, meeting scheduling, and automated reporting. Why

Hook AI agents — autonomous assistants that research, draft, execute workflows, and generate reports — are finally crossing from pilot projects into real, revenue-impacting work. That shift matters for sales, operations, and finance teams looking to cut costs and speed decision-making. The story in a nutshell – What’s happening: Businesses are deploying AI agents that

Headline: AI agents are moving from experiments to real business value — fast Short summary More companies are moving beyond one-off AI features and deploying autonomous AI agents that handle repeatable sales and ops tasks: personalized outreach, CRM updates, meeting follow-ups, and automated reporting. These agents combine large language models, company data (via secure retrieval),

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