Oct 16, 2025
Building intelligent agents can be easy. Trusting them is another story.
As AI systems become more capable, understanding why they make decisions becomes critical. Yet most evaluation methods still focus on surface metrics like accuracy or latency, leaving behind reasoning quality, data grounding, and decision logic.
To build reliable, enterprise-ready AI systems, we need to go beyond surface performance numbers and start evaluating how well agents reason, plan, and learn.
In this article, we’ll explore an AI evaluation framework for agentic systems, introduce key AI performance metrics, and show how to benchmark and evaluate AI agents using StackAI’s AI observability and analytics dashboards.
Why Evaluating Agentic AI Systems Is Harder Than You Think
Agentic pipelines are non-deterministic.
What does that mean in practice? It means two identical prompts can lead to different reasoning paths, tool choices, or retrieval strategies. That’s why AI benchmarking and reproducibility are harder here than with more traditional AI approaches. Success isn’t only about the final answer—but also about how the agent actually got there.
A truly effective agentic AI evaluation looks beyond surface-level metrics and digs into reasoning quality, decision logic, and data groundedness. We need to ask: Did the agent choose the right tool for the task? Was its plan efficient, or did it take unnecessary steps? Were its outputs backed by the right evidence? These are just some of the questions that define AI trust and reliability.
Moreover, agentic pipelines run across multiple layers, from retrieval and reasoning to generation and orchestration, and each layer introduces potential failure points. Without strong AI observability and a clear AI evaluation framework, you’re left with black-box risks: systems that look intelligent but can’t be trusted when the stakes are high.
The Core Dimensions of AI Evaluation: Accuracy, Reasoning, and Groundedness
Now that we know traditional accuracy-based testing isn’t enough, the next step is to figure out what really matters when we evaluate AI agents. A good AI evaluation framework should capture not just what an agent outputs, but how it thinks, how it plans, and how it checks the information along the way.
In agentic AI evaluation, that means looking at performance from several angles: accuracy, reasoning, groundedness, efficiency, and user trust.
Dimension | What it Captures | Example metric |
Outcome Quality (Accuracy) | Measures whether the agent achieved the intended result or produced factually correct outputs. | Task success rate, factual precision, relevance score |
Reasoning Quality | Evaluates the logic and coherence behind the agent’s process: how it connects data, plans steps, and makes decisions. | Step validity, reasoning path length and coherence |
Data Groundedness | Assesses how well the agent supports its claims with real evidence from its knowledge base or retrieval sources. | Context relevance, citation quality, hallucination rate |
Operational Efficiency | Captures how effectively the agent balances cost, latency, and resource use without sacrificing quality. | Average response time, token cost, error rate |
Feedback | Combines human judgment and LLM-as-a-judge scoring to quantify helpfulness, tone, and overall user satisfaction. | Rating score, preference comparison, feedback consistency |
Together, these elements form a structured AI performance metrics framework that you can use to benchmark AI agents at every stage, from prototype to production.
AI observability brings it all together. If you keep an eye on how each of these dimensions change over time, you can spot when agents drift off track, see where they’re improving, and build real trust through transparent evaluation.
Used well, these metrics turn evaluation into a measurable and repeatable process—one that helps you understand not just how your agents perform, but how they evolve and respond to different prompts.
The next step is putting that framework into action. With StackAI’s integrated evaluation and observability tools, you can track, benchmark, and refine your AI agents at every stage.
Using StackAI to Benchmark and Evaluate AI Agents
StackAI makes agentic AI evaluation practical. Instead of juggling disconnected tools and manual scripts, you get everything you need for AI benchmarking and AI observability in one place, from tracking performance metrics to evaluating reasoning quality.
🔗 Learn More: If you want to know more what StackAI is, we recommend reading our dedicated articles.
Firstly, in StackAI you can enable citations when working with RAG pipelines with one click. This means every answer your agent produces links directly to the source it came from, which allows you to easily check the accuracy and reliability of its output. It’s an essential part of measuring data groundedness, since you can instantly verify whether responses are supported by the right evidence.
In this example, we’ve built a simple Festival Goer’s Guide Bot in StackAI. The user asks, “Can I bring a refillable bottle to the festival?” The workflow runs through four nodes:
User Message: Captures the question.
