zbrain.ai-Scope Key Metrics Configuration and Best Practices.pdf

zbrain.ai-Scope Key Metrics Configuration and Best Practices.pdf

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  1/34 ZBrain August 1,2025 Scope, Key Metrics, Configuration, and Best Practices zbrain.ai/how-to-use-zbrain-monitor ← All Insights AI solutions are transforming enterprise operations, driving automation, elevating customer experiences, and enabling smarter decision-making across industries. As these agentic AI systems become more autonomous and interconnected, real-time monitoring is no longer optional; it’s essential for maintaining quality, reliability, and business value. Recent research highlights the urgency: a May 2024 study, “Visibility into AI Agents,” warns that a lack of transparency into agent activities and decision-making poses serious operational and governance risks. According to PwC’s 2024 AI Business Survey, only 11% of executives report having fully implemented fundamental responsible AI capabilities. Monitoring and auditing are highlighted as one of the most crucial categories for Responsible AI, yet more than 80% of organizations indicate they are still progressing toward full implementation. Security surveys indicate that the risk is escalating. SailPoint’s research found that while 98% of companies plan to expand the use of AI agents, 96% view them as a growing security threat, and 54% specifically report risks related to the information accessed by AI agents. These gaps in observability and control are red flags signaling the need for real- time, agent-level monitoring. ZBrain’s Monitor feature meets this need, enabling continuous, automated oversight of AI agents and applications. By combining advanced evaluation metrics with actionable insights, it empowers organizations to detect issues early and optimize AI performance at
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  2/34 scale. This articleprovides an in-depth overview of AI agent and application monitoring, with step-by-step guidance on implementing effective oversight using ZBrain Monitor’s feature. AI agent and application monitoring: An overview Building AI agents is an exciting challenge, but simply deploying them is not enough to ensure consistent and reliable results in real-world settings. Once in production, AI agents and applications become part of dynamic, often unpredictable business environments. To maintain performance, prevent failures, and continuously improve outcomes, organizations need robust monitoring, evaluation and observability practices. AI agent and application monitoring is the ongoing process of observing and evaluating the real-world behavior, performance, and impact of autonomous systems within an enterprise. It involves the systematic tracking and analysis of agent’s inputs, outputs, resource usage (such as tokens and credits), response quality, and operational health. It monitors each agent’s outcomes, success and failure trends, and cost metrics, providing end-to-end visibility into the behavior and performance of autonomous systems in production. This goes well beyond checking if the service is “up or down.” It means capturing and analyzing signals such as logs and metrics that reflect how agents interact with data. A new frontier in this field is Large Language Model (LLM) observability. Unlike traditional software, LLM-powered agents are probabilistic, context-dependent, and sometimes unpredictable. LLM observability empowers teams to: Track which prompts, contexts, and actions led to specific responses Debug and trace unexpected behaviors Bootstrap robust test sets from real user data Compare key metrics, like accuracy, latency, and cost, across different model versions in production. Comprehensive monitoring provides real-time visibility into the operational status and performance of AI agents and applications. Effective monitoring enables organizations to: Confirm AI solution health: Verify that agents and applications are live, responsive, and able to produce valid outputs through automated health checks. Evaluate response quality: Continuously assess outputs using metrics such as response relevancy, faithfulness to context, exact match to reference answers, and other configurable evaluation criteria. Track success and failure trends: Monitor patterns of successful and failed responses to quickly identify recurring issues and breakdowns in agent and app workflows. Drive operational insights: Analyze key operational metrics, including latency, token usage, and resource costs, to inform ongoing optimization.
