Ron Mitchell

Summary AI agents — software that can act autonomously on your behalf (for example, draft outreach, update a CRM, or build a report) — are crossing a key threshold. Over the past year the tools and integrations around large language models have matured: easier connectors to databases and CRMs, built-in guardrails, and lightweight orchestration frameworks. […]

Short summary AI “agents” — small, task-focused systems that combine large language models with tools and data (think automated assistants that can use your calendar, CRM, and dashboards) — are moving quickly from experiments to real business use. Better models, easier integrations, and patterns like retrieval-augmented generation (RAG) mean agents can do work that used

Summary AI “agents” — autonomous or semi-autonomous AI tools that can act across apps, talk to customers, and complete tasks — have moved from experiments to real business use. Companies are using them to qualify leads, update CRMs, generate weekly sales reports, and even draft quotes or follow-up emails without a human in the loop

Summary Over the last couple of years, open-source agent frameworks and toolkits (think LangChain, LlamaIndex and similar platforms) have matured enough that companies can build useful, reliable AI agents without starting from scratch. These agents can read your documents, run workflows, update CRMs, and create automated reports — all tied into your existing systems. Why

Quick summary Autonomous AI agents — software that can take multi-step actions, connect to apps, and run without constant human prompts — have moved from experiments into real business use. Instead of only answering questions, these agents can qualify leads, update CRMs, run recurring reports, and coordinate workflows across systems. Why this matters for business

Quick summary AI “agents” — autonomous software that researches, composes, and takes actions (like drafting outreach, updating CRMs, or pulling reports) — are moving from labs into real business pilots. Over the last year many companies have run pilots where agents handle prospect research, produce personalized emails, compile performance reports, and even triage customer requests.

Summary Autonomous AI agents — small, task-focused AI systems that can access tools, apps, and data to complete multi-step work — are moving out of labs and into real business use. Over the past year we’ve seen bigger vendors and startups ship agent frameworks that plug into CRMs, calendars, email, and reporting tools. That means

Quick summary AI agents — autonomous, workflow-focused AI that can act on your data, speak to apps, and follow multi-step instructions — have shifted from demos to real, revenue-driving deployments. Sales and operations teams are using agents to qualify leads, draft and send personalized outreach, update CRMs, summarize meetings, and generate regular sales reports automatically.

Quick summary Over the past year businesses have moved AI agents from experiments into production. These are LLM-powered “agents” that can run multi-step workflows: fetch data, apply rules, write emails, update CRMs, and generate executive reports — all with less human hand-holding. That shift is making automation more flexible and more valuable than older, rule-only

Quick summary AI agents — software that can act autonomously across tools, systems, and data — are moving from research demos into real business use. Vendors and startups now offer agent orchestration, connectors to CRMs/ERPs, and built-in reporting so agents can do more than draft text: they can find leads, update records, run follow-ups, and