Documents: Connects to the uploaded knowledge base and indexes it for retrieval.
AI Model: Runs the query using GPT-4.1.
User Output: Displays the final response.
As you can see in the output panel, the agent answers directly: “Yes, you can bring an empty refillable bottle to the festival…” and cites its source. The reference icon next to the sentence links back to the Festival FAQs.pdf, showing exactly where the information came from.
This built-in citation feature helps you verify accuracy and data groundedness in this type of workflow. You can instantly trace the reasoning behind each answer, confirm the source, and make sure your AI response is based on transparent, verifiable evidence.
You can also see exactly what’s happening behind the scenes when a workflow runs. The interface shows which nodes or tools were used, how data moved through the pipeline, and what decisions the agent made along the way. This level of AI observability helps you understand not just the output, but the process that led to it, so you can spot inefficiencies, dead ends, or retrieval errors that might otherwise go unnoticed.
From there, StackAI offers two complementary views of your agent’s performance: the Analytics Dashboard and the Evaluator Interface.
Analytics Dashboard: Measuring What Matters
Next, the AI analytics dashboard gives you a clear view of your pipeline’s overall health. You can track AI performance metrics like total runs, latency, error rate, token usage, and model versions, all visualised over time. Each execution is logged with full context. It’s a simple way to spot drift, track improvements, and compare versions of your agent workflows side by side.
The dashboard tracks several key AI performance metrics:
Runs: The total number of completed executions.
Errors: Any failed runs.
Users: How many unique users interacted with the workflow.
Tokens: Total tokens processed, which helps you monitor cost and efficiency.
At the bottom, you can also inspect each run in detail, including its status, input, output, latency, and model used.
Evaluator Interface: Understanding How Your Agents Think
The Evaluator on StackAI goes a step deeper. You can seamlessly:
Configure an AI evaluation agent.
Write a grading prompt such as “Score this response from 1–10 based on clarity, accuracy, and helpfulness.”
Decide whether to compare outputs against an expected answer for reference-based scoring.
Run batch evaluations across dozens of queries to see patterns in reasoning and quality.
This is how the Evaluator dashboard in StackAI is configured. From here, you can set up an AI grading agent to automatically assess your agent’s outputs.
Choose which output to evaluate, write your own evaluation criteria, and decide whether to compare against an expected answer. Once everything is set up, you can run individual tests or batch evaluations to score multiple outputs at once within the same interface.
Together, these features let you evaluate AI agents from multiple angles without having to leave the StackAI workspace.
LLM-as-a-Judge: AI Evaluation at Scale
LLM-as-a-Judge is a method where a large language model (LLM) reads another model’s output and scores it against clear criteria. This “LLM judge” can assess a wide range of qualities, such as:
Factual accuracy → Is the answer correct and supported by evidence?
Clarity → Is the response easy to follow and well-structured?
Goal alignment → Does it address what the user actually asked for?
Tone and style → Is it appropriate for the context or audience?
Reasoning quality → Does the logic make sense from step to step?
Creativity or usefulness → For open-ended tasks, does it provide original or valuable insight?
This method brings scalability and consistency to agentic AI evaluation. Rather than spending hours manually comparing responses, you can efficiently run batch evaluations across hundreds of examples.
To see how this works in practice, let’s test it with a simple example. We built a basic Workout Planning Agent in StackAI, a small pipeline that generates weekly workout routines based on the user’s availability, time, and goals.
The setup uses just a few essential nodes:
Input Node: So the user can provide details like: “I can train 3 days a week for 45 minutes, and my goal is to build endurance.”
LLM Node: The heart of the pipeline, which creates a workout plan based on the prompt and the system instructions.
Output Node: Displays the generated workout plan for review.
This simple structure will allow us to show how to evaluate reasoning and quality with StackAI’s Evaluation.
Let’s configure the following system prompt for our Personal Trainer Agent:
You are a certified personal trainer and strength & conditioning coach.
Your goal is to create a weekly workout routine based on the user’s preferences and constraints.