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  3/34 Support reliability: Detectabnormal behaviors, errors, or unintended entity actions that could indicate failures, etc. Enable troubleshooting: Maintain detailed logs and query-level monitoring to support rapid root-cause analysis and continuous improvement cycles. Simply put, AI agents and application monitoring transform opaque, self-learning systems into accountable business assets, empowering enterprises to maximize value while minimizing risk. Streamline your operational workflows with designed to address enterprise challenges. Explore Our AI Agents Key challenges addressed by AI monitoring As AI agents and applications take on increasingly critical roles in enterprise operations, robust, real-time monitoring has become essential for addressing a range of operational and strategic challenges. Detecting silent failures Monitoring enables organizations to catch responses that appear valid but are actually incorrect, irrelevant, or low quality. With query-level visibility and configurable evaluation metrics, silent failures are identified early, preserving business accuracy and user trust. AI apps and agents may seem to perform as expected, yet silently deliver low-quality or inconsistent results. Granular quality assessment Monitoring allows every response to be automatically assessed against custom criteria for relevance, faithfulness, and accuracy. This ensures that AI agents and applications consistently meet enterprise benchmarks for quality. Framework diversity and integration overhead AI solutions are built using a wide range of frameworks, each with distinct abstractions, control patterns, and internal states. Achieving unified visibility often requires manual instrumentation (adding trace metadata, wrapping agent logic, custom logging), which is tedious, error-prone, and can still miss key behaviors like retries or fallbacks. Fragmented view of agent workflows In complex environments, agents interact with multiple tools, APIs, or models. Without centralized monitoring, understanding the complete flow of data and decisions across these components is challenging, making root-cause analysis a time-consuming process. Visualization gaps in traditional tools
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  4/34 Most observability dashboardsare designed for linear, synchronous applications, not for the nonlinear, parallel, and branching logic typical of agentic systems. This makes it difficult to reconstruct execution paths, trace agent decision-making, or identify where and why failures occurred. Enabling timely issue detection With real-time alerts and thresholds, monitoring quickly surfaces performance bottlenecks, error spikes, or sudden drops in accuracy. This supports rapid diagnosis and minimizes the impact on users and operations. Vendor lock-in Relying on monitoring tools that store logs and metrics in proprietary formats can trap organizations with a single vendor. If teams later need to switch providers or integrate with other platforms, migrating historical monitoring data becomes costly and technically challenging, directly limiting flexibility and slowing processes. Integration complexity and technical debt Monitoring AI agents and apps built on diverse frameworks often demands custom connectors and manual scripts for each environment. As systems evolve, maintaining these integrations increases support costs, slows development, and compounds technical debt, raising the risk of operational failures. By addressing these challenges, comprehensive monitoring enables AI agents and applications to transform from opaque, unpredictable systems into transparent, manageable, and reliable enterprise assets. Metrics used for monitoring AI agents and applications
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  5/34 Robust monitoring ofAI agents and applications relies on a diverse set of metrics to evaluate response quality, operational health, and alignment with business objectives. The ZBrain Monitor module supports a rich selection of evaluation metrics that fall into three primary categories: LLM-based metrics Non-LLM-based metrics LLM-as-a-judge metrics LLM-based metrics LLM-based metrics evaluate the quality and relevance of agent and app responses using large language models. The LLM-based metrics are specific to the Ragas library, which uses internal prompt templates and language models to perform automated evaluation. These are especially valuable when output quality cannot be reliably measured by simple string matching or basic logic. These metrics capture the nuanced, semantic qualities of language and are particularly valuable for open-ended, context-rich outputs. Common LLM-based metrics include: Response relevancy: This metric assesses how well the AI solution’s response addresses the user’s query. A higher score indicates a more relevant and contextually appropriate answer, supporting better user experiences and business outcomes. Faithfulness: Evaluates whether the generated response accurately reflects the provided context, data, or source information. High faithfulness scores indicate that the output minimizes hallucinations or unsupported statements, which is crucial for ensuring trustworthy AI in enterprise settings. Example: For a customer support agent, LLM-based metrics help ensure that responses not only sound reasonable but are factually accurate and tailored to the actual customer question. LLM-based metrics help overcome the limitations of simple text matching by leveraging semantic understanding, making them more aligned with human judgment. Non-LLM-based metrics Non-LLM-based metrics rely on deterministic, algorithmic comparisons, making them efficient and useful for specific scenarios where the expected output is known or tightly controlled. These are highly efficient for structured outputs and scenarios where deterministic comparisons are appropriate. Common non-LLM-based metrics include: Health check: Verifies if the agent or application is operational and capable of producing a valid response. If the health check fails, no further metric evaluations are performed for that execution, helping teams quickly pinpoint systemic issues and ensuring focus on root-cause diagnosis.
