The reason AI agents are getting dramatically better? You're giving them more time to think. Here's what that means for how you work with AI — and how to get the most out of it. Why are the latest AI agents so much more capable than earlier versions? The answer isn't just better models — it's time. Modern AI agents are designed to reason, plan, and iterate before delivering a response. The more time and computational resources (tokens) you give an agent, the more sophisticated its output becomes. This video breaks down why that tradeoff exists, what it means for how you use AI today, and how to actively guide agents toward better results. The human analogy that explains everything Think about how you'd respond if someone asked you a question and expected an immediate answer versus giving you time to research, think it through, and come back with a well-developed response. The second answer is almost always better. AI agents work the same way. When you allow an agent to take its time — to make a plan, consider options, and reason through a problem — you get proportionally better output. The quality scales with the time and tokens you allow. What "slow" AI actually signals Early AI chat interfaces trained users to expect near-instant responses. Waiting even a few seconds felt like a failure. But that expectation was built around a simpler generation of tools. Today's most capable agents may take seconds, minutes, or in some cases hours or days to complete a task — and that latency is a signal of depth, not a bug. The newest agents are doing more: planning, searching, verifying, and iterating before they surface a result. How to get more from your agents The best modern agents give you controls to guide this process. You can explicitly instruct an agent to "think really hard about this," "take your time," or "make a plan before responding." When you use these controls, the output takes longer — but the quality improvement is proportional. Understanding this tradeoff is one of the most practical things you can do to get more value from AI tools right now. FAQs Q: Why do newer AI agents take so much longer to respond than earlier AI tools? A: Because they're doing more work. Modern agents are designed to reason, plan, and iterate — which takes more time and computational resources (tokens). The latency is a direct result of the depth of processing, not a performance issue. Q: Does giving an AI agent more time always produce better results? A: According to the transcript, you usually get a proportionally better response the more time and tokens you allow the agent to use. The relationship isn't guaranteed, but the pattern holds across the newest generation of agents. Q: What are "tokens" and why do they matter for AI agent quality? A: Tokens are the units of computation an AI model uses to process and generate text. The more tokens an agent is allowed to use, the more it can reason through a problem before responding — which typically improves output quality. Q: How can I actively get better results from an AI agent? A: The transcript recommends using the controls and options that good agents provide — explicitly instructing the agent to "think really hard about this," "take your time," or "make a plan." These prompts signal to the agent that depth is expected, not speed. Q: Is this relevant to enterprise AI deployments, or just consumer AI tools? A: The principle applies to both, but it's especially relevant in enterprise contexts where agents are being used for complex, multi-step tasks — contract review, research synthesis, workflow automation — where output quality directly affects business outcomes. See Box AI in Action: https://www.youtube.com/playlist?list=PLCSEWOlbcUyLFnppTnftEU251AuwphRFi

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