Ron Mitchell

Quick summary AI agent builders — think no-code and low-code platforms that chain LLMs to your tools and data — are moving from tech demos into real business use. Platforms like LangChain-based toolkits, Microsoft’s Copilot Studio, and similar offerings let teams assemble task-specific agents that read CRM records, pull reports, draft emails, schedule meetings, and […]

Quick summary AI agents — small AI programs that plan and act on your behalf — are no longer just a developer curiosity. Tools like AutoGPT, LangChain agents, and the agent features from major vendors have made it simple to automate multi-step work: gather data, update systems, draft outreach, generate reports, and follow up. For

What’s happening AI agents — autonomous, task-focused AI that can act on your behalf (reach out to customers, update CRM records, generate reports, route approvals) — are finally moving out of experiments and into real business use. Improvements in large models, easier connectors to enterprise apps, and lower deployment costs mean teams can now build

Quick summary AI agents — autonomous, task-focused AI that can act across apps and data — are no longer just lab experiments. Major vendors and startups are productizing agent frameworks and “agent-as-a-service” offerings that plug into CRMs, calendars, and reporting systems. The result: agents that can draft outreach, qualify leads, update records, generate reports, and

Quick summary AI “agents” — autonomous software that can use tools, access company data, and complete tasks end-to-end — are no longer just lab demos. Businesses are starting to deploy agents to handle repetitive, multi-step work like lead enrichment and outreach, invoice handling and exception resolution, and automated reporting that combines live data with natural-language

Summary AI “agents” — autonomous, goal-oriented AI programs that can plan, act, and iterate — have moved from research demos into practical business use. Teams are deploying agents to handle repeating knowledge-work tasks: triaging customer messages, drafting and routing sales follow-ups, generating weekly performance reports, and orchestrating multi-step automation across cloud apps. Why this matters

Quick summary AI agents — autonomous software that can carry out multi-step tasks (like qualify leads, schedule meetings, or generate reports) — are no longer just demos. More companies are deploying them inside CRMs, customer service platforms, and reporting stacks to handle routine sales and ops work. That means faster responses, fewer manual steps, and

Quick summary AI “agents” — autonomous programs that can act on behalf of users, chain together steps, and call apps and data sources — are moving from research demos into real business use. Today’s agent platforms make it easier to automate multi-step tasks: qualifying leads, compiling weekly sales reports, monitoring competitors, triaging customer requests, or

Short summary AI “agents” — purpose-built LLM-based assistants that can call tools, access data, and complete multi-step tasks — have moved from demos into real business use. Over the past year we’ve seen an explosion of agent builders, prebuilt workflows, and integrations that make it feasible to automate things like lead qualification, routine customer replies,

AI agents move into the boardroom: practical steps for business AI, automation, and reporting Summary — what’s happening now AI “agents” — models that can take action, follow multi-step workflows, and connect to your systems — are shifting from experiments to real business tools. Instead of just answering questions, these agents can run processes (like