AI agent evaluation—does this sound essentially important? No doubts about that at all. Just like when you write a paper, revisions are necessary to avoid predictable mistakes; evaluating conversational AI agents ensures you flag predictable errors and correct them before you call them a production-ready tool. 

As is with AI Agents, they are adept at handling conversations independently and solving problems without human intervention. Here, you need to be proactive about checking if they can understand contexts for multi-step queries. Each step of the interactions contains nuances, which AI agents must tackle with precision. AI agent evaluation helps ensure conversational AI systems are capable and reliable for customer or employee experience management. 

We’ll explore the challenges of agent AI evaluation, best practices for defining an effective AI agent, and many other aspects. This blog will provide solid fundamentals for evaluating AI agents, which has become a norm in the AI world. 

AI agent evaluation is designed to comprehensively identify quality issues and determine the root cause of those issues. AI system assessment runs along all stages of AI workings through multiple sub-modules or AI capabilities using various metrics to analyze their performance abilities. LLM evaluations help determine that agents can enhance decision-making, elevate customer experience, and improve efficiency. 

Besides, the essential part of evaluating AI agents involves determining affordability, reliability, stability, and security beyond executing end-to-end tasks.

Let’s get straightforward—-an under-tested AI agent is the breeding ground for many issues, such as inaccurate predictions, incorrect answers, security vulnerabilities, biases, and lack of adaptability. The aim of robust AI agent evaluation is to ensure that artificial intelligence agents can adapt to unpredictable situations for questions they encounter in everyday life. 

For example, if an AI agent engages with a user regarding home loan queries, it must have the necessary knowledge to handle both the common and changing expectations of that user. 

Here’s why AI agent eval is essential:

• Performance efficiency  

If you roll out an AI agent-based solution without a robust evaluation, organizations like yours tend to deploy a product that can underperform or generate misinformation. This could probably lead to failure to capture the outstanding performance efficiency gains that an AI agent can unleash, and users may lose trust in an organization. 

• Strategic outlook 

Agentic AI is rapidly becoming sophisticated, bringing change to its existing workings and requiring new configurations. Keeping this ongoing scaling in mind, organizations must employ equally sophisticated evaluation methods to get an edge over their competitors with better AI products. 

• Regulatory needs

AI agent evaluation is also essential for building users' trust and confidence. AI agents must protect personal information when used in banking, finance, or healthcare. Evaluation ensures they are reliable enough to prevent data leakage and build user trust. 

With the sophistication of AI models, traditional evaluation methods are ineffective. They can quickly ignore the importance of security, reliability, trust, and affordability. Here are some of the best metrics you must follow as you are up for agent evaluation.

Artificial intelligence agent testing involves several crucial metrics, including topic adherence, tool calli accuracy, agent goal adherence, latency, security, and stability. Each metric plays a key role as an AI agent performance metric in business and enterprise. Let’s say you want to satisfy your customers and build a long-term relationship. To ensure that your customer-facing interface works just fine, AI agent evaluation metrics for the customer support chatbot are indeed essential. Let’s learn them elaborately.

Topic adherence for AI agent evaluation 

One of the significant AI eval metrics for AI systems is topic adherence. AI systems are expected to answer by adhering to domains of interest while interacting with users. But, AI systems sometimes inadvertently overlook this aspect and generate generic answers. That’s why you should work with topic adherence metrics to evaluate whether your AI systems align with the predefined domain specifications during interactions. Note that the topic adherence metric is essential in conversational AI systems where AI systems must adhere to domain topics. 

Tool call accuracy for AI agent evaluation 

As the name goes on, the tool call accuracy—- can easily be defined as Agentic AI’s ability to connect with third-party external systems, fetch information, and generate appropriate answers that solve problems. 

Imagine if your AI agent systems lose their fundamental precision in connecting with the right systems and generating relevant information. Their accuracy will be deemed unfit. 

Tool call accuracy is a metric for AI agent evaluation to determine the LLM’s performance in identifying and calling the right or predefined tools to execute a given task. 

