Here are 9 ways to make AI Agents faster #ai #claude #chatgpt #learnai #aiagents

Here are nine techniques to make AI agent 10 times faster. What if your AI agent could start fetching data before it even decides it needs it? That's called predict and pre-fetch. And it's just one of nine techniques that separates slow agents from production ready ones. So first technique is multi-tool speed up. Instead of calling APIs one after another, call them all at the same time. If you need stock price and news, fetch both simultaneously, instead of waiting for one to finish first. Two, branching strategies. Don't bet on one approach. Generate three different strategies in parallel. Then let a judge pick the best one. More exploration means better answers. Three, multi-critic review. Send your content to multiple specialist critics at once. One checks fax, one checks tone, one checks risk. Then a final editor combines all feedback. Four, predict and pre-fetch. While your LLM is still thinking, start the tool call you predict it will need. If you're right, the data is already ready. You just hid three seconds of waiting. Five, manager worker teams. One manager agent breaks the bid task into smaller pieces. Assigns each to specialist workers who run in parallel. Then combines their results. Six, agent competition. Run three different agents on the same task. Different models, different prompts. A judge picks the winner. Diversity reduces risk of bad outputs. Seven, pipeline processing. Like a factory line. While agent one processes item two. Agent two processes item one. Maximizes throughput when you have many items to process. Eight, shared workspace. Agents watch a central blackboard. When they see something matching their expertise, they activate themselves and contribute. No rigid workflow needed. Nine, backup agents. Run identical copies of the same agent. First one to succeed wins. Cancel the rest. Protects against random failures and slow responses. Follow to stay ahead in agent AI.