Showing 1–12 of 21 articles tagged Agentic AI

I regularly run two or three OpenChair agent sessions at once. The gain comes from parallel execution, but every thread still competes for one human attention budget.

I built OpenChair with specialist AI agents across product, UX, engineering and release. Execution scaled. Accountability stayed with me.

Agentic AI changes developer experience: platform engineering now owns context, guardrails, telemetry, evals, and agent-ready golden paths.

An agent-ready platform works for humans and browser agents: stable actions, permissions, observability, and UX that survives automation.

Agent strategy starts with the work customers need done. Without that map, you are just automating organisational noise.

Solo polymath or multiplayer trio? Wrong frame. In the agentic coding era, the Three Amigos became a coordination protocol, and the strong ones win.

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.