AI Post

Quick summary AI agents — autonomous software that can read, decide, and act across apps — moved from demos into real business pilots in 2024–25. Companies now deploy agents for things like lead qualification, scheduling, order tracking, and exception handling. The difference vs. traditional automation: agents can combine language understanding, process logic, and connectors to […]

Quick summary – Over the last 12–18 months, more companies have moved from experimenting with chatbots to deploying AI agents that run multi-step tasks — things like research, lead qualification, personalized outreach, and automatically updating sales reports. – These agents combine language models, access to internal data (via retrieval-augmented generation or RAG), and automation tools

Quick summary Autonomous AI agents — software that plans, acts, and completes multi-step tasks with minimal human supervision — have jumped from research demos to real business pilots. Companies are using them to triage customer requests, run data extraction and reconciliation, create periodic reports, and trigger downstream automations. The result: faster workflows, fewer manual handoffs,

What happened (short summary) – Over the last year, leading AI platforms pushed easy-to-build “AI agents” and orchestration tools that do multi-step work on their own — from pulling data and drafting emails to updating systems and generating reports. – These agents combine language models, connectors (to CRM, BI, Slack, etc.), and business rules so

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