Ron Mitchell

Short summary AI “agents” — autonomous, task-focused systems that can search, summarize, take actions, and talk to other apps — have moved from proof-of-concept into real business use. Over the past year we’ve seen more vendors and frameworks make it easier to deploy agents that handle prospect research, automate follow-up, generate recurring reports, and trigger […]

Quick summary AI platforms from OpenAI, Microsoft, Google and open‑source toolkits have made it easy for non‑technical teams to build custom AI agents — purpose‑built bots that handle tasks like lead triage, automated reporting, meeting scheduling, and customer follow‑up. These “agents” combine natural language prompts, connected company data (CRMs, spreadsheets, ticket systems), and simple workflows

Summary AI “agents” — small, task-focused software that can read, decide, act and learn — are moving out of labs and into everyday business systems. Over the past year we’ve seen CRMs, sales platforms and automation vendors add agent-style features that can draft personalized outreach, follow up automatically, log updates to your CRM, and generate

Quick summary AI agents — autonomous AI assistants that can run tasks end-to-end — are no longer just demos. Improvements in large language models, better integration tools, and vendor platforms have made agents practical for automating routine business workflows: sales outreach, data gathering, monthly reporting, and repetitive approvals. Companies are piloting agents to save time,

Quick summary AI “agents” — AI models that call tools, search data, and carry out workflows — are no longer just experiments. Businesses are now using them to qualify leads, automate outreach, generate sales reports, and handle routine ops tasks. These agents combine large language models with connectors (CRM, calendar, ticketing), retrieval-augmented generation (RAG) for

Quick summary – Over the past year, AI agents — autonomous software that can read, decide, act and use tools — have shifted from lab demos to real business pilots. – Companies are using agents to automate tasks like personalized sales outreach, invoice processing, and monthly reporting. The common thread: agents combine LLMs with data

Quick summary There’s been a clear shift: AI agents — autonomous, task-focused AIs that can call apps, run processes, and follow multi-step workflows — are moving from R&D experiments into real business use. Over the last year we’ve seen lots of vendor and startup activity making these agents easier to connect to CRMs, calendars, and

The story (short) Over the past year we’ve moved from flashy demos to real-world deployments of autonomous AI agents — software that can research prospects, draft outreach, update CRMs, prepare weekly reports, and even schedule follow-ups with minimal human supervision. Companies of all sizes are running pilot projects that combine AI agents with existing tools

Quick summary Over the past two years we’ve moved from isolated generative AI experiments to practical, agent-style tools that can act on behalf of users: triage customer requests, generate and distribute sales outreach, and assemble executive reports from multiple data sources. Vendors have baked “Copilot” and agent frameworks into productivity suites and cloud platforms, and

Quick summary AI agents — autonomous software that connects to your apps (CRM, email, calendar, data warehouse) and completes tasks — have moved from lab experiments into real business use. Instead of one-off AI features, teams are deploying agents that qualify leads, draft personalized outreach, update records, and generate recurring reports without constant human supervision.