https://link.excalidraw.com/l/1rjiAc1l3pr/Az6YBFbMK8U https://docs.google.com/document/d/1sjU7lPTK3asOvIaDTpxC8EMm4kY6OZA0xhAYoglJC1g/edit?tab=t.0#heading=h.ga2p8l61ojcn Please feel free to ask any questions👇

People say they run gazillion agents. How is that even possible? Do they run "agents" or "crons"? In Hermes, would it mean having a ROLE.md file for each "agent" or is one Hermes setup a single agent capable of doing multiple crons? Your Watson is doing a lot of things, but it's ONE agent right?

I worked with both Nikhyl, and Jagjit at Google. This conversation is well worth your time. https://www.youtube.com/watch?v=5y3kx8TTsaI

There is a pattern showing up across organizations right now. Someone builds an AI agent, it works, people start using it, and then quietly, gradually, things go wrong. The research brief pulls from outdated sources. The support replies reference an old refund policy. The backlog items confuse the engineering team. Nobody can quite explain why, because nobody was really watching. The problem is not the agent. The problem is that nobody owned it. Most conversations about AI agents focus on building. How do you create one? Which tools do you use? How do you connect it to your data? These are reasonable questions, but they stop at the wrong moment. The moment an agent starts doing real work — reading files, drafting outputs, shaping decisions — a more important question takes over: Who is responsible for what this agent produces? Agents that do useful work do not stay in demo territory. They become part of how daily work gets done. And when that happens, unowned agents start causing real problems in ordinary, invisible ways. What Actually Counts as an Agent? There is a lot of confusion about the word "agent," and most of it is unnecessary. The brand name does not matter. The tool does not matter. What matters is the nature of the work being done. A one-off question to an AI assistant is not an agent interaction. You ask, it answers, you decide what to do next. That is a conversation. An agent is something different. An agent has a repeated job. It reads specific sources. It follows defined rules. It produces work that you or your team actually acts on. If a system can read important context, produce outputs that influence decisions, and touch workflows other people depend on — that is close enough to an agent that it needs to be treated like one. The practical test is simple: Is this system doing work, or just answering questions? If it is doing work, someone needs to own that work. The Four Things Every Agent Needs Once you have an agent doing real work, there are four things it needs to function well over time.

https://link.excalidraw.com/l/1rjiAc1l3pr/Az6YBFbMK8U https://docs.google.com/document/d/1Y6xcAcR-09JuFAeix9bfyZy_QIQKq8pzXX4kJLC76R8/ https://docs.google.com/document/d/1IHxERFii5iD6uOACrAg33isa-3Ja3ciS2Nt2M8u-3fo https://docs.google.com/document/d/16VkleZI56MVxXj-LbqCQ33xAkZrl-JYvrnpNh50SKi4/ Upcoming Bootcamp https://maven.com/marily-nika/ai-agent-certification