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Tokenmaxxing: Pushing AI Agents to Token Limits

Source: nytimes.com

Developers obsess over tokenmaxxing to squeeze every drop of efficiency from AI agents amid skyrocketing costs.
Multi-agent systems explode token usage - like jumping from 13 to 1005 input tokens in one example - demanding smart design tweaks.
Tricks like prompt compression and output limits keep proactive agent swarms viable without bankrupting users.

AI agents are getting smarter and more autonomous, but they guzzle tokens - the basic text chunks LLMs process - driving up costs and hitting context limits fast. The piece dives into "tokenmaxxing," the push to optimize every token in multi-agent setups where bots chat, decide, and act together. It matters because as agents scale to handle real work like trading or analysis, unchecked token bloat could kill adoption.