What Are Agent Evals? Why Testing AI Agents Is Harder Than Models

AI is undergoing a fundamental shift.

We are moving from systems that generate outputs to systems that take actions. From chatbots that respond, to agents that reason, decide, and execute workflows across tools, data, and environments.

This evolution unlocks massive potential—but it also introduces a new challenge:

How do you know if an AI agent is actually doing the right thing?

In traditional software, testing is deterministic.
In traditional AI, evaluation is output-based.

But with AI agents, neither approach is enough.

This is where Agent Evaluations (Agent Evals) come in—a critical, yet still emerging discipline that sits at the heart of building reliable, trustworthy AI products.

The Shift: From Outputs to Behaviours

To understand why Agent Evals matter, we need to understand what changed.

Traditional AI (Model-Centric)

• Input → Output

• Evaluate correctness (accuracy, precision, recall)

• Static benchmarks

Agentic AI (Behavior-Centric)

• Input → Reasoning → Actions → Outcomes

• Multi-step workflows

• Interaction with tools and systems

The shift is subtle but profound:

You are no longer evaluating answers—you are evaluating behavior over time.

This introduces complexity that traditional evaluation methods simply cannot handle.

What Are Agent Evals?

Agent Evaluations are the processes, frameworks, and metrics used to assess how effectively an AI agent performs tasks, makes decisions, and operates across workflows.

They go beyond simple correctness and focus on:

• Task completion

• Decision-making quality

• Safety and compliance

• Consistency over time

• User trust and experience

Instead of asking:

• “Was the answer correct?”

We now ask:

• “Did the agent achieve the intended outcome?”

• “Did it follow the right steps?”

• “Did it behave safely and responsibly?”

• “Would a user trust it to do this again?”

Why Agent Evals Are Fundamentally Hard

1. Multi-Step Workflows

AI agents rarely operate in a single step.
They:

• Break down tasks

• Call tools

• Iterate decisions

• Adjust based on feedback

A single failure in any step can cascade into a failed outcome.

This makes evaluation combinatorially complex.

2. Non-Deterministic Behavior

Unlike traditional systems, AI agents:

• May produce different outputs for the same input

• May take different paths to reach the same outcome

This makes reproducibility difficult.

You’re not testing one behavior—you’re testing a range of possible behaviors.

3. Context & Memory Dependence

Agents rely on:

• Historical context

• Session memory

• External data sources

Evaluation must consider state, not just input/output.

A correct decision in isolation may be wrong in context.

4. Real-World Variability

Agents interact with:

• APIs

• Changing data

• Human inputs

• External systems

Static test cases are not enough.

You need dynamic, scenario-driven evaluation environments.

5. Subjective Quality

Many agent tasks involve:

• Judgment

• Prioritization

• Trade-offs

There isn’t always a “right answer.”

This introduces the need for:

• Human evaluation

• Heuristic scoring

• Context-aware metrics

Rethinking Metrics: What Should You Measure?

For Product Managers, this is the most critical shift.

Traditional AI metrics focus on:

• Accuracy

• Precision

• Recall

But for agents, these are insufficient.

✔ Task Success Rate

Did the agent achieve the intended outcome?

This is your primary success metric.

✔ Decision Quality

Were the agent’s choices logical, relevant, and appropriate?

Not just what it did—but how it decided.

✔ Reliability / Consistency

Does the agent behave predictably across repeated runs?

Consistency builds trust.

✔ Safety & Constraint Adherence

Did the agent respect boundaries and avoid harmful actions?

This ties directly to AI safety and governance.

✔ Latency & Efficiency

How long did it take to complete the task?
How many steps were involved?

Efficiency impacts user experience and cost.

✔ Human Intervention Rate

How often did a human need to step in?

This is a powerful maturity metric:

• High intervention → Low autonomy

• Low intervention → Higher trust

✔ Recovery & Resilience

When the agent fails, can it recover?

This is often overlooked but critical in real-world systems.

Types of Agent Evals

When building AI agents, a single evaluation method is not enough.
You need a combination of evaluation types to truly understand performance, reliability, and real-world behavior.

Here are the four core types of Agent Evals:

1. Offline Evals (Pre-Deployment)

Evaluation using predefined test cases and simulated scenarios, before the agent is released to users.

• Controlled test scenarios

• Simulated workflows

• Benchmark datasets

👉 Useful for:

• Early validation

• Regression testing

1. Online Evals (Production)

Evaluation based on real user interactions in production.

• Real user interactions

• Live system monitoring

• Continuous feedback loops

👉 Essential for:

• Real-world validation

• Detecting drift and failures

2. Human-in-the-Loop Evals

Humans review, rate, or validate agent behavior and decisions.

• Experts review agent behavior

• Rate decision quality

• Provide feedback

👉 Critical for:

• Subjective tasks

• High-risk workflows

1. Automated Evals (LLM-as-a-Judge)

Using AI models to evaluate other AI outputs or behaviors.

• AI systems evaluate other AI outputs

👉 Benefits:

• Scalable

• Fast

👉 Risks:

• Bias

• Overconfidence

👉 Requires calibration and oversight.

The real value comes from combining all four types:

The Product Manager’s Role in Agent Evals

Agent Evals are not just an engineering concern—they are a core product responsibility.

As a PM, you must:

1. Define “What Good Looks Like”

• What does success mean for the user?

• What outcomes matter most?

2. Align Metrics to User Value

• Avoid over-indexing on technical metrics

• Focus on business and user outcomes

3. Design for Measurability

• Build systems that generate evaluation signals

• Ensure observability from day one

4. Balance Automation vs Control

• Decide when to allow autonomy

• Define escalation and intervention points

5. Drive Continuous Improvement

• Treat evaluation as an ongoing process

• Feed insights back into product and model improvements

From Evaluation to Trust

There is a direct relationship:

• Strong Evals → Reliable Behavior

• Reliable Behavior → User Confidence

• User Confidence → Adoption

Agent Evals are the foundation of Agent Trust

Without them:

• You cannot validate performance

• You cannot guarantee reliability

• You cannot scale adoption

Final Thoughts

Agentic AI represents a new paradigm—not just in capability, but in responsibility.

The hardest problem is no longer building intelligence.

👉 It is ensuring that intelligence behaves reliably in the real world.

Agent Evals sit at the center of this challenge.

Because ultimately, success is not defined by:

• What the AI can do

But by:

• What the AI can do consistently, safely, and at scale