AI Post

Big idea — what happened Over the last year we’ve seen AI agents move from research demos to real business tools. These aren’t just chatbots — they’re autonomous workflows that combine large language models with connectors (CRMs, spreadsheets, email), retrieval (RAG), and simple logic to complete multi-step tasks. Companies are using agents to automatically prepare […]

Why this matters now AI agents — autonomous, task-focused AI that can act across apps and data — moved from lab experiments to real business pilots in 2024–2025. Companies are using agents to run outreach, triage support tickets, assemble weekly reports, and automate recurring workflows. That shift matters because agents can reduce repetitive work, accelerate

Quick summary AI agents — small, task-focused systems powered by large language models — are shifting from pilots into regular use across sales, finance, and operations. Teams are using them to automate repetitive work: qualify leads in the CRM, generate weekly sales and finance reports, triage support tickets, and run routine reconciliations. The result is

What happened AI agents — autonomous software powered by large language models that can take actions (send emails, update CRMs, run queries, create reports) — are suddenly practical for real business work. Improvements in model reliability, easier integrations (APIs, Zapier/Workato, CRM connectors), and safer control layers (human-in-the-loop, permissioning) mean companies are moving from pilots to

Short summary AI “agents” — autonomous software that plans, acts, and iterates to complete multi-step tasks — have moved from research demos to practical business tools. Using frameworks like LangChain and orchestration platforms, companies are now running agents that draft outreach, reconcile reports across systems, automate approvals, and triage customer issues without constant human direction.

Summary AI agents — autonomous, connected AI tools that can read CRM data, send messages, schedule meetings, and generate reports — have moved beyond demos. Companies are now using them to qualify leads, automate routine customer support, and produce near-real-time reporting. The result: faster sales cycles, lower operational cost, and more consistent follow-up. Why this

Quick summary OpenAI’s rollout of custom “GPTs” (no-code, customizable AI assistants) has pushed AI agents into the mainstream. Businesses can now create purpose-built agents that access company data, run simple workflows, answer questions, and connect to external tools — all without building a model from scratch. Why this matters for businesses – Faster deployment: Teams

Story summary AI agents — software that can act autonomously across apps and data — have moved from demos into real enterprise use. Modern agents combine large language models with connectors, retrieval-augmented generation (RAG), and orchestration tools so they can complete multi-step tasks: qualify leads, pull and summarize sales reports, update CRMs, schedule follow-ups, and

Trending story (short summary) AI agents — small, purpose-built AI programs that can read your CRM, send emails, schedule meetings, and pull data from dashboards — went from proof-of-concept to practical in 2024–25. Advances in retrieval-augmented generation (RAG), secure connectors to business systems, and “no-code” agent builders mean companies can now create agents that handle

Quick summary AI agents — autonomous or semi-autonomous AI “workers” that can read, act, and follow multi-step workflows — have moved from experiments into real business apps. Improvements in large language models, retrieval-based pipelines, and low-code agent builders have made it much easier to connect agents to CRMs, ERPs, ticketing systems, and data warehouses. That