Measuring and Monitoring the Performance of Agentic Applications

As IT leaders integrate LLM-powered agentic applications into their enterprise stack, performance measurement becomes critical. Unlike traditional applications, these systems can take on open-ended questions and generate novel responses, making quality assessment more complex than conventional software performance monitoring.

To manage and optimize agentic applications effectively, organizations must define their required performance levels and find the most cost-efficient way to achieve them. Performance measurement spans two key dimensions:

1. Quality of generated responses: Ensuring responses align with application requirements.
2. Speed and responsiveness: Monitoring service latency and throughput.

In this sneak peek of Chapter 4 from the upcoming technical guide we’re producing in partnership with O'Reilly Media, "The LLM Mesh: An Architecture for Building Agentic Applications in the Enterprise,” discover how an LLM Mesh — a common architecture for building and managing agentic applications — provides scalable, consistent performance monitoring across the enterprise.

Dimensions and sub-dimensions of measuring the performance of agentic applications

Beyond Traditional Benchmarks

A lot of attention is given to standard LLM benchmarks, such as massive multitask language understanding (MMLU) or graduate-level Google-proof Q&A (GPQA), to compare models. However, these benchmarks only measure general model performance and do not indicate whether an LLM, used within the context of a specific agentic application, is effectively solving enterprise-specific tasks.

Instead, IT leaders need real-world performance evaluations that capture:

• How accurately responses meet task-specific requirements.
• How well an application adapts to different inputs.
• Whether outputs are reliable, consistent, and cost-effective.

An LLM Mesh ensures that performance measurement is conducted consistently across applications, preventing teams from building redundant evaluation frameworks and making it easier to optimize quality while managing costs.

3 Phases of Performance Monitoring

Performance monitoring occurs throughout an agentic application's lifecycle.

1. Pre-Development: Defining Metrics
   • Identify key quality indicators such as accuracy, clarity, and relevance.
   • Establish benchmarks for acceptable performance.
2. Development: Iterative Testing
   • Evaluate responses against predefined metrics.
   • Improve prompts, retrieval methods, and formatting.
   • Compare models and strategies to find the best-performing solution.
3. Deployment: Ongoing Monitoring
   • Track response quality over time to detect drift due to model updates or changing input data.
   • Adjust prompts, retrieval methods, or tools when necessary.
   • Implement automated evaluations to maintain consistent performance.

Unlike traditional machine learning, LLM-powered applications are non-deterministic, meaning the same input can yield different outputs. Additionally, they function in open-ended contexts, requiring different evaluation metrics depending on the specific task (e.g., selecting a tool, summarizing content, or providing recommendations).

Evaluating the Quality of Generated Responses

Performance evaluation falls into two primary categories:

• Intrinsic quality – Measures how well an LLM performs independent of specific tasks.
• Extrinsic quality – Assesses whether the model fulfills a given application’s objectives.

Intrinsic Quality Measures

Intrinsic evaluations focus on the model’s core capabilities rather than its task-specific performance. Key metrics include:

• Perplexity – Measures how confident the model is in predicting its next token. High perplexity can indicate uncertainty which may be a symptom of poor prompt design or the application being used in an unanticipated context.
• Consistency – Ensures stable responses when given the same input multiple times. Variability may signal issues with model updates or ambiguous instructions.
• Retrieval Quality – Evaluates whether a RAG (retrieval-augmented generation) system is surfacing relevant and accurate documents.

These intrinsic measures help detect potential weaknesses early in development before an application is deployed.

Extrinsic Quality Measures

Extrinsic evaluations assess whether an LLM-generated response meets business requirements. A good response is one that:

• Accurately answers the prompt.
• Is relevant to the task and user needs.
• Adheres to required formatting and structure.

For example, in a customer service categorization application, an ideal response would:

1. Correctly classify a user’s request into a predefined category.
2. Return only the category name (not a full sentence).

For a more complex agentic application, additional evaluations might include:

• Tool selection accuracy – Did the agent choose the right tool for the job?
• Data extraction precision – Is retrieved information relevant and correctly formatted?
• Final response coherence – Does the response flow logically and follow enterprise style guidelines?

