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Agentic AI
How AI will stop being impressive and start being useful with agentic workflows, continual learning, efficient reasoning, world models, and architectural innovation.
7 min readJust now
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Illustration created by Gemini
TL;DR
AI progress in 2026 is no longer driven by bigger models or better demos; it is driven by constraint. As agents move from reasoning to execution, real-world limits around cost, memory, reliability, and coordination dominate performance. Single agents fail at scale; production systems require multi-agent orchestration, persistent memory, and graceful failure recovery. Continual learning is finally breaking the retraining–forgetting loop, enabling models to adapt without lo…
Member-only story
Agentic AI
How AI will stop being impressive and start being useful with agentic workflows, continual learning, efficient reasoning, world models, and architectural innovation.
7 min readJust now
–
Press enter or click to view image in full size
Illustration created by Gemini
TL;DR
AI progress in 2026 is no longer driven by bigger models or better demos; it is driven by constraint. As agents move from reasoning to execution, real-world limits around cost, memory, reliability, and coordination dominate performance. Single agents fail at scale; production systems require multi-agent orchestration, persistent memory, and graceful failure recovery. Continual learning is finally breaking the retraining–forgetting loop, enabling models to adapt without losing prior knowledge. At the same time, world models challenge language-only systems by learning physical causality and spatial dynamics. Reasoning itself is becoming a tunable feature rather than a separate class of models, while transformers give way to hybrid architectures optimized for efficiency. The central shift is clear: the AI systems that win in 2026 are not the largest — they are the ones that allocate intelligence precisely under real-world constraints.