If you’ve ever used ChatGPT, you know it types an answer back. It’s a powerful tool for generating text, summarizing documents, or brainstorming ideas. But that’s mostly talking.

A newer kind of AI does things instead of just talking. These are called AI agents.

Imagine an AI that doesn’t just tell you how to book a flight, but actually goes online, compares prices across different airlines, selects the best option based on your preferences, and completes the booking for you. That’s an AI agent in action. It breaks down a goal, decides on steps, and uses tools to achieve them.

Many businesses are looking at these agents to automate complex tasks: handling customer support, analyzing market data, even writing code. The promise is huge: faster operations, lower costs, and entirely new capabilities.

But there’s a catch.

Why is figuring out if an AI agent actually works so expensive?

Here’s the thing: evaluating these agents is incredibly difficult and costly. To truly know if an AI agent can, say, identify a software bug and write a fix, you can’t just ask it a simple question. You have to give it a real-world coding problem, let it access codebases, use developer tools, and see if it actually delivers a working solution.

Benchmarks (standardized tests used to compare different AI models) for agents, like SWE-Bench (for software engineering tasks) or GAIA (for general problem-solving), are designed to be realistic. They’re like sending a new employee into a complex, real-world project and seeing if they can deliver. This process can take days to run for a single evaluation. And it uses a lot of computing power, which means thousands of dollars per test.

Honestly, this confused me for years. How do you possibly build and improve these agents if every test costs a fortune and takes forever? It’s like trying to design a new car model, but every single design change requires you to build a full prototype and crash-test it. You’d go bankrupt before you even got close to a good design.

What most people think: You just have to run the full, expensive tests.

When you’re developing or choosing an AI agent, the common wisdom has been that there’s no shortcut. You have to put the agent through its paces on those big, complicated benchmarks to see how it performs. You need to simulate the real world, let it use its tools, and watch it try to solve the problem.

This approach gives you the most accurate picture, sure. But it slows everything down. It makes iterating on agent designs a nightmare. And for a CFO or investor, the sheer cost of development and selection becomes a massive hurdle. It feels like a necessary evil, a tax on innovation.

The smarter way: Predicting agent performance with PACE

What if you could predict how well a new car model would perform in a crash test by just looking at its blueprint, materials, and running a few small, targeted simulations? You wouldn’t need to build and crash every prototype.

That’s essentially what PACE (a Proxy for Agentic Capability Evaluation) does for AI agents. It’s a new framework introduced in a paper published on Hugging Face on July 2, 2026. PACE helps you estimate an AI agent’s performance on those massive, expensive real-world tasks using much smaller, faster, and cheaper tests.

Think of it this way: Imagine you’re hiring a chef for a high-end restaurant. Instead of making them cook a full, multi-course meal for 50 guests (the expensive, slow agent benchmark), you give them a few quick, specific challenges. “Chop these vegetables precisely in under 30 seconds.” “Prepare a perfect hollandaise sauce.” “Identify these 10 herbs by smell.”

These small, focused challenges are called “atomic evaluation instances.” They test individual, fundamental skills. PACE figures out which specific atomic instances, when combined, can reliably predict how well the chef would do on the full, complex dinner service. It’s like finding a formula: if a chef scores X on chopping and Y on sauce, they’ll likely score Z on the big meal. This formula is what researchers call a “regression” model.

The result is PACE-Bench, a curated collection of these small tests. It acts as a stand-in, a proxy, for the full, expensive benchmarks.

1 Identify complex age... 2 Select atomic evalua... 3 Build regression model 4 Predict agent perfor... *The PACE framework first identifies a complex agent task, then selects atomic evaluation instances, builds a regression model, and finally predicts agent performance.*

What this means for your business

This is where it gets interesting for decision-makers.

  1. Massive Cost Savings: The paper found that PACE-Bench can predict agentic scores at less than 1% of the full evaluation cost. Think about that: you can get reliable performance estimates for pennies on the dollar. This frees up budget for more development, more experimentation, or other strategic investments.

  2. Accelerated Development and Deployment: Instead of waiting days for an agent to complete a full benchmark, you can get a strong prediction in minutes or hours. This means your teams can test new ideas, refine agent behaviors, and select the best models much, much faster. Development cycles shrink dramatically.

  3. Smarter Investment Decisions: When choosing between different AI agents or large language models (LLMs, the “brain” of an agent, like OpenAI’s GPT-4) to power your agents, PACE gives you a quick, data-driven way to compare them. You can confidently select the agent that’s most likely to succeed in your specific business context, without the guesswork.

  4. Deeper Understanding of Agent Skills: PACE also helps reveal which specific skills are most crucial for success on different complex tasks. For example, it might show that for a coding agent, logical reasoning skills are more predictive than pure code generation speed. This insight helps guide future AI development.

The research showed some really compelling numbers. Across 14 different AI models and 4 major agent benchmarks, PACE-Bench predicted scores with a mean absolute error (MAE) under 4%. This means its predictions were typically off by less than 4 percentage points from the true score. It also achieved a Spearman correlation (a measure of how well two sets of data move together) above 0.80, and a pairwise model-ranking accuracy (how often it correctly ranked one model as better than another) around 85%.

These are strong indicators of reliability for a prediction tool.

100% 66% 33% 0% 85% Ranking Accuracy 80% Spearman Correlation 4% Mean Absolute Error PACE-Bench predictio... *PACE-Bench achieved a pairwise model-ranking accuracy of around 85%, a Spearman correlation above 0.80, and a mean absolute error under 4% across various models and agentic benchmarks.*

The caveat: It’s still a proxy

Here’s the thing: PACE is powerful, but it’s a proxy. It provides a highly reliable estimate, not a perfect, 100% real-world simulation. For truly mission-critical applications where failure is not an option (think medical diagnostics or financial trading), you will still want to perform some full, expensive evaluations.

But you won’t need to do them constantly. You can use PACE-Bench to quickly narrow down your options, iterate on designs, and make 90% of your decisions. Then, only the most promising candidates get the full, rigorous, and costly final validation. This is a massive shift in how AI agent development can be managed.

This is a big deal for anyone investing in or deploying AI agents. It means you can move faster, spend less, and make smarter bets on the AI technologies that will drive your business forward. It’s about getting reliable answers without the prohibitive price tag.

You can read the original research paper on Hugging Face: PACE: A Proxy for Agentic Capability Evaluation.