Ron Mitchell

Summary AI “agents” — autonomous or semi-autonomous systems that can take multi-step actions (think: qualify a lead, schedule a demo, draft a follow-up, and update the CRM) — are moving from demos to real-world use. Companies across industries are piloting agents to automate front-office work, speed up reporting, and free sales reps for higher-value conversations. […]

Short summary AI “agents” — autonomous software that completes tasks by calling apps and using company data — have moved from experiment to practical tool. Over the past year we’ve seen platforms and tools make it much easier to build agents that do things like personalize outreach, automate recurring workflows, and generate on-demand reports. That

Quick summary AI agents — autonomous, task-focused AI that can run workflows, pull company data, and take actions inside business apps — are no longer a lab experiment. Over the past 18 months major platform vendors and enterprise software makers have shipped agent frameworks and integrations that let companies automate repeatable sales and operations work:

Quick summary Over the last year, a wave of enterprise-ready AI agents and orchestration tools has made it practical for businesses to automate complex work — not just simple tasks. These agents combine large language models (LLMs), retrieval-augmented generation (RAG), and connector libraries to access CRMs, ERPs, and shared documents. The result: automated outreach, faster

The story AI “agents” — goal-directed assistants built on large language models that can read documents, call APIs, and take actions — have moved out of labs and into real business workflows. Over the last year we’ve seen platforms and toolkits mature (think agent orchestration frameworks, integrated copilots, and enterprise-ready connectors to CRMs and data

Quick summary AI “agents” — software that can act autonomously across apps and data sources — moved from labs to the enterprise in 2023–24. Vendors added agent-style features to CRM, BI and productivity tools, and open-source frameworks (LangChain, agent libraries) made customized agents easier to build. The result: tools that can qualify leads, build reports,

The story in brief Major vendors and startups have shifted AI agents from demos to real-world tools. New agent frameworks, better model tooling, and easier integrations mean companies can now deploy autonomous assistants that do more than chat: they pull data from CRMs, generate reports, triage leads, schedule tasks, and trigger workflows across apps. Why

Quick summary A new wave of AI agents — small, goal-driven AI programs that act on behalf of users — is moving out of research labs and into everyday business tools. Vendors and startups are embedding agents into CRMs, calendars, chat systems, and analytics platforms so the software can autonomously find leads, qualify prospects, draft

AI story (brief): Over the past year we’ve seen AI models evolve from standalone chat tools into practical, task-focused “AI agents” that can act across apps, pull from company data, and carry out multi-step workflows. Startups and major vendors are shipping agent orchestration platforms and pre-built connectors for CRMs, ticketing systems, and data warehouses. That

Quick summary AI “agents” — software that can act autonomously across systems (email, CRM, calendar, databases) — are no longer just research demos. Modern toolchains (connectors, retrieval-augmented generation, and lightweight orchestration) let companies build agents that qualify leads, run reports, route exceptions, and trigger follow-up actions without constant human babysitting. Why this matters for businesses