Ron Mitchell

Quick summary Over the past year we’ve seen a clear shift: AI agents — autonomous systems that can run workflows, talk to SaaS apps, and generate reports — are leaving lab experiments and showing real business value. Improvements in large language models, retrieval-augmented generation (RAG), vector databases, and low-code agent frameworks make it easier to […]

What happened (short summary) – Over the last 18 months, businesses and platform vendors have accelerated investment in autonomous AI agents — tools that can perform multi-step tasks (research, draft outreach, update systems) with minimal human prompts. – New building blocks — better model APIs, “function calling,” RAG (retrieval-augmented generation — models that pull from

Quick summary AI “agents” — autonomous, multi-step AI assistants that can use tools, access data, and complete tasks — are moving from labs into real company workflows. Over the last year we’ve seen more businesses pilot agents for sales outreach, operations automation, and real-time reporting. These agents can do things like gather customer context, draft

Story summary AI “agents” — small, task-focused AI programs that act like digital assistants — moved from research demos to real business tools in 2024. Vendors and open-source frameworks made it easy to connect LLMs to your systems (CRMs, email, databases, BI tools) so agents can run workflows: gather data, draft messages, update records, or

Quick summary Companies are moving from one-size-fits-all AI models to custom, task-focused AI agents that connect to your apps (CRM, email, Slack, ERP) and run end-to-end workflows. Think: a sales agent that qualifies leads from your forms, updates the CRM, writes follow-up emails, and flags hot prospects — all with minimal human handoff. The same

Short summary AI agents — software that can act autonomously to complete tasks (think: schedule follow-ups, triage support tickets, draft reports) — are no longer just tech demos. Over the last couple of years we’ve seen faster toolkits, agent marketplaces, and more enterprise-ready integrations that let organizations automate repeatable work across sales, ops, and finance.

Quick summary AI agents — autonomous, task-focused systems that can read, act, and learn across apps — are no longer just demos. Businesses are using them to qualify leads, generate sales reports, automate order workflows, and handle routine customer requests. The result: faster decision-making, lower operational cost, and more time for teams to focus on

Recent months have seen a clear shift: AI agents — software that can connect to apps, run multi-step workflows, and act with minimal human direction — are moving from pilots into everyday business use. Instead of just generating text, these agents can qualify leads, update CRMs, compile weekly sales reports, schedule meetings, and trigger follow-ups

Quick summary Autonomous AI agents — software that can carry out multi-step tasks on their own (think: qualify leads, update your CRM, or generate weekly sales reports) — have moved from experiments into production for many companies. Built with large language models, retrieval systems, and workflow connectors, these agents can act like junior employees that

Summary AI agents — think intelligent assistants that can read your systems, take actions, and keep learning — are no longer experimental. Companies are using them to draft proposals, run recurring reports, qualify leads, and orchestrate multi-step workflows across CRM, calendar, and finance systems. Why this matters for business – Time savings: Agents can automate