Methods for Evaluating Response Quality

There are three primary approaches to assessing the quality of agentic applications.

1. Human Expert Evaluation

The most reliable but least scalable method is direct human evaluation. Subject matter experts review outputs and assess them based on a myriad of dimensions, some of them including:

• Relevance – Does the response align with the query?
• Accuracy – Are the facts correct?
• Clarity – Is the response easy to understand?
• Completeness – Does it contain all necessary information?

While valuable in early development, manual evaluation is impractical for large-scale applications. To mitigate this, organizations can:

• Implement simple user feedback mechanisms (e.g., thumbs up/down ratings).
• Periodically sample responses for expert review to catch quality degradation.

2. Ground Truth-Based Statistical Methods

For applications with structured tasks, responses can be compared against reference outputs codified in golden datasets, which can be built using the previously-mentioned human expert evaluations, using standard evaluation metrics:

• Bilingual Evaluation Understudy (BLEU) – Measures text similarity for tasks like translation.
• Recall-Oriented Understudy for Gisting Evaluation (ROUGE) – Evaluates recall of key phrases in summarization.
• BERTScore – Assesses semantic similarity beyond simple word overlap.

These methods work well for structured outputs but struggle with open-ended responses where multiple valid answers exist.

3. LLM-Based Evaluation (LLM-as-aJudge)

A quickly developing practice is LLM-based evaluation, where an LLM assesses another LLM’s output based on predefined criteria. This approach can grade responses for:

• Factual accuracy – Does the response align with source data?
• Coherence – Is it logically structured?
• Adherence to enterprise guidelines – Does it follow brand voice and compliance standards?

Although powerful, LLM-as-a-judge systems require calibration to ensure they align with human assessments. They also introduce additional computational costs, requiring careful trade-offs between accuracy and efficiency.

Implementing an LLM Mesh for Performance Monitoring

An LLM Mesh centralizes evaluation and enables teams to reuse standardized performance measurement tools across multiple applications. This prevents redundant development efforts and ensures:

• Consistency – All applications follow the same evaluation framework.
• Efficiency – Developers focus on application logic rather than reinventing monitoring methods.
• Scalability – New applications can leverage pre-built evaluation services.

An effective LLM Mesh should track:

• Model version – Ensures reproducibility and compatibility.
• Evaluation method – Specifies whether human, statistical, or LLM-based evaluation is used.
• Performance metrics – Captures scores for aspects like accuracy, clarity, and retrieval quality.
• Trends over time – Monitors shifts in performance due to model updates or changing inputs.

By integrating these capabilities, organizations can detect performance drift early and proactively address issues.

Measuring Speed and Cost Optimization 

Beyond quality, speed and efficiency are key considerations for enterprise applications.

Key Speed Metrics

• Time to First Token (TTFT) – Measures how quickly a model begins responding.
• Tokens per Second (TPS) – Assesses the rate at which responses are generated.
• Total Generation Time – Tracks end-to-end response latency.

These metrics ensure that applications meet required responsiveness thresholds, especially for real-time interactions.

Balancing Evaluation Costs

Performance monitoring itself incurs costs, particularly when using LLM-based evaluation methods. To manage expenses:

• Use strategic sampling – Not all responses need full evaluation.
• Optimize frequency – Run expensive evaluations only when necessary.
• Experiment with different techniques – An LLM Mesh allows teams to test and choose cost-effective evaluation strategies.

In high-volume applications, balancing evaluation accuracy with economic viability is essential to maintain operational efficiency.

An LLM Mesh Is the Key to High-Performing Agentic Applications

Measuring the performance of agentic applications requires a structured approach to monitoring quality, speed, and cost efficiency. By implementing an LLM Mesh, enterprises can standardize evaluation, reduce redundancy, and ensure applications remain reliable as they scale.

While agentic applications introduce unique challenges, a well-designed performance framework empowers IT leaders to optimize accuracy, responsiveness, and cost-effectiveness, enabling their organizations to fully leverage the power of agentic applications at scale.