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  6/34 Exact match: Comparesthe agent’s and app’s response to an expected result for an exact, character-by-character match. This metric is particularly valuable for tasks that require deterministic outputs, such as code generation, database lookups, or standardized answers. F1 score: Balances precision and recall, measuring how well the response covers all relevant information (precision) and avoids missing any expected content (recall). Widely used for classification and extraction tasks. Levenshtein similarity: Calculates the minimal number of single-character edits needed to change one string into another. This provides a measure of similarity between the generated and reference responses, which is useful for tracking the closeness of the output to the desired answer. ROUGE-L score: Evaluates the similarity between generated and reference responses by identifying the longest common subsequence of words. ROUGE-L is commonly used in natural language generation tasks, such as summarization. Example: For document automation or data extraction agents, these metrics ensure the outputs match ground truth records with high accuracy and reliability. These metrics are fast, objective, and especially valuable for rule-based validation and deterministic use cases. LLM-as-a-judge metrics LLM-as-a-judge metrics simulate human-like evaluation by using an LLM to judge qualitative aspects of a response. By “judging” aspects of a response in natural language, these metrics help organizations quantify subjective qualities that are important for user engagement and satisfaction. This approach provides a scalable, consistent, and cost- effective alternative to human review, enabling continuous quality monitoring at production scale: Creativity: Rates the originality and imagination in the response while addressing the prompt, which is valuable for generative content or brainstorming agents. Helpfulness: Measures how well the response guides or supports the user in resolving their question or problem, ensuring the AI solution provides actionable and valuable information. Clarity: Assesses how clearly the message is communicated, reflecting whether the response is easily understandable and effectively delivers its intended meaning. Example: For executive summary generators or creative writing assistants, these metrics help teams ensure that AI outputs are not only accurate but also engaging and easy to understand. Why use multiple metrics?
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  7/34 Combine multiple metricsto define composite and reliable evaluation rules. By leveraging a mix of LLM-based, non-LLM-based, and LLM-as-a-judge metrics, organizations gain a comprehensive and nuanced view of operational health, output quality, and user experience. This enables continuous optimization, facilitates faster troubleshooting, and enhances confidence in AI-powered workflows. LLM-based metrics bring semantic intelligence, non-LLM-based metrics ensure objective comparability, and LLM-as-a-judge delivers scalable, human-like evaluation. By combining these, the Monitor module provides end-to-end visibility, allowing enterprises to: Detect subtle, context-specific errors invisible to simple algorithms Quantify output quality across diverse use cases and formats Benchmark model performance over time and across versions Automate routine evaluation while reserving human attention for high-impact issues With configurable evaluation conditions and flexible metric selection, modern monitoring practices empower enterprises to maintain the highest standards of accuracy, reliability, and user satisfaction across all ZBrain AI agents and applications. How ZBrain Monitor enables real-time oversight of AI agents and applications For enterprises relying on AI solutions, maintaining high standards of reliability, quality, and compliance is non-negotiable. The ZBrain Monitor module is purpose-built for this challenge, empowering teams to automate granular evaluation and performance tracking across all AI agents and applications. The ZBrain Monitor module delivers end-to-end visibility and control of AI agents and applications, as well as prompts specific to apps, by automating both evaluation and performance tracking. With real-time tracking, ZBrain Monitor ensures response quality by continuously monitoring for emerging issues based on configured evaluation criteria. This enables users to be alerted in real-time, helping maintain optimal performance across all deployed solutions. Monitor captures inputs and outputs from your AI solutions and continuously evaluates responses against defined metrics at scheduled intervals. This process delivers real-time insights into operational performance, tracking both success and failure rates while
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  8/34 highlighting trends thatrequire attention. All results are presented in an intuitive interface, enabling rapid identification and resolution of issues to ensure consistent, high-quality AI interactions across your enterprise. Automated evaluation: Leverage flexible evaluation frameworks with LLM-based, non-LLM-based, and LLM-as-a-judge metrics. This enables scenario-specific monitoring tailored to your specific use cases. Performance tracking: Identify trends in agent and application performance through dynamic visual logs and comprehensive reports. Query-level monitoring: Configure evaluations at the individual query level within each session. This granular approach provides precise oversight of agent and app behaviors and outputs. Agent and app support: Achieve end-to-end visibility by monitoring both AI agents and AI applications across your enterprise landscape. Input flexibility: Evaluate responses across a wide range of file types, including PDF, text files, images, and other supported formats, ensuring broad applicability across diverse workflows. Notification alerts: Enable real-time notifications to receive event status updates via multiple channels or email.
