arize ai

Did science fiction just quietly become our everyday tech reality? Because just a few years ago, the idea of machines that think, plan, and act like humans felt like something straight from the pages of Asimov or a scene from Westworld. This used to be futuristic fiction!

However, with AI agents, this advanced machine intelligence is slowly turning into a reality. These AI agents use memory, make decisions, switch roles, and even collaborate with other agents to get things done.

But here’s the twist: as these agents become more capable, evaluating them has become much harder.

Traditional LLM evaluation metrics do not capture the nuance of an agent’s behavior or reasoning path. We need new ways to trace, debug, and measure performance, because building smarter agents means understanding them at a much deeper level.

The answer to this dilemma is Arize AI, the team leading the charge on ML observability and evaluation in production. Known for their open-source tool Arize Phoenix, they are helping AI teams unlock visibility into how their agents really work, spotting breakdowns, tracing decision-making, and refining agent behavior in real time.

 

 

To help understand this fast-moving space, we have partnered with Arize AI on a special three-part community series focused on evaluating AI agents. In this blog, we will walk you through the highlights of the series that focuses on real-world examples, hands-on demos using Arize Pheonix, and practical techniques to build your AI agents.

Let’s dive in.

Part 1: What is an AI Agent?

The series starts off with an introduction to AI agents – systems that can take actions to achieve specific goals. It does not just generate text or predictions, but interacts with its environment, makes decisions, uses tools, and adjusts its behavior based on what is happening around it.

Thus, while most AI models are passive – relying on a prompt to generate a response, agents are active. They are built to think a few steps ahead, handle multiple tasks, and work toward an outcome. This is the key difference between an AI model and an agent. One answers a question, and the other figures out how to solve a problem.

For an AI agent to function like a goal-oriented system, it needs more than just a language model. It needs structure and components that allow it to remember, think ahead, interact with tools, and sometimes even work as part of a team.

 

 

Its key building blocks include:

• Memory

It allows agents to remember what has happened so far, like previous steps, conversations, or tool outputs. This is crucial for maintaining context across a multi-step process. For example, if an agent is helping you plan a trip, it needs to recall your budget, destination preferences, and dates from earlier in the conversation.

Some agents use short-term memory that lasts only during a single session, while others have long-term memory that lets them learn from past experiences over time. Without this, agents would start from scratch every time they are asked for help.

• Planning

Planning enables an agent to take a big, messy goal and break it down into clear, achievable steps. For instance, if you ask your agent to ‘book you a vacation’, it will break down the plan into smaller chunks like ‘search flights’, ‘compare hotels’, and ‘finalize the itinerary’.

In more advanced agents, planning can involve decision trees, prioritization strategies, or even the use of dedicated planning tools. It helps the agent reason about the future and make informed choices about what to do next, rather than just reacting to each prompt in isolation.

• Tool Use

Tool use is like giving your agent access to a toolbox. Need to do some math? It can use a calculator. Need to search the web? It can query a search engine. Want to pull real-time data? It can call an API.

 

Here’s a guide to understanding APIs

 

Instead of being limited to what is stored in its training data, an agent with tool access can tap into external resources and take actions in the real world. It enables these agents to handle much more complex, dynamic tasks than a standard LLM.

• Role Specialization

This works mostly in a multi-agent system where agents start dividing tasks into specialized roles. For instance, a typical multi-agent system has:

• A researcher agent that finds information
• A planner agent that decides on the steps to take
• An executor agent that performs each step

Even within a single agent, role specialization can help break up internal functions, making the agent more organized and efficient. This improves scalability and makes it easier to track each stage of a task. It is particularly useful in complex workflows.

 

 

Common Architectural Patterns

Different agent architectures offer different strengths, and the right choice depends on the task you’re trying to solve. Let’s break down four of the most common patterns you will come across:

Router-Tool Pattern

In this setup, the agent listens to the task, figures out what is needed, and sends it to the right tool. Whether it is translating text, fetching data, or generating a chart, the agent does not do the work itself. It just knows which tool to call and when. This makes it super lightweight, modular, and ideal for workflows that need multiple specialized tools.

ReAct Pattern (Reason + Act)

The ReAct pattern enables an agent to alternate between thinking and acting, step by step. The agent observes, reasons about what to do next, takes an action, and then re-evaluates based on what happened. This loop helps the agent stay adaptable in real time, especially in unpredictable or complex environments where fixed plans can’t work.

Hierarchical Pattern

Hierarchical pattern resembles a company structure: a top-level agent breaks a big task into smaller ones and hands them off to lower-level agents. Each agent has its own role and responsibility, making the system modular and easy to scale. Thus, it is useful for complex tasks that involve multiple stages or specialized skills.

