AI Post

Quick summary In the last 12–18 months the conversation around generative AI has shifted from one-off chat experiments to practical, autonomous “AI agents” that connect to real systems — CRMs, calendars, BI tools and document stores — and carry out business tasks on their own. Companies are using these agents to draft personalized outreach, run […]

Quick summary Autonomous AI agents — programs that can act, decide, and complete tasks with little human direction — are no longer a lab curiosity. Over the past 18–24 months the technology stack (agent frameworks, reliable APIs, better retrieval/knowledge grounding) has matured enough that companies are running pilots and putting agents into production for sales,

Summary AI “agents” — autonomous AI assistants that can read, act, and execute multi-step tasks — are no longer just experimental demos. Businesses are starting to deploy them for lead qualification, customer follow-ups, automated reporting, and routine process work (for example: triaging emails, creating weekly sales dashboards, or updating CRM records). These agents combine large

Quick summary Over the past year, autonomous AI agents—small systems that carry out tasks end-to-end (think: qualify a lead, create a tailored proposal, update your CRM, or build a weekly sales report)—have moved out of labs and into real business pilots. Better large language models, easier integration tools, and no-code “agent builders” mean these agents

The story in plain terms AI agents — autonomous, task-oriented systems built on large language models plus tools (search, CRM connectors, databases) — have crossed from lab demos into real business workflows. Big vendors (Microsoft Copilot in 365/Dynamics, Google Duet/Workspace) and an ecosystem of agent frameworks and vector databases have made it realistic to automate

Quick summary AI “agents” — systems that can act on your behalf across apps and data — moved from lab experiments into real business pilots in 2024–25. Companies are using agents to do things like qualify leads, generate weekly sales reports, manage scheduling, and run multi-step workflows without constant human hand-holding. Why this matters for

Quick summary Autonomous AI agents — software that can carry out multi-step tasks with little human direction — moved from research labs into real business tools over the last 18 months. Platform vendors rolled out agent builders (custom GPTs, Copilot studios, and similar tools), and companies started using agents for things like automated lead qualification,

Quick summary Companies are rapidly adopting AI agents — small, goal-driven systems that use large language models plus internal data to perform tasks (think: summarize calls, qualify leads, draft personalized outreach, or auto-build weekly reports). In 2023–24 we saw these agents move from experiments into practical pilots because two things got better: retrieval-augmented knowledge (so

Quick summary AI “agents” — task-oriented AI that can act, plan, and follow workflows — are moving out of labs and into real-world business use. Major cloud vendors and AI platform providers have released agent frameworks and integrations that let companies automate multi-step tasks: qualify leads, generate quotes, summarize meetings, and produce recurring sales and

The story (short summary) AI “agents” — autonomous, task-focused AI assistants built on models like GPT — have moved beyond demos. Over the last year we’ve seen a wave of tools and platforms that let teams create low-code agents to handle specific workflows: qualifying leads, updating CRMs, summarizing meetings, and generating regular sales and financial