# Demystifying Evals for AI Agents

> Source: Anthropic Engineering\
> Article: *Demystifying evals for AI agents*\
> Date: **January 09, 2026** URL: [demystifying-evals-for-ai-agents](https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents)

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![demisify ai evals infographic](/files/sSW5lHwQ8Md31tDJob8I)

## 1. What is an Eval?

An **evaluation (eval)** is a structured test where an AI agent is given a task and is **graded on whether it succeeds**.

**Core idea:** Eval = Task + Graders + Outcome

Evals help turn vague notions of “agent quality” into **measurable, repeatable signals**.

***

## 2. Why Agent Evals Are Hard

Traditional single-turn benchmarks fail for agents because agents:

* Operate over **multiple steps**
* Use **tools** and external systems
* Maintain **state and memory**
* Adapt strategies mid-task

As a result, evaluation must consider **entire interaction transcripts**, not just final answers.

***

## 3. Eval Components

Every agent eval is composed of the following building blocks:

### 3.1 Task

* A clearly defined goal for the agent
* Must be **unambiguous** and realistic

### 3.2 Trial

* One execution attempt of the task
* Often run multiple times to measure consistency

### 3.3 Grader

* Determines whether the agent succeeded
* Can be:
  * Deterministic (rules, scripts)
  * LLM-based (judgment calls)
  * Human (for edge cases)

### 3.4 Transcript

* Full record of the agent’s actions:
  * Prompts
  * Tool calls
  * Intermediate reasoning
  * Outputs

### 3.5 Outcome

* Pass / fail
* Or a scalar score

***

## 4. Types of Evals

### 4.1 Capability Evals — “Can it do it?”

* Measure **what the agent is capable of**
* Used early in development
* Often have **low initial pass rates**

Examples:

* Can the agent write correct code?
* Can it complete a multi-step research task?

***

### 4.2 Regression Evals — “Does it still do it?”

* Ensure previously working behaviors **do not break**
* Used continuously during iteration
* Focus on **stability and reliability**

Key role: **catch regressions before users do**

***

## 5. Metrics Explained

### 5.1 pass\@k

> Probability that the agent succeeds **at least once** in `k` attempts

* Measures *best-case capability*
* Useful when retries are allowed

Example:

* Did the agent solve the task in any of 5 tries?

***

### 5.2 pass^k

> Probability that the agent succeeds **every time** across `k` attempts

* Measures **consistency**
* Critical for production reliability

Example:

* Did the agent succeed in all 5 runs?

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## 6. Agent Types and Evaluation Focus

Different agents require different eval designs:

### Coding Agents

* Correctness
* Edge cases
* Determinism

### Conversational Agents

* Coherence
* Goal completion
* User satisfaction

### Research Agents

* Information accuracy
* Source usage
* Reasoning quality

### Computer-Use Agents

* Correct UI actions
* Error recovery
* End-to-end task completion

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## 7. Eval Best Practices

* Write **clear and unambiguous tasks**
* Use **balanced problem sets**
* Avoid overly brittle graders
* **Review transcripts regularly**
* Combine evals with:
  * Production monitoring
  * A/B testing
  * User feedback

> Automated evals are powerful, but they never replace human judgment — they **augment it**.

***

## 8. Big Picture Takeaway

Evals are not just tests — they are **core infrastructure** for building AI agents.

Well-designed evals:

* Accelerate development
* Prevent regressions
* Enable confident iteration
* Make agent behavior measurable and explainable

**No evals → no reliable agents.**

![demisfy-ai-evals-note](/files/XLIB5krz0CAkb5okBQHA)

![demisfy-ai-evals](/files/Gu8azx9qzTg0fmbMAsvt)


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