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  9/34 The Monitor moduleincludes these main sections, accessible from the left navigation panel: Events: View and manage all configured monitoring events in a centralized list. Monitor logs: Review detailed execution results and evaluation metrics, making it simple to trace, audit, and troubleshoot. Event settings: Configure evaluation metrics, thresholds, and notification parameters for each event, enabling tailored monitoring strategies. User management: ZBrain Monitor supports role-based user permissions. Administrators can assign access and management rights to specific users, including Builders and Operators, ensuring secure and controlled oversight of monitoring activities. By automating the monitoring process and surfacing actionable insights in real time, ZBrain Monitor enables teams to maintain continuous, high-quality AI operations, achieve faster issue resolution, and drive ongoing performance improvements. A practical guide to configure ZBrain Monitor for apps and agents This section provides a practical walkthrough for configuring ZBrain Monitor across both apps and agents. You will also find insights on setting thresholds and selecting the right metrics to align monitoring with your operational goals. Let’s explore how to achieve precision monitoring in practice. How to configure a monitoring event for apps using ZBrain Monitor Step 1: Access the monitoring configuration To set up monitoring for an application: Access the app session: Navigate to the Apps page. Click on your desired application. Go to the query history section of the app. Select a specific user session from the list; details such as Session ID, user information, session time and prompt count are displayed for each session.
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  10/34 Review the conversation: Viewsession details and chat history for the selected user session. Configure monitoring events at the individual query level; each query within a conversation can have its dedicated monitoring event. Access conversation logs: Click ‘Conversation Log’ to see the interaction details of a specific query Review status, time, and token usage Check the input, output, and metadata
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  11/34 Enable monitoring: Click the‘Monitor’ button in the overview tab Click ‘Configure now’ when prompted with ‘Added for monitoring’
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  12/34 Step 2: Configureevent settings You will be redirected to the Events > Monitor page. In the last status column, click ‘Configure’ to open the event settings page. On the event settings screen: 1. Review entity information Entity name: Confirm the name of the application being monitored. For example, it is the HR Policy Query App Entity type: The type of entity being monitored (e.g., App) 2. Verify monitored content Monitored input: Review the query or prompt that will be evaluated. Monitored output: Confirm the corresponding response to be assessed.
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  13/34 3. Set evaluationfrequency Click the dropdown menu under “Frequency of evaluation” Select the desired interval (Hourly, Every 30 minutes, Every 6 hours, Daily, Weekly, or Monthly) for monitor event execution. 4. Configure evaluation conditions Click ‘Add metric’ in the Evaluation Conditions section Select a metric type. The following metrics are currently available for configuration, and additional options are being continuously added to enhance monitoring capabilities. Metric category Metric name Purpose Example
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  14/34 LLM- based metrics Response relevancy Checks how wellthe response answers the user’s question. Higher scores indicate better alignment with the query. Use for FAQ bots or chat assistants to validate on-topic answers. Faithfulness Measures whether the response accurately reflects the given context, minimizing hallucinations or incorrect information. Essential for RAG or context-driven LLM apps to prevent factual errors. Non- LLM metrics Health check Determines if the app/agent is operational and capable of producing a valid response; further checks halt on failure. Run at the start of every app/agent execution for operational monitoring. Exact match Compares theapp’s or agent’sresponse to the expected output for an exact character-by- character match. Use for deterministic output tasks (e.g., structured data extraction). F1 score Balances precision and recall to assess how well the response captures the expected content. Useful in QA, classification, or multi-label tasks with expected answers. Levenshtein similarity Calculates how closely two strings match, based on the number of edits needed to transform one into the other. Detects typos or near-matches in text or code outputs. ROUGE-L score Evaluates similarity by identifying the longest common sequence of words between the generated and reference text. Effective for summarization and paraphrase evaluation. LLM-as- a-judge metrics Creativity Rates how original and imaginative the response is in addressing the prompt. Use for brainstorming, content generation, or marketing copy. Helpfulness Assesses how well the response guides or supports the user in resolving their query. Evaluate customer support or advisory agent responses.
