Ron Mitchell

Summary AI “agents” — autonomous, goal-driven software that can read your data, run tasks, and talk to other apps — are no longer just a developer toy. In the past year we’ve seen major platforms and toolchains make it much easier for businesses to build agents that integrate with CRMs, data warehouses, and communication tools. […]

Summary There’s been a big surge in interest around AI agents — autonomous, multi-step assistants that can chain together tasks (think: read a CRM record, draft a personalized email, schedule a demo, and update a dashboard). Companies are moving past single-query chatbots to practical agent workflows that automate repeatable sales, operations, and reporting tasks. Why

Summary Autonomous AI agents — software that plans and executes multi-step tasks without constant human prompts — have moved fast from research demos into real business pilots. These agents can pull data from your CRM, draft personalized outreach, run A/B tests, and generate weekly performance reports automatically. That means fewer manual touchpoints, faster decisions, and

Quick summary AI “agents” — autonomous software that can read, act, and follow up across apps — moved fast from demos into real business use. Today’s low-code agent builders and enterprise connectors let companies launch agents that do things like research prospects, draft and send personalized outreach, update your CRM, and produce weekly sales reports

Quick summary AI agent platforms from major cloud providers and startups have moved past demos and into real business use. Today’s agents can hold memory, connect to CRMs and data warehouses, run scheduled reports, and autonomously act on simple tasks (e.g., follow up leads, update records, generate executive summaries). That shift — better connectors +

Summary AI “agents” — task-focused, multi-step AI assistants that can access tools, fetch data, and take actions — have moved from proof-of-concept demos into real workplace use. Modern LLMs plus integrations (enterprise copilots, plugin ecosystems, and APIs) now let these agents do things like enrich leads, draft and send follow-ups, update CRMs, and create automated

Quick summary AI agents — small, autonomous AI apps that can act on behalf of people — moved from experiments into real business pilots in 2024–25. Instead of asking an AI for a single answer, companies are now giving agents repeatable tasks: qualify leads, update CRMs, run exception workflows, and produce recurring reports with commentary.

Quick summary AI “agents” — autonomous, task-focused assistants that can act across apps, take multi-step actions, and learn from feedback — have moved from proofs-of-concept into commercial products. Big platform providers and startups have rolled out agent frameworks, custom GPTs, and integrations that let these systems schedule meetings, draft proposals, update CRMs, run analyses, and

Short summary AI agents — autonomous, goal-oriented assistants built from large language models and connected to your systems — are no longer just demos. Companies are deploying them to handle sales outreach, routine customer follow-ups, automated reporting, and workflow orchestration. The result: faster decisions, fewer manual tasks, and more consistent customer interactions. Why this matters

Quick summary AI “agents” — software that autonomously carries out multi-step tasks using language models, connectors, and business data — moved from proof-of-concept to practical deployments in 2023–2024. Open-source frameworks (Auto-GPT, LangChain) and vendor tools (Microsoft Copilot Studio, Google/Anthropic integrations) made it far easier to build agents that can research, draft, act in apps, and