The metrics for tool call accuracy are evaluated depending on the score on a scale of 0 to 1. The higher the score, the better the accuracy rate. 

Agent goal accuracy for agentic AI evaluation 

AI agents are designed to engage in conversations and supply information until they execute a task and reach their goal. Based on this concept, agent goal accuracy is a metric used to evaluate the performance of LLMs in identifying and achieving the goal for the users. While this metric is being run during the evaluation process, you can assess its performance on 1 and 0, where 1 indicates that it has attained its goal, while 0 indicates failure. 

AI Agent latency and response time evaluation 

AI agents are designed to execute complex workflows. Behind the curtain, it can refer to completing a workflow and how AI agents can adeptly plan, reflect, iterate, make decisions, and execute a tack. Evaluating AI agents needs to consider their answer delivery precision and ability to adapt to changing situations with the available data and correct its course. 

Several latency metrics go on to determine LLMs’ performance include, 

• Planning and execution latency: It involves AI agents taking time to break down a task into sub-tasks and execute them using tools and real-time data. 

• Reflection and iteration latency: Evaluating AI agents defines their time to re-evaluate steps after receiving feedback and correct mistakes. 

• Time to first response: This evaluation latency metric defines how fast AI agents can plan and execute tasks.  

• Latency throughput: The number of tasks AI agents can execute in a given time. 

AI Agent accuracy for reliable outputs 

If we ask, ‘How to assess AI accuracy?’, we would suggest it is always better to adopt the modern method, in which you evaluate beyond the ability to provide correct answers. Using a modern AI agent evaluation method, you can assess several aspects of accuracy for planning, adaptability, and execution. 

Such accuracy metrics include, 

• Task completion rate: It is a metric to evaluate the ability of AI agents to complete tasks with correct outcomes. 

• Step-level accuracy: This metric evaluates how effectively an AI agent can execute actions or sub-tasks in a larger workflow.

• Precision and recall: This is a valuable AI agent accuracy metric to evaluate if the systems are able to generate both relevant and comprehensive answers. 

• Reflection accuracy: This metric helps determine the time taken to improve execution after the first iteration.

AI Agent security evaluation 

Evaluating security controls for LLMs or AI systems is very crucial. AI agents have access to third-party systems and sensitive data. The AI agent evaluation method for security ensures that data is protected during machine-human interaction and that no data leakage happens. Some essential security evaluation metrics include, 

• Threat detection rate: This metric defines how effectively AI agents can identify and respond to malicious inputs during interaction. 

• Tool and data security rate: AI agent evaluation for tool and data security metrics helps understand that their interactions are secure when AI agents interact with third-party systems.

AI agent stability and reliability evaluation 

During the evaluation of AI agents, agent stability and reliability metrics play a key role in determining that AI agents can seamlessly handle dynamic user interactions and perform under evolving conditions. Hence, stability and reliability metrics for agent evaluation refer to the robustness of AI systems in handling context variations and error execution while improving customer experience within a workflow. 

AI agent cost optimization evaluation

When you have your AI agent-based systems for customer or employee support, it is crucial to consider cost evaluation. This can involve the total cost of ownership for infrastructure, operational efficiency, and scalability. Based on these aspects, cost optimization metrics include, 

• Infrastructure and operational costs: AI agent evaluation metrics for cost optimization define how much it costs to maintain hardware and human resources. 

• Scalability costs: As the workload becomes more complex, the agent must scale. This metric defines scalability costs for complex workloads.

It is not that AI agent testing and evaluation is pretty hard. But it has to be methodical. You can find some of the best approaches that work better for evaluating AI agents. 

Build a test case

When AI agents are implemented in the user interface for interaction, the key objective is to generate responses to user queries. Hence, working across known and unknown scenarios is essential to sort out compelling cases and gather examples for building dialogues. Aim for inputs that help you provide coverage during interactions. For example, if you want to develop an employee support chatbot with AI agent abilities, create inputs like,

• Normal query — how do I reset my password?

