Showing 1–12 of 15 articles tagged Agentic AI

Every useful agent becomes a power user of the SaaS underneath it. Install base explodes, API calls multiply, workflow gets more essential, not less.

Chat is the wrong interface for AI agents in professional software work. A well-written issue is a better agent instruction than any prompt.

Everyone is asking which AI agent is best. The real question is which platform agents will work from. The answer is whoever owns the queue.

A 97% attack detection rate sounds fine until an agentic system has tool access, private data, and a path to action. Then it is a breach rate.

Scaffolding gives you 10-20% gains that the next model wipes out. The bitter lesson for product builders: give the model tools and a goal, not a workflow.

Weekend build to 145K GitHub stars to acquisition in weeks. The pattern: agents that execute locally instead of chatting in a browser window win on adoption.

I built AI voice receptionists that handle real phone calls for real businesses. Latency, conversation flow, graceful handoff. Here's what actually matters.

Most teams evaluate agents with manual chats and gut feel. A practical framework for eval suites that let you ship, starting with 20 examples, not 20,000.

If your AI roadmap succeeds, customers need fewer seats and you earn less revenue. The fix: price around the units of work completed, not user logins.

The shift is from prompt engineering to designing multi-agent hierarchies: AI managers overseeing AI workers that operate invisibly in the background.

AI is not a feature, it is a new compute paradigm. Bolting GenAI onto legacy platforms destroys unit economics. If the AI is optional, it's a gimmick.

Google's A2UI signals the end of the chatbot text wall. Agents that render native UI components instead of paragraphs change what product teams build.