Swarm-Based Pattern

Swarm-based architectures rely on lots of simple agents working in parallel without a central leader. Each agent does its own thing, but together they move toward a shared goal. This makes the system highly scalable, robust, and great for solving problems like simulations, search, or distributed decision-making.

These foundational ideas – what agents are, how they work, and how they are architected – set the stage for everything else in the world of agentic AI. Understanding them is the first step toward building more capable systems that go beyond just generating answers.

Curious to see how all these pieces come together in practice? Part 1 of the webinar series, in partnership with Arize AI, walks you through real-world examples, design patterns, and live demos that bring these concepts to life. Whether you are just starting to explore AI agents or looking to improve the ones you are already building, this session is for you.

 

 

Part 2: How Do You Evaluate Agents?

Now that we understand how an AI agent is different from a standard model, we must explore the way these features impact the evaluation of these agentic models. In Part 2 of our series with Arize AI, we will cover these conversations on transitioning evaluation techniques in detail.

Traditional metrics like BLEU and ROUGE are designed for static tasks that involve a single prompt and output. Agentic systems, however, operate like workflows or decision trees that can reason, act, observe, and repeat. There are unique challenges associated when evaluating such agents.

 

You can also read in detail about LLM evaluation and its importance

 

Some key challenges to evaluating AI agents include:

• Planning is more than one step.

Agents usually break a big task into a series of smaller steps, making evaluation tricky. Do you judge them based on each step, the final result, or the overall strategy? A smart plan can still fail in execution, and sometimes a sloppy plan gets lucky. Hence, you must also evaluate how the agent reasons, and not just the outcome.

• Tool use adds a layer of complexity.

Many agents rely on external tools like APIs or search engines to complete tasks. In addition to internal logic, their performance also depends on how well they choose and use these tools. It makes their behavior more dynamic and sometimes unpredictable.

• They can adapt on the fly.

Unlike a static model, agents often change course based on what is happening in real time. Two runs of the same task might look totally different, and both could still be valid approaches. Given all these complexities of agent behavior, we need more thoughtful ways to evaluate how well they are actually performing.

Core Evaluation Techniques for AI Agents

As we move the conversation beyond evaluation challenges, let’s explore some key evaluation techniques that can work well for agentic systems.

Code-Based Evaluations

Sometimes, the best way to evaluate an agent is by observing what it does, not just what it says. Code-based evaluations involve checking how well the agent executes a task through logs, outputs, and interactions with tools or APIs. These tests are useful to validate multi-step processes or sequences that go beyond simple responses.

LLM-Driven Assessments

You can also use language models to evaluate agents. And yes, it means you are using agents to judge agents! These assessments involve prompting a separate model (or even the same one in eval mode) to review the agent’s output and reasoning. It is fast, scalable, and helpful for subjective qualities like coherence, helpfulness, or reasoning.

 

 

Human Feedback and Labeling

This involves human evaluators who can catch subtle issues that models might miss, like whether an agent’s plan makes sense, if it used tools appropriately, or if the overall result feels useful. While slower and more resource-intensive, this method brings a lot of depth to the evaluation process.

Ground Truth Comparisons

This works when there is a clear correct answer since you can directly compare the agent’s output against a ground truth. This is the most straightforward form of evaluation, but it only works when there is a fixed ‘right’ answer to check against.

Thus, evaluating AI agents is not just about checking if the final answer is ‘right’ or ‘wrong.’ These systems are dynamic, interactive, and often unpredictable, so we must evaluate how they think, what they do, and why they made the choices they did.

 

Learn about Reinforcement Learning from Human Feedback for AI applications

 

While each technique offers valuable insights, no single method is enough on its own. Choosing the right evaluation approach often depends on the task. You can begin by answering questions like:

• Is there a clear, correct answer? Ground truth comparisons work well.
• Is the reasoning or planning complex? You might need LLM or human review.
• Does the agent use tools or external APIs? Code-level inspection is key.
• Do you care about adaptability and decision-making? Consider combining methods for a more holistic view.

As agents grow more capable, our evaluation methods must evolve too. If you want to understand how to truly measure agent performance, Part 2 of the series, partnered with Arize AI, walks through all of these ideas in more detail.

 

 

Part 3: Can Agents Evaluate Themselves?

In Part 3 of this webinar series with Arize AI, we look at a deeper side of agent evaluation. It is not just about what the agent says but also about how it gets there. With tasks becoming increasingly complex, we need to understand their reasoning, not just their answers.