• Inputs for worst-case scenarios —- provide inputs for complex queries

• Queries for specific functions your agent can handle

You can monitor and make changes per the engagement pattern as you go with the bot.

Design the agent’s workflow 

Building logic for agent workflows is essential. Evaluate when to call a function based on skill or route a call to enable your agents to handle interactions efficiently. Map out a plan and handle issues better. 

Choose the right evaluation method

Decide how your agents can interact and successfully execute the task. There are two methods to apply to the AI agent evaluation process. 

• Compare outcomes with the expected output. This is where you already have a set of outputs for user inputs. So, if the agent can deliver the expected results, it is okay. But, if it does not follow the predefined logic, you must correct it.  

• When there is no proper answer to the unpredictable questions, and you want the agent to provide contextual answers, add another language model or human-in-the-loop. This allows you to get insights and refine your model. 

Focus on agent-specific challenges

Evaluate that your agents can perform as you expect. 

• Tool selection: Your agent tends to pull information from multiple systems, so ensure that they choose the right system.

• Parameter extraction: inputs can be overwhelming and confusing. Ensure that your agents can work with the correct details.

• Workflow management: Ensure your agent can work across the correct flow and not interact with unnecessary loops.

Iterate and refine 

Once everything is set up, you can tweak and improve your LLM agent. Check whether your prompts are correct and the logic is perfectly adjusted. If necessary, rerun your test and fix any glitches. 

You can add new test case scenarios if you doubt a new unnatural model behavior.

Sometimes, it seems very easy to run AI agent testing. But, when researchers or developers do the evaluation, they tend to encounter some challenges. Here is the list of AI evaluation challenges every AI enthusiast must know. 

• Consistency across conversations: LLM-based responses can vary between conversations. It is essential to evaluate and ensure consistent response generation. 

• Real-world impact: AI agents integrate with external or internal systems, which can result in errors and have significant implications beyond misinformation. 

• Complex failure modes: AI models are becoming sophisticated. It is difficult to identify root causes and correct failures without scaling your traditional testing tool. 

• Multi-turn testing complexity: Each test requires simulating complete conversations seeking user and agent participation. 

• Lack of standardization and reproducibility: Current evaluation models are not standardized, leading to inconsistent evaluation results. This is hard to compare results across multiple applications and studies.

You can try out several strategies to address the AI agent evaluation challenges. Applying them to your AI agent evaluation steps can be helpful enough to get high-performing AI agents for your business purpose. So, if you want to roll out customer support or employee support chatbots, these strategies do wonders for preventing evaluation challenges. Let’s see what you should do to address these challenges, 

1. Consistency across conversations: Implement fine-tuning techniques to reduce variability in responses. 

2. Real-world impact: Develop evaluation methods to simulate real-world scenarios and measure the precision of your AI agents in integrating with external systems efficiently. 

3. Complex failure modes: You can use advanced assessment tools to analyze failure across multiple systems. Also, use explainable AI to understand the decision-making abilities of the AI agents. 

4. Multi-turn testing complexity: An automated testing framework can help speed up the testing process across multiple systems. 

5. Standardization and reproducibility: The best way to follow the standard testing rule and remain consistent with the AI agent's performance is to collaborate with industry leaders and build a standard benchmark.

Recognizing the importance of highly efficient and high-performing AI agents for productivity and efficiency, it is essential to learn the best practices for AI agent testing and evaluation. Small steps always work better so that it does not seem overwhelming and challenging. Let’s do it in an agile way, where you can start with a small step and fix any issues before you launch a final product in the live environment and avoid financial setbacks. 

1. Build a prototype: Start by creating an initial version of your conversational AI agent chatbot with minimal features. Based on the features, you can segment your testing processes. 