Follow these instructions carefully:
Read the user’s input to understand their goal, number of available days, session duration, and any specific preferences or limitations.
Build a balanced weekly plan that includes a mix of strength, endurance, mobility, and recovery sessions, depending on their goal.
Keep each session within the specified time limit.
Clearly label the days, focus areas, and approximate duration for each workout.
If the user’s request is incomplete, make reasonable assumptions and explain them briefly in your answer.
Always prioritize safety, progression, and recovery balance in your recommendations.
Output your response in a structured format such as:
Example Output:
Goal: [User’s Goal]
Weekly Plan:
Day 1: [Workout Type – Duration – Notes]
Day 2: [Workout Type – Duration – Notes]
…
End with a short summary (2–3 sentences) explaining how the plan supports the user’s goal.
Once the agent is built, let’s publish our workflow and move to the evaluator tab. Here we set up an AI judge using Claude 3.5 Sonnet as the model and give it a specific system prompt to guide its scoring:
You are an expert fitness coach and evaluator.
Your task is to review a workout plan created by an AI personal trainer and rate its quality across key dimensions.
Read the user’s request and the AI-generated workout plan, then score the plan for each of the following aspects on a scale from 1 to 10, where 1 = very poor and 10 = excellent.
Scoring Criteria:
Goal alignment: Does the plan match the user’s stated fitness goal (e.g., endurance, strength, mobility, or general fitness)?
Time accuracy: Does each session fit within the time available, and is the total schedule realistic for the user’s stated availability?
Balance: Does the plan distribute workouts sensibly across the week, balancing muscle groups, rest days, and exercise types?
Clarity: Is the plan clearly structured and easy to follow, with clear labelling of days, durations, and workout types?
Safety and progression: Does the plan encourage safe, progressive training without overloading the user?
For each criterion, provide:
A score from 1–10
A short comment (1–2 sentences) explaining your reasoning.
Finally, calculate the average score across all five aspects and include it at the end.
Now we’re going to try different user inputs. We’ll keep them varied in goal and availability so we get a good spread of responses for the evaluator to score.
Strength and Conditioning Focus: I want to build full-body strength and muscle tone. I can train 4 days a week for 60 minutes per session. I have access to a gym with weights.
Mobility and Recovery Emphasis: I’ve been feeling tight from sitting all day and want to improve mobility and posture. I can train 3 days a week for 30 minutes each session. No gym, just a yoga mat at home.
Endurance Training Goal: I’m training for a half marathon in 12 weeks. I can train 5 days a week for 45 minutes. Please include both running and cross-training sessions.
Busy Schedule, General Fitness: I just want to stay active and healthy. I can only train 3 days a week for 25 minutes. I prefer bodyweight exercises and short sessions I can do at home.
Fat Loss and Conditioning: I want to lose fat and improve stamina. I can train 6 days a week for 40 minutes each. I like a mix of cardio and strength, and I’m okay with gym equipment.
These are the responses and evaluations that we get:
Text Input | Output | AI Grading Agent Evaluation |
I want to build full-body strength and muscle tone. I can train 4 days a week for 60 minutes per session. I have access to a gym with weights. | Goal: Build full-body strength and muscle tone Weekly Plan: Day 1: Strength Training – 60 minutes – Focus on upper body (chest, back, shoulders, arms) - Warm-up: 10 minutes of dynamic stretching - Main workout: - Bench Press: 4 sets of 8-10 reps - Bent-over Rows: 4 sets of 8-10 reps - Shoulder Press: 3 sets of 10-12 reps - Pull-ups or Lat Pulldowns: 3 sets of 8-10 reps - Cool down: 5 minutes of stretching Day 2: Strength Training – 60 minutes – Focus on lower body (legs, glutes) - Warm-up: 10 minutes of dynamic stretching - Main workout: - Squats: 4 sets of 8-10 reps - Deadlifts: 4 sets of 8-10 reps - Lunges: 3 sets of 10-12 reps per leg - Calf Raises: 3 sets of 12-15 reps - Cool down: 5 minutes of stretching Day 3: Active Recovery – 60 minutes – Focus on mobility and flexibility - Activities: Yoga or Pilates session, emphasizing full-body stretches and mobility work - Include foam rolling for muscle recovery Day 4: Strength Training – 60 minutes – Full-body circuit - Warm-up: 10 minutes of dynamic stretching - Main workout: - Circuit (3 rounds, 1-minute rest between