Evaluating the reasoning path allows us to trace the logic behind each action, understand decision-making quality, and detect where things might go wrong. Did the agent follow a coherent plan? Did it retrieve the right context or use the best tool for the job? These insights reveal far more than a simple pass/fail output ever could.

Advanced Evaluation Techniques

To understand how an agent thinks, we need to look beyond just the final output. Hence, we need to rely on advanced evaluation techniques. These help us dig deeper into the agent’s decision-making process and see how well it handles each step of a task.

Below are some common techniques to evaluate reasoning:

Path-Based Reasoning Analysis

Path-based reasoning analysis helps us understand the steps an agent takes to complete a task. Instead of just looking at the final answer, it follows the full chain of thought. This might include the agent’s planning, the tools it used, the information it retrieved, and how each step led to the next.

This is important because agents can sometimes land on the right answer for the wrong reasons. Maybe they guessed, or followed an unrelated path that just happened to work out. By analyzing the path, we can see whether the reasoning was solid or needs improvement. It also helps debug errors more easily since we can pinpoint exactly where things went off track.

Convergence Measurement

Convergence measurement is all about tracking progress. It figures out if the agent is getting closer to solving the problem or just spinning in circles. As the agent works step by step, we want to see signs that it is narrowing in on the goal. This is especially useful for multi-step or open-ended tasks.

It shows whether the agent is truly making progress or getting lost along the way. If the agent keeps making similar mistakes or bouncing between unrelated ideas, convergence measurement helps catch that early. It is a great way to assess focus and direction.

Planning Quality Assessment

Before agents act, many of them generate a plan. Planning quality assessment looks at how good that plan actually is. Is it clear? Does it break the task into manageable steps? Does it show a logical structure? A good plan gives the agent a strong foundation to work from and increases the chances of success.

This method is helpful when agents are handling complex or unfamiliar tasks. Poor planning often leads to confusion, delays, or wrong results. If the agent has a solid plan but still fails, we can look at execution. But if the plan itself is weak, that tells us where to focus our improvements.

Together, these methods give us a more complete picture of an agent’s thinking process. They help us go beyond accuracy and understand how well the agent is reasoning.

 

 

Agent-as-Judge Paradigm

As agents become more advanced, they are starting to judge how well those tasks are done. This idea is known as the Agent-as-Judge Paradigm. It means agents can evaluate their own work or the work of other agents, much like a human reviewer would.

Let’s take a deeper look at the agent-as-judge paradigm:

Self-Evaluation and Peer Review

In self-evaluation, an agent takes a step back and reviews its own reasoning or output. It might ask: Did I follow the right steps? Did I miss anything? Was my answer clear and accurate? This reflection helps the agent learn from its own mistakes and improve over time.

Peer review works a little differently. Here, one agent reviews the work of another. It might give feedback, point out errors, or suggest better approaches. This kind of agent-to-agent feedback creates a system where multiple agents can help each other grow and perform better.

Critiquing and Improving Together

When agents critique each other, they are not just pointing out what went wrong, but also offering ways to improve. This back-and-forth exchange helps strengthen their reasoning, decision-making, and planning. Over time, it leads to more reliable and effective agents.

These critiques can be simple or complex. An agent might flag a weak argument, suggest a better tool, or recommend a clearer explanation. When executed well, this process boosts overall quality and encourages teamwork, even in fully automated systems.

Feedback Loops and Internal Tools

To support this, agents need tools that help them give and receive feedback. These can include rating systems, critique templates, or reasoning checklists. Some systems even build in internal feedback loops, where agents automatically reflect on their outputs before moving on.

 

Here’s a comparison of RLHF and DPO in fine-tuning LLMs

 

These tools make self-review and peer evaluation more structured and useful. They create space for reflection, correction, and learning, without the need for human involvement every time.

Thus, as agents grow more capable, evaluating how they think becomes just as important as what they produce. From tracing reasoning paths to building internal feedback loops, these techniques give us deeper insights into agent behavior, planning, and collaboration.

In Part 3 of this series, we dive into all of this in more detail, showing how modern agents can reflect, critique, and improve not just individually, but as part of a smarter system. Explore the last part of our series if you want to see how self-aware agents are changing the game.

 

 

Wrapping It Up: The Future of AI Agents Starts Now

AI agents are evolving, from being task-driven systems to ones capable of deep reasoning, collaboration, and even self-evaluation. This rapid technological advancement also raises the need for more sophisticated ways to measure and improve agent performance.

If you are excited about the possibilities of these smart systems and want to dive deeper, do not miss out on our webinar series in partnership with Arize AI. With real-world examples, live demos, and valuable insights, we will help you build better agents. Explore the series now and take your understanding of agentic AI to the next level!