āĻāĻ āĻāĻĒāĻŋāϏā§āĻĄ āϏāĻŽā§āĻŦāύā§āϧā§
Most AI practitioners build an agent, use it for a few weeks, and then watch it quietly get worse. In this episode of đđļđŋđŋđļđđ đ§đŽđšđ¸: đđ´đ˛đģđđļđ° đđ, đ§đļđē đđŽđŋđđļđģđ´ and đđŽđđļđģ đđŋđŽđģđ¸đšđļđģ of đđļđŋđŋđļđđ đĻđŧđšđđđļđŧđģđ break down the four pillars of a true agentic operating system and explain what it takes to keep your agent useful for months or years, not just days.
đĒđĩđŽđ đđŧđâđšđš đšđ˛đŽđŋđģ:
- The difference between an agent harness and a true agentic OS, and why most deployments only have the former
- The three tiers of agent memory, and why the ChatGPT memory toggle is just one small slice
- The four core pillars: signal layers, context flow, memory management, and model portability
đĒđĩđŧ đļđâđ đŗđŧđŋ:
For engineers, architects, and technical leaders moving past their first agent prototype and asking what it actually takes to run one sustainably for months or years. Also valuable for team leads evaluating a company-wide agent deployment.
đđđ˛đđ + đ°đŋđ˛đąđļđ¯đļđšđļđđ:
đ§đļđē đđŽđŋđđļđģđ´ and đđŽđđļđģ đđŋđŽđģđ¸đšđļđģ are members of the đđļđŋđŋđļđđ đĻđŧđšđđđļđŧđģđ engineering and AI practice. đ§đļđē đđŽđŋđđļđģđ´ has been running his personal agentic OS in daily production for over six months; đđŽđđļđģ đđŋđŽđģđ¸đšđļđģ leads the technical architecture of đđļđŋđŋđļđđ đĻđŧđšđđđļđŧđģđâ internal agent deployment.
đŖđŋđļđēđŽđŋđ đđ§đ:
Follow đđļđŋđŋđļđđ đ§đŽđšđ¸: đđ´đ˛đģđđļđ° đđ for the next episodes in this series as we go deep on each of the four pillars.
đĨđ˛đ°đŧđēđēđ˛đģđąđ˛đą đđļđģđ¸đ & đĨđ˛đđŧđđŋđ°đ˛đ
Any Questions: Reach out to us at CirriusTalk@cirriussolutions.com
#AgenticAI #AIAgents #AgentOS #ContextEngineering #LLM #ArtificialIntelligence #CirriusTalk