How we build evals for AI Agents in 2026

Did you know that growth and revenue teams waste hours each week on research, qualification, and writing tasks? These tasks need detailed research and complex judgment calls.

AI agents promise to automate these time-consuming activities. The biggest problem lies in determining if these agents actually work well. Building an AI agent takes you halfway there. The accuracy of performance review matters just as much.

Our team at Persana Unify discovered that standard evaluation metrics weren't enough during our initial AI system development. We needed fresh approaches to measure our AI's ability to handle nuanced tasks like date comparisons and tone management.

Our trip to learn how to review AI models led us to build foundations for our always-on system. This system combines various AI features that include both single-turn AI and multi-turn agents. We built datasets from real email chains and used human labeling to establish ground truth for proper testing.

This piece will share our method to build complete evaluations for AI agents. You'll learn about the challenges we overcame and the metrics that truly matter when your AI needs real-life deployment.

Evaluating Single-Turn AI Systems

Single-turn AI serves as a core building block in modern artificial intelligence systems that power many applications we use daily. Let's look at how these systems work and why they need special evaluation methods.

What is single-turn AI and how it works

Single-turn AI systems process inputs and create outputs in one atomic interaction. These systems differ from conversational ones as they make just one forward pass through the model to generate a result that can be evaluated. They power many applications like summarizers, question-answering systems, and autonomous agents. The testing process usually treats the AI as a black box and looks at system inputs and outputs. Some evaluation methods can show what happens inside individual components through LLM tracing.

Reply classification as a use case

Email reply classification shows single-turn AI at its best. This technology sorts incoming emails into preset categories like "Out of Office," "Unsubscribe," "Willing to meet," or "Follow up question".

Apollo's sales development teams make use of these systems to handle large volumes of email responses. This helps them focus on promising leads instead of sorting through automated messages. Their system produced remarkable results: 90% overall accuracy for all categories, 99% precision in Out of Office detection, and 90% success rate in spotting potential meetings.

On top of that, these systems can spot subtle replies like "I'll review this next week" to pause outreach automatically and start again at the right time. This shows how single-turn AI creates real business value through simple but powerful classification.

Why standard accuracy metrics fall short

In spite of that, basic accuracy measurements often tell an incomplete story about AI system performance. Teams often find that overall accuracy can be misleading, especially with uneven data or when some classification errors matter more than others.

To cite an instance, see this eye-opening example: an HIV test that returns "negative" without scrutinizing the sample would show 99.3% accuracy simply because HIV affects only 0.7% of people. So we need better ways to evaluate these systems.

Teams that build effective single-turn systems add precision, recall, and F1 scores to their accuracy metrics. Some create weighted accuracy measurements that focus on business-critical classifications. This integrated approach reveals issues that basic accuracy metrics might miss and shows clear paths for model improvements.

Challenges in Multi-Turn Agent Evaluation

Multi-turn AI agents represent a huge leap in complexity for evaluation teams compared to basic input-output systems. Teams need completely new approaches to test and measure these sophisticated systems.

How multi-turn agents differ from single-turn AI

Multi-turn agents keep track of context through multiple interactions, like human conversations that build on previous exchanges. Single-turn systems process each request on its own. However, multi-turn agents remember what was discussed before. Users can ask follow-up questions naturally without repeating themselves. This contextual awareness creates dynamic and unpredictable interactions that regular evaluation methods can't properly assess.

Common failure modes in agent evaluation

Several distinct failure patterns show up when evaluating multi-turn agents:

• Context drift - Agents lose track of earlier information gradually, which makes later responses less relevant

• Contradiction detection - Models contradict themselves as conversations continue

• Tool misuse - Wrong selection or use of available tools during complex tasks

• Inefficient reasoning - Extra steps or redundant planning that slows down task completion

• Bandwidth overload - Context windows get full with previous exchanges

Multi-agent systems also face unique security risks. These include agent misalignment and users losing trust when systems are compromised.

Why traditional metrics are not enough

Standard evaluation approaches don't work for multi-turn systems because success depends on entire interactions rather than single turns. Traditional metrics like BLEU and ROUGE look at basic word matching instead of measuring true understanding or long-range connections.

Similar starting points might lead agents through different yet equally valid paths to reach the same goal during evaluation. This unpredictability means we can't just check if agents followed specific steps. Evaluation needs to focus on outcomes while checking if the process made sense.

Teams need flexible methods to assess both final results and key checkpoints throughout complex processes. The evaluation must measure how well the agent maintains context, handles tools across turns, and moves conversations forward.

New Metrics for Evaluating AI Agents

Metrics that capture unique capabilities are essential to assess multi-turn AI agents. Our team at Persana.ai has created specialized frameworks that give a full picture of agent performance beyond standard metrics.