rounds): - Push-ups: 10-15 reps - Kettlebell Swings: 12-15 reps - Dumbbell Thrusters: 10-12 reps - Plank: 30-60 seconds - Cool down: 5 minutes of stretching Summary: This plan incorporates a balanced approach to building full-body strength and muscle tone through targeted strength training sessions, complemented by an active recovery day focused on mobility. Each session is designed to maximize your gym time while ensuring adequate recovery and progression. | This workout plan is well-structured and tailored to the user's specific goals and constraints. Here's a breakdown of its strengths: 1. Addresses the goal: Focuses on full-body strength and muscle tone. 2. Fits the time constraint: 4 days a week, 60 minutes per session. 3. Utilizes available resources: Designed for a gym with weights. 4. Balanced approach: Includes upper body, lower body, and full-body workouts. 5. Incorporates active recovery: Includes a day for mobility and flexibility. 6. Proper structure: Each session includes warm-up, main workout, and cool-down. 7. Variety: Mixes different exercises and training styles (strength training, circuit training). 8. Progression: Provides rep ranges to allow for progressive overload. 9. Recovery: Allows for rest days between strength training sessions. 10. Comprehensive: Includes all major muscle groups over the course of the week. The plan could be improved by adding some notes on progression (e.g., how to increase weights over time) and providing alternatives for some exercises. However, overall, it's a very solid and helpful response. Grade: 9/10 |
I’ve been feeling tight from sitting all day and want to improve mobility and posture. I can train 3 days a week for 30 minutes each session. No gym, just a yoga mat at home. | Goal: Improve Mobility and Posture Weekly Plan: Day 1: Mobility & Stretching – 30 minutes – Focus on dynamic stretches and mobility exercises targeting the hips, shoulders, and spine. Include movements like cat-cow, hip openers, and thoracic spine rotations. Day 2: Yoga Flow for Posture – 30 minutes – A gentle yoga session emphasizing postures that promote good alignment and flexibility. Include poses like downward dog, cobra, and bridge, focusing on breath and mindful movement. Day 3: Core Strength & Stability – 30 minutes – Engage in exercises that strengthen the core and support good posture. Include planks, bird-dogs, and side-lying leg lifts, ensuring to maintain proper form throughout. This plan is designed to enhance your mobility and posture through a combination of stretching, yoga, and core strengthening exercises. Each session is tailored to fit within your time constraints while promoting recovery and flexibility. | This workout plan is well-tailored to the user's specific needs and constraints. Here's why it's effective: 1. Addresses the goal: The plan directly targets improving mobility and posture, which aligns with the user's stated objective. 2. Fits time constraints: Each session is exactly 30 minutes, matching the user's available time. 3. Frequency: The plan utilizes all three days the user can train per week. 4. No equipment needed: The plan can be done at home with just a yoga mat, as specified. 5. Variety: The plan incorporates different types of exercises (mobility work, yoga, and core strengthening) to provide a well-rounded approach. 6. Specific focus areas: The plan targets key areas that are often affected by prolonged sitting, such as hips, shoulders, and spine. 7. Progressive approach: The plan starts with mobility work, moves to yoga for alignment, and then focuses on core strength, which is a logical progression. 8. Clear instructions: Each day has a clear focus and examples of exercises to be included. 9. Consideration of recovery: The plan alternates between different types of exercise, allowing for recovery between sessions. 10. Adaptability: While specific exercises are suggested, the plan allows for flexibility in choosing exact movements within each category. Given how well this plan meets the user's needs and constraints while providing a comprehensive approach to improving mobility and posture, I would rate this response a 9/10. |
