Ron Mitchell

There’s a clear shift happening: AI agents — task-focused, autonomous assistants built on large language models — are moving fast from experiments into real workplace use. Instead of just answering questions, these agents can fetch data, run processes, generate reports, and trigger actions across apps. That makes them a practical tool for sales teams, operations, […]

Quick summary 2024 accelerated a clear shift: AI agents — software that autonomously performs tasks by chaining prompts, using tools, and accessing company data — moved from lab experiments into real business pilots. Agent frameworks (LangChain, Auto-GPT-style approaches) and vendor copilots (Microsoft, Google, others) made it easier to automate workflows, generate timely reports, and act

Quick summary AI “agents” — autonomous, multi-step AI programs that can access apps, pull data, and take actions — have moved from lab demos to real business pilots. Toolkits and platforms (think agent frameworks, API function-calling, and integrations with CRMs, calendars, and BI tools) make it possible to automate entire workflows: research, outreach, approvals, and

What’s happening – Over the last year major vendors and startups have focused on “AI agents” — small, goal-driven AI programs that can connect to tools (email, CRMs, cloud storage), fetch data, take actions, and hand off results. – These agents are getting easier to build and safer to run thanks to improved connectors, role-based

Quick summary AI “agents” — autonomous software that can plan, act, and follow up without constant human prompts — are moving from experimental labs into real business workflows. Over the past 18 months we’ve seen more off-the-shelf agent frameworks (LangChain, AutoGPT-style patterns, vendor-built agents in Copilot/GenAI suites) and packaged “agent-as-a-service” tools that connect to CRMs,

Quick summary AI “agents” — autonomous workflows that can read, act, and make decisions across apps — have moved from experiments to real business pilots. Tools like Auto-GPT, LangChain-based agents, and agent features from major LLM providers let a model run multi-step tasks (research, extract data, update systems, send messages) without constant human prompting. Why

What’s happening AI “agents” — autonomous workflows that combine large language models with connectors, tools, and decision logic — have moved from lab experiments into real business pilots. Major platforms and open-source frameworks now make it easier to chain tasks, call internal systems, and act on results without a person clicking every step. Why it

Quick summary AI “agents” — models that act autonomously across systems (think: read emails, pull CRM data, create reports, and take actions) — moved from labs into real business pilots in 2024–25. Vendors and open-source toolkits now make it easy to connect LLMs to calendars, CRMs, databases, and workflow systems. That means companies can automate

Quick summary AI “agents” — models that can act across apps, run tasks, and follow goals with minimal human prompts — moved from experiments into real business pilots over the last 18–24 months. Toolkits and orchestration platforms now let companies connect agents to CRMs, calendars, email, databases, and reporting stacks so the AI can do

Short summary AI agents—autonomous AI assistants that can read, act, and integrate across apps—moved from lab demos into real business tools in 2024. Major vendors and open-source projects are shipping agent frameworks and “Copilot”-style assistants that can draft emails, update CRMs, generate weekly reports, and kick off workflows automatically. That means routine tasks that once