Plan quality using human and LLM-as-a-judge

Research agents that create plans before conducting research need quality assessment through human evaluations and LLM-as-judge methods. Judge LLMs apply rubrics to check thoroughness, instruction precision, and user guidance adherence. This method matches human video-based assessments well, with 42-100% agreement in 5-class systems and 68-100% in 2-class systems.

Tool choice and diversity scoring

Each task receives a tool choice score based on specific research requirements that need particular tools. The number of unique tools used across tasks determines tool diversity. A 1-5 score from the ToolCallAccuracyEvaluator shows how well agents pick and use appropriate tools.

Efficiency and step count analysis

Step counting helps measure task efficiency, with each step representing core phases like planning, reflecting, or tool calling. This method spots complex solution paths and helps simplify agent processes. The TaskAdherenceEvaluator tracks how well agents maintain focus without wasting steps.

Reliability through repeated runs

Running similar evaluations multiple times helps calculate standard deviation. This reveals agent behavior inconsistencies, such as problems with date comparisons or varying results from identical inputs.

Generalizability across tasks

Agent performance consistency across different task types forms the basis of meta-evaluation. Testing agents in various scenarios reveals configuration strengths and weaknesses. Quality evaluations use benchmark datasets with ground examples to ensure reliable performance.

Real-World Use Cases and Evals

AI agents have already crossed the adoption tipping point in enterprise settings. Gartner predicts that by 2027, 40% of enterprise workloads will run on autonomous AI agents. These systems manage increasingly critical business functions, so our evaluation approaches need to evolve.

Firmographics and technographics evaluation

AI agents must interpret firmographic data (industry, company size, revenue, location) and technographic information (technology stack, tools used) effectively. Our testing evaluates an agent's skill to combine these datasets into an all-encompassing, three-dimensional view of potential customers. A well-functioning agent should spot relationships between a company's characteristics and technology priorities. This enables hyper-targeted segmentation beyond traditional analysis methods.

Account qualification and growth signals

Revenue teams need AI agents that can qualify leads in multiple languages and regions at once. The evaluation frameworks must assess capabilities across four key dimensions: multi-channel qualification (email, WhatsApp, SMS, voice), lead scoring with enrichment services, voice-based qualification, and response time measurement. By 2025, more than 60% of enterprises will deploy at least one AI agent to handle these functions.

Business understanding through reasoning

Business reasoning evaluation looks at how agents maintain context across workflows and make smart decisions. The frameworks test whether agents follow enterprise norms and policies while delivering accurate insights. This process measures their skill to keep conversations flowing and complete tasks in business settings.

Writing capability and copy quality

AI-generated content evaluation has evolved beyond simple accuracy metrics to detailed quality assessment. The frameworks analyze relevance, coherence, groundedness, and brand voice alignment. You can learn how our evaluation tools help implement these frameworks for your organization's agents at Persana.

Conclusion

AI agent evaluation remains significant as these technologies become part of business workflows. Standard metrics don't adequately assess single-turn and multi-turn AI systems. So, specialized evaluation frameworks need to think about context retention, tool utilization, and reliability in repeated runs.

Simple accuracy metrics have evolved into complete evaluation systems that match AI agents' growing sophistication. These advanced metrics help us understand agent performance in various business scenarios - from firmographic analysis to account qualification and content generation.

Trustworthy AI deployment in enterprise settings needs effective evaluation frameworks as its foundation. Organizations implementing reliable testing methodologies build confidence in their AI systems and spot areas that need improvement.

The future of evaluation methodologies will evolve with the agents they measure through 2026 and beyond. Our team at Persana has built specialized frameworks to address these unique challenges - you can learn how these evaluation tools benefit your organization's AI initiatives at Persana.ai.

The difference between a basic AI agent and one delivering exceptional business value comes down to thorough evaluation practices. Companies investing in complete testing now will without doubt gain competitive advantages as AI agents handle complex workflows over the next several years.

Key Takeaways

Building effective AI agents requires moving beyond traditional accuracy metrics to comprehensive evaluation frameworks that capture real-world performance complexities.

• Traditional accuracy metrics fail for AI agents - Standard metrics miss critical failures like context drift, tool misuse, and inconsistent reasoning across multi-turn interactions.

• Multi-turn agents need specialized evaluation approaches - Unlike single-turn systems, agents require assessment of plan quality, tool diversity, efficiency, and reliability across repeated runs.

• Business-focused metrics drive real value - Evaluate agents on firmographic analysis, account qualification, and content quality rather than just technical performance scores.

• Reliability testing through repeated runs is essential - Measure standard deviation across identical evaluations to identify inconsistencies and ensure consistent agent behavior.

• LLM-as-a-judge combined with human evaluation provides robust assessment - This hybrid approach achieves 42-100% agreement rates while scaling evaluation processes effectively.

The future of AI agent deployment depends on rigorous evaluation practices that assess both technical capabilities and business outcomes, ensuring agents deliver consistent value in enterprise workflows.