11 min readJust now
–
The Problem That’s Been Quietly Killing AI Agents
Imagine hiring an assistant who needs to read a 500-page instruction manual before making a simple phone call. That’s essentially what’s been happening with AI agents — and it’s been draining budgets, slowing responses, and limiting what these agents can actually do.
Here’s the kicker: Anthropic just figured out how to fix it. And we’re not talking about a 10% improvement. We’re talking about workflows that used to consume 150,000 tokens now running on just 2,000 tokens. That’s a 98.7% reduction. 🚀
If you’re building AI agents (or thinking about it), this changes everything. Let’s dive into why this matters and how you can use it.
🎯 What’s Actually Going On Here?
The Traditional Appr…
11 min readJust now
–
The Problem That’s Been Quietly Killing AI Agents
Imagine hiring an assistant who needs to read a 500-page instruction manual before making a simple phone call. That’s essentially what’s been happening with AI agents — and it’s been draining budgets, slowing responses, and limiting what these agents can actually do.
Here’s the kicker: Anthropic just figured out how to fix it. And we’re not talking about a 10% improvement. We’re talking about workflows that used to consume 150,000 tokens now running on just 2,000 tokens. That’s a 98.7% reduction. 🚀
If you’re building AI agents (or thinking about it), this changes everything. Let’s dive into why this matters and how you can use it.
🎯 What’s Actually Going On Here?
The Traditional Approach (And Why It’s Broken)
When you build an AI agent with multiple tools, here’s what typically happens:
- Tool Definition Overload: Every single tool your agent might use gets loaded into its context window upfront
- Token Tsunami: Each tool needs its description, parameters, expected formats, and return values documented
- Intermediate Result Hell: Every time your agent chains tools together, each result passes through the context window