Ron Mitchell

Summary Over the last year, we’ve moved past proof-of-concept chatbots and into a new era: autonomous AI agents that can act across apps, pull from your systems, and complete multi-step work. These agents are being used to draft personalized sales outreach, generate recurring financial reports, triage customer issues, schedule follow-ups, and trigger downstream automations — […]

Quick summary AI “agents” — systems that combine large language models with connectors, memory, and decision logic — are no longer just research demos. Over the past year we’ve seen enterprise-grade agent platforms (think Copilot-like studios and LangChain-based frameworks) and off-the-shelf connectors to CRMs, finance systems, and reporting tools. Companies are using these agents to

Quick summary Over the past year we’ve seen a clear shift: AI agents (autonomous, scriptable assistants that can use tools, run workflows, and talk to systems) are no longer just R&D demos. Companies are deploying them to qualify leads, run recurring sales reports, update CRM records, and automate follow‑up — tasks that used to eat

Quick summary OpenAI’s 2024 push around custom GPTs — plus similar agent tools from Microsoft and Google — has made it far easier for companies to create purpose-built AI agents. These agents can sit on top of your data, connect to CRMs and calendars, draft emails, generate recurring reports, and carry out multi-step workflows with

Quick summary AI agents — autonomous, LLM-powered bots that can read your data, take actions, and follow multi-step workflows — are no longer just headlines. Companies are using them for lead qualification, automatic invoice processing, real-time sales reporting, and customer follow-up. When paired with retrieval-augmented generation (RAG) and connectors to CRMs, ERPs, and email, these

Short summary AI “agents” — autonomous assistants that can read your systems, take actions, and follow up on tasks — are moving from labs into real business use. They’re being used to automate outreach, qualify leads, update CRMs, and generate routine reports. The result: faster response times, fewer manual errors, and more time for reps

Short summary Autonomous AI agents — software that can take multi-step actions on its own — have moved from experiments to practical tools for business teams. In 2025 we’re seeing a wave of agent platforms and “orchestration” tools that let companies automate end-to-end workflows: qualifying leads, routing customer issues, generating weekly sales reports, and even

Quick story The biggest recent shift isn’t a single product — it’s a tipping point. AI “agents” (autonomous assistants that combine language models with tool access and data retrieval) have moved from research demos into real business workflows. Vendors like Microsoft and Google baked agent-like copilots into office and cloud tools, and open-source frameworks (LangChain,

Summary AI “agents” — small, autonomous systems built from large language models and workflow tools — are no longer just labs and demos. Today’s agents can act across apps: researching prospects, drafting personalized outreach, updating CRM records, and producing regular sales or performance reports without manual handoffs. Why this matters for business – Save time:

What’s happening AI agents — software that performs multi-step tasks on behalf of users — moved from research demos into real business tools. Over the past year we’ve seen platforms and frameworks make it easier to connect language models to systems (CRMs, email, reporting tools) so agents can run workflows end-to-end: qualify leads, generate and