The agent that does not forget you when the tab closes Most chat agents are tourists. They answer your question, the conversation ends, and everything they learned about you evaporates. The next session starts from zero: same introductions, same context
A pattern essay. The follow-up to The Reflexive Advisor Team — lifting the general shape out of the specific net, so you can see Agentic-Nets as a substrate for designing teams of personas, not just clever pipelines. From one agent
A walk-through of building a six-net, self-learning life-advisor system on AgenticNetOS where the agents reflect, talk to each other on a schedule, crystallize shared insights, and fuse multi-domain advice into one answer — all between your questions, while you’re not
What if you had an AI agent that understood your entire platform — not just one net, but all of them? An agent that could search documentation, diagnose errors, delegate repairs to specialists, and synthesize everything into actionable advice? This
When the Net Reads Its Own Results In Part 1, we built a self-healing QA net: eight command tokens fan out through an executor, results collect in a place, and a recycle loop resets everything for the next run. The