I’m training for a half marathon in 12 weeks. I can train 5 days a week for 45 minutes. Please include both running and cross-training sessions. | Goal: Half Marathon Training Weekly Plan: Day 1: Running – 45 minutes – Focus on a steady-state run at a comfortable pace to build endurance. Aim for 4-5 miles depending on your current fitness level. Day 2: Cross-Training (Cycling or Swimming) – 45 minutes – Engage in low-impact cardio to improve aerobic capacity while giving your legs a break from running. Maintain a moderate intensity. Day 3: Running – 45 minutes – Incorporate interval training. Warm-up for 10 minutes, then alternate between 1 minute of fast running and 2 minutes of walking/jogging for 25 minutes, followed by a 10-minute cool down. Day 4: Cross-Training (Strength Training) – 45 minutes – Focus on full-body strength exercises that enhance running performance, such as squats, lunges, planks, and core work. Aim for 2-3 sets of 10-15 reps for each exercise. Day 5: Long Run – 45 minutes – Gradually increase your distance each week. Start with a comfortable pace and aim for a longer distance than Day 1, focusing on building endurance. Summary: This plan balances running and cross-training to enhance your endurance and strength while minimizing the risk of injury. The mix of steady runs, intervals, and cross-training will prepare you effectively for your half marathon in 12 weeks. | This training plan for a half marathon in 12 weeks is well-structured and appropriate. Here's my evaluation: Strengths: 1. Balanced mix of running and cross-training (3 running days, 2 cross-training days) 2. Variety in running workouts (steady-state, intervals, long run) 3. Incorporates strength training, which is crucial for injury prevention 4. Low-impact cross-training options provided 5. Time-efficient workouts fitting the 45-minute constraint 6. Gradual progression mentioned for the long run 7. Clear focus for each session Areas for improvement: 1. Could provide more specific guidance on how to progress over the 12 weeks 2. Might benefit from including a rest day, though this wasn't specified in the request 3. Could offer alternatives for the cross-training days to add variety Overall, this plan provides a solid foundation for half marathon training within the given constraints. It's comprehensive and well-balanced, addressing key aspects of endurance, speed, and strength. Grade: 8/10 |
I just want to stay active and healthy. I can only train 3 days a week for 25 minutes. I prefer bodyweight exercises and short sessions I can do at home. | Goal: Stay Active and Healthy Weekly Plan: Day 1: Full Body Bodyweight Workout – 25 minutes – Focus on compound movements like squats, push-ups, and lunges to engage multiple muscle groups. Include a warm-up and cool-down for safety. Day 2: Cardio and Core – 25 minutes – Incorporate high-intensity interval training (HIIT) with exercises like jumping jacks, mountain climbers, and planks to boost heart rate and strengthen the core. Day 3: Mobility and Flexibility – 25 minutes – Focus on dynamic stretches and yoga-inspired movements to improve flexibility, balance, and recovery. Include deep breathing exercises to enhance relaxation. This plan supports your goal of staying active and healthy by providing a balanced mix of strength, cardio, and mobility work, all within your preferred time frame and using bodyweight exercises that can be done at home. | This response is highly relevant and tailored to the user's specific requirements. Let's break it down: 1. Addresses time constraint: The plan fits perfectly into the user's 3 days a week, 25-minute sessions. 2. Focuses on bodyweight exercises: As requested, no equipment is needed. 3. Home-based workouts: All suggested exercises can be done at home. 4. Balanced approach: The plan includes a mix of strength training, cardio, and flexibility work, which is ideal for overall health and fitness. 5. Specific and actionable: Each day has a clear focus and general outline of exercises to be performed. 6. Aligned with the goal: The plan directly supports the user's desire to "stay active and healthy." 7. Safety considerations: Mentions warm-up and cool-down, which is important for injury prevention. 8. Variety: Offers different types of exercises across the week to prevent boredom and target different aspects of fitness. 9. Concise yet informative: Provides enough detail to be useful without overwhelming the user. 10. Flexibility: The general nature of the plan allows for modifications based on individual fitness levels. Given how well this response meets the user's needs and provides a practical, well-rounded plan, I would rate this a 9/10. The only potential improvement might be to provide a few more specific exercise examples for each day, but overall, it's an excellent response. |