2. Benchmark your data: Put together comprehensive benchmark scenarios to analyze them later. 

3. Create a testing pipeline: Schedule a testing agent to track interactions within the conversational AI bot. 

4. Gather results: Capture data from the testing pipeline and organize the testing outcomes. 

5. Capture metrics: Extract meaningful performance data you set for predefined functions.

6. Keep humans in the loop: Human-machine collaboration can quickly help you detect errors in the output and flag them for reiteration. Be proactive about keeping experts in the loop for evaluation during prototype conversations.     

7. Evaluate metrics: Now, evaluate overall agent performance using the capture insights. 

These best practices are essential for evaluating, testing, and building an effective and robust AI agent platform that meets your business objectives.

Why should you build your AI agent-powered customer or employee support bot with Workativ? To drive efficiency and cost savings.

When you look to build your custom AI agent-based systems or chatbots, it is challenging, as we already discussed. Unless you have OpenAI, Microsoft, or a Meta-like budget, it is difficult to allocate resources and spend time after development processes. Workativ might help you achieve your objectives around AI agents without any hassles. 

No-code platform to build your AI agent

Workativ’s no-code platform, known as the AI agent studio, is the best bet for you to roll out your AI agent systems. Its easy-to-use interface lets you build your knowledge base and apply AI agentic capabilities without extra effort. Besides ensuring your data is clean, you no longer need to take up any hassles. Since AI agentic capabilities are built-in with its ready-to-use platform, you can eliminate the challenges of AI agent evaluation and testing. 

Cost-efficiency and savings 

Workativ’s no-code AI agent studio comes with tested and evaluated built-in agentic capabilities so that you can only focus on the strategic development of workflows instead of bearing the expenses of building LLMs from scratch with costly infrastructure and hardware parts. Another advantage is that you can work with your small AI engineering team without the need for subject matter experts to handle AI agent testing and evaluation. 

Best-in-class data security

Workativ’s AI agent-based system platform protects all users' data by providing SOC2 Type 2 and ISO27001 certifications. Role-based access control and data segmentation further guardrail your sensitive data so that your team can focus on building meaningful workflows for customer or employee support. 

AI agents are the future of robust user experience and escalating business growth. If you want to gain a competitive advantage, build instant AI agents with Workativ AI Agent Studio. Schedule a call today.  

Comprehensive AI agent evaluation is the cornerstone for tapping into the most significant potential of AI agents for business growth. By testing each step of the evaluation workflow, you can build a reliable AI agent system for your business by gathering human feedback and running necessary refinements.

So, whether you want to develop AI agents for customer support to solve password reset issues or customer support for booking an order, a comprehensive, tested, and evaluated AI agent system ensures you can drive efficiency and user experience. Remember, AI agent evaluation provides the best assurance for a reliable system. 

In between, if you want hassle-free AI agent development with an already evaluated AI agent platform, try Workativ’s AI agent studio.  Book a demo today. 

What is AI agent evaluation?

AI agent evaluation is a testing method for identifying and fixing quality issues by determining their root causes. It is significant and necessary to build a reliable system. 

What key metrics are essential to evaluate AI agents?

Several key metrics for AI agent evaluation include topic adherence, tool call accuracy, and agent goal accuracy. The wonder of leveraging AI agents through a no-code platform like Workativ AI Agent Studio is that you can get an evaluated AI system. 

What are the best AI agent evaluation tools and software applications?

The best AI agent evaluation tools and software systems include Vertex AI Gen AI evaluation system, LangSmith, RAGAS, DeepEval, etc. Each tool has different methodologies to evaluate your AI agents. 

What are the benefits of using a no-code platform for AI agent evaluation?

A no-code AI agent evaluation platform quickly removes the need for aggressive testing and evaluation for AI agents because these platforms are already evaluated. As an AI enthusiast, you just build your knowledge bases and customize workflows. You are ready to go. 

What are the benefits of Workativ for building your AI agents?

Building AI agents with Workativ is straightforward. It provides a tested AI agent studio platform to launch your custom bot without hassles or financial implications, such as hardware and infrastructure maintenance fees, developer resources, subject matter expert resources, etc. In addition, you can leverage robust data security and performance efficiency.