use-cases
Sales reps don't have a talent problem; they have a time distribution problem. Sales reps spend only 28% of their time actually selling, according to Salesforce's AI Sales Agent Guide, with the rest going to Customer Relationship Management (CRM) data entry, lead research, follow-up sequencing,
Education
TL;DR * AI agent workflows handle multi-step ops end-to-end; traditional automation stops at one rule * Building one takes four decisions: trigger, tool chain, judgment rules, and oversight loop * Teams scaling ops use platforms like Pazi to run these workflows inside Slack Table of Contents * What an AI
Use Cases
Real estate teams have added more tools in the past three years than in the previous decade. The coordination load didn't go with them, because tools generate output and stop; AI agents monitor workflows, act when conditions are met, check the result, and keep going until the work
Use Cases
TL;DR * Marketing's real bottleneck is logistics, moving workflows forward without a human at every step. * AI agents now own content production, email nurturing, social scheduling, and analytics reporting end-to-end. * Teams scaling ops are moving to agent platforms like Pazi - built for judgment plus execution. Table
use-cases
74% of consumers now expect 24/7 customer service availability, a standard AI created that most support teams are still building toward. The teams that have closed the gap changed how their tickets move. Their agents own the full resolution loop, checking order history, applying business rules, escalating on defined
pazi-vs
Key Takeaways * If your operations team needs agents running shared workflows across Slack, Teams, and 22+ channels end-to-end, Pazi is built for that. Lindy is not. * Lindy is a personal AI work assistant for one user's inbox, calendar, meetings, and follow-ups. Over 400,000 professionals
ai-agents
Finance teams that have deployed robotic process automation (RPA) know what comes after the rollout. The tool handles routine volume cleanly, then creates a parallel manual process for everything it can't resolve. That second queue is usually where the costly exceptions accumulate. AI agents handle that queue differently,
AI Agents
Most teams I talk to describe their AI setup as "running multiple agents" when they're actually running one agent with a long list of tools. The distinction sounds semantic until the operation gets complex enough that the single context window starts dropping things: wrong context, missed
Education
Operations teams have 12 core workflows where AI agents replace manual coordination, triage, and reporting, from incident response to compliance prep. If you haven't automated at least half of these, you're spending engineering time on work that should run itself. TL;DR - AI agents handle incident
Education
Enterprise AI Agent Platform: A CTO's Guide to Autonomy at Scale Most CTOs evaluating an enterprise AI agent platform are being pitched two categories of product that don't deliver what the category name implies. The first is a raw LLM API with a tool-calling layer:
Education
Project management automation is the practice of offloading repeatable coordination work (status aggregation, blocker detection, and reporting) to software systems so that the project manager can focus on the decisions that require human judgment. Rule-based automation handled the easy end of this for a decade; the status meetings didn&
Education
Most people describing an AI agent are describing a chatbot. The two are different in a way that matters: a chatbot responds when you ask it something. An agent runs on your behalf, calls tools, completes work, and stops when the job is done. You don't need to