Ron Mitchell

Summary Autonomous AI agents — tools that can read your systems, make decisions, and take multi-step actions — are moving from demos into real business work. Instead of one-off chat answers, these agents can update CRMs, compile weekly sales reports from multiple data sources, triage customer requests, and even trigger downstream workflows automatically. That means […]

Summary — the story in plain language – In the last year we’ve seen a big push to make AI agents — software that can act autonomously on behalf of a user — practical for everyday business work. New platforms and tools make it easier to connect agents to your systems (CRM, ERP, knowledge bases)

Quick summary • AI “agents” — autonomous workflows that combine generative models with actions (CRM updates, emails, data pulls, reporting) — are no longer just developer demos. Major vendors and platforms have built orchestration layers and connectors that let agents operate across business systems. • That matters because agents can automate routine sales and ops

Quick summary AI agents — autonomous systems that combine large language models with tools, data connectors, and decision rules — are moving from tech demos to real business use. Modern agent frameworks (custom GPTs, agent libraries, and platform integrations) let companies automate multi-step work: qualify leads, generate tailored outreach, update CRMs, create BI reports, and

AI agents move from lab demos to real sales workflows — what leaders should do next Summary — the story in plain terms Over the last year we’ve moved past proofs-of-concept: AI agents — autonomous assistants that can run tasks, pull data, and make decisions — are being embedded into real business processes. Platforms and

Quick summary AI agents — purpose-built AI “workers” that can research, make recommendations, and take actions across systems — have moved from proofs-of-concept into real business deployments. Companies are using agents to automate repetitive sales tasks, generate faster customer-facing reports, and orchestrate multi-step workflows (e.g., lead enrichment → personalized outreach → meeting scheduling). This shift

Quick summary AI “agents” — autonomous or semi-autonomous AI that can carry out multi-step tasks across apps — are moving from experiments into real business use. Over the past year we’ve seen major platforms and no-code builders make agent development easier, and companies are starting to deploy agents for things like prospect research, meeting follow-ups,

Hook AI agents — persistent, task-focused AI assistants — are no longer just a lab experiment. Over the past 18–24 months they’ve moved into real business workflows: qualifying leads, generating management reports, triaging customer issues, and automating routine processes. What happened (short summary) – Better foundation models + agent frameworks (tool integrations, retrieval-augmented memory, low-code

Step 1 — The story Over the last 18 months major vendors and open-source toolkits have shifted AI agents from research demos into everyday business tools. Microsoft and Salesforce expanded built-in copilots for sales and productivity, while frameworks like LangChain/AutoGen and plugin ecosystems made it far easier for companies to build custom, task-focused agents. The

Quick summary Over the last year major AI platforms made it much easier to build purpose-built AI agents. No-code “custom GPTs” and agent frameworks (plus integrations from vendors like Microsoft and Google) let companies create assistants that read your CRM, generate reports, draft outreach, and even kick off workflows — without rebuilding models from scratch.