I want to lose fat and improve stamina. I can train 6 days a week for 40 minutes each. I like a mix of cardio and strength, and I’m okay with gym equipment. | Goal: Fat Loss and Improved Stamina Weekly Plan: Day 1: Strength Training – 40 minutes – Focus on full-body strength with compound movements (e.g., squats, deadlifts, bench press). Aim for 3 sets of 10-12 reps with moderate weights to build muscle and boost metabolism. Day 2: High-Intensity Interval Training (HIIT) – 40 minutes – Alternate between 30 seconds of high-intensity exercises (e.g., burpees, kettlebell swings) and 30 seconds of rest. This will enhance cardiovascular fitness and promote fat loss. Day 3: Steady-State Cardio – 40 minutes – Choose a cardio machine (treadmill, elliptical, or bike) and maintain a steady pace at 60-70% of your maximum heart rate. This will improve endurance and aid in fat loss. Day 4: Strength Training – 40 minutes – Focus on upper body strength (e.g., pull-ups, rows, shoulder press). Incorporate core exercises (planks, Russian twists) to enhance stability and support fat loss. Day 5: Circuit Training – 40 minutes – Combine strength and cardio in a circuit format. Include exercises like push-ups, lunges, jump rope, and mountain climbers. Perform each exercise for 1 minute with minimal rest between exercises. Day 6: Active Recovery – 40 minutes – Engage in low-intensity activities such as yoga, stretching, or a leisurely walk. This will promote recovery and flexibility while still keeping you active. Day 7: Rest – Allow your body to recover fully. This plan balances strength training and cardiovascular workouts, which are essential for fat loss and stamina improvement. The mix of HIIT, steady-state cardio, and strength training will keep your workouts engaging while promoting overall fitness and recovery. | This workout plan is well-structured and addresses the user's goals and preferences effectively. Here's why it deserves a high rating: 1. It directly addresses the user's goals of fat loss and improved stamina. 2. It accommodates the user's preference for a mix of cardio and strength training. 3. The plan fits the user's availability of 6 days a week for 40 minutes each. 4. It incorporates gym equipment, as the user is comfortable with this. 5. The plan provides a good balance of different types of workouts (strength, HIIT, steady-state cardio, circuit training). 6. It includes an active recovery day and a full rest day, which are important for overall fitness and injury prevention. 7. The plan offers specific examples of exercises for each day, making it easy to follow. 8. It explains the benefits of each type of workout, helping the user understand the purpose of each session. 9. The plan is varied enough to prevent boredom and target different aspects of fitness. 10. It includes guidance on intensity and rep ranges for strength training days. The only minor improvement could be to provide more specific guidance on how to progress or modify the plan over time. However, overall, this is an excellent and comprehensive response to the user's input. Rating: 9/10 |
In this table, we can see how the LLM-as-a-Judge evaluates each workout plan created by the agent. Each user input (on the left) corresponds to a generated workout plan (in the center), followed by the AI Grading Agent’s evaluation (on the right).
For every plan, the evaluator reads the AI-generated output and scores it against the five predefined criteria from the system prompt: goal alignment, time accuracy, balance, clarity, and safety and progression.
The evaluator provides a short written explanation for each criterion, then calculates an overall grade (for example, 8/10 or 9/10) that reflects the average of the five scores. You can see how it justifies each rating with concrete reasoning, such as noting strong structure, clear labelling, or areas where the plan could include more progression guidance.
This is where LLM-as-a-Judge shows its strength: it delivers detailed, consistent feedback across multiple runs, something that would be time-consuming to do manually. The results are automatically stored in the StackAI Evaluator interface, so you can compare responses side by side and quickly spot patterns in quality and reasoning.
🔗 Learn More: If you want to know more about building your own chatbots, we recommend reading our dedicated articles.
Conclusion: From Evaluation to Evolution
Evaluation isn’t just the final step in building AI agents but the foundation for improving them. As systems become more agentic, with the ability to reason, plan, and act independently, AI evaluation becomes the key to maintaining trust, transparency, and reliability.
With StackAI, you can close the feedback loop: design, test, measure, and refine, all in one place where the combination of AI observability, AI benchmarking, and LLM-as-a-Judge scoring turns abstract intelligence into something tangible and trackable.
Want to see the evaluator in action at your enterprise? Book a demo today.
Divesh Punjabi
Product Manager at StackAI
