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AI agents that manage their context window well outperform those with 10x more tools.
The secret isn't more capabilities -- it's smarter context engineering.
Thread: What makes AI agents actually work in production? 🧵
1/ It's not the model size. It's context management.
The best agents treat their context window like RAM -- offloading to filesystem, summarizing aggressively, loading info on demand.
2/ Subagents are the key to scaling.
Instead of one agent doing everything, delegate to specialists. The main agent only sees the summary, not 50 intermediate tool calls.
3/ Skills > giant system prompts.
Progressive disclosure: load detailed instructions only when the task needs them. Your agent's prompt stays clean until it matters.
4/ Memory needs structure.
Semantic (facts), episodic (experiences), procedural (rules) -- route them to different backends so they persist appropriately.
5/ The takeaway: the best agent architectures are about information flow, not raw capability.
What patterns are you using? Reply with your favorite agent architecture trick.