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  15/34 Clarity Measures howeasy the response is to understand and how clearly it communicates the intended message. Review for user- facing documentation or explanations. Select evaluation method: Choose how the metric should be applied (e.g., is less than, is greater than, or equals to). Set threshold value: Enter the appropriate threshold (between 0.1 and 5.0) for your metric. The threshold represents the cutoff point at which the monitoring event will trigger an alert or action. Example: For a response relevance metric, you might set a threshold of 1.0 with “is less than”—meaning the event will be flagged if relevance drops below 1.0. Additionally, if the metric is set to be less than 0.5, the evaluation of the event is marked as fail. Add metric: Click ‘Add’ to include the metric in your monitoring configuration. Set the “Mark evaluation as” dropdown to ‘Fail’ or ‘Success’
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  16/34 5. Configure notifications Togglethe ‘Send Notification’ option to enable alerting for this monitoring event. Click ‘+ Add a Flow from the Flow Library’. This opens the Add a Flow panel. In the panel, search for the desired notification flow and select it.
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  17/34 Click the Play▶️button to run a delivery test. Confirmation: If the flow succeeds, a “Flow Succeeded” message appears. Error handling: If the flow fails, you will see inline error messages and a link to “Edit Flow” for troubleshooting. 6. Test your configuration Test the setup: Click the ‘Test’ button, enter a test message if required, and review the test results. Reset option: Click ‘Reset’ to try again or adjust your configuration as needed.
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  18/34 7. Save yourconfiguration Click ‘Update’ to save and activate your monitoring event These structured steps enable you to establish robust monitoring for your AI applications, driving operational excellence and reliability. How to configure a monitoring event for agents using ZBrain Monitor This section provides a step-by-step walkthrough for configuring a monitoring event for an AI agent. For example, we will demonstrate how to set up comprehensive monitoring for the Article Headline Optimizer Agent, illustrating each stage of the process for clarity and actionable insights. Step 1: Access the monitoring setup To set up a monitoring event for an agent: Access the agent dashboard: Go to the Agents page and select the deployed agent (e.g., Article Headline Optimizer Agent). Enable monitoring: To activate monitoring for an AI agent, follow these steps: Open the full-screen view of ‘Agent Activity’ for your chosen agent execution. Click the ‘Monitor’ button associated with the relevant execution. When prompted, select ‘Configure Now’ to proceed with setting up the monitoring parameters.
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  19/34 Step 2: Configureevent settings When you click on configure ‘Now’, you will be redirected to the ‘‘Monitor’’ page. Click ‘Configure’ in the ‘Last Status’ column to open the Event Settings page. On the Event Settings screen, review the following settings: Entity Name and type: Agent name – Headline Optimizer Agent and type – Agent Monitored input (e.g., a prompt or document): This shows the input text/file details. Monitored output: This shows the response generated by the agent for the specified input.
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  20/34 After reviewing thesedetails, proceed to configure the key monitoring parameters for the event: Frequency of evaluation: Select how often you want the agent to be evaluated— choose from intervals such as hourly, daily, weekly, or every 30 minutes, etc., to match your operational needs. Metrics: Under Evaluation Conditions, use the “Add Metric” option to select from LLM-based, Non-LLM-based, or LLM-as-judge metrics. For example, response relevance and health check metrics are used in this execution. Combine multiple metrics using AND/OR concatenators as needed.
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  21/34 Once you selectthe evaluation conditions, set the threshold as per business needs. The optimal threshold values will differ by industry, application, and business priority. These practices are essential to set appropriate thresholds: Industry standards and regulatory requirements: Highly regulated sectors such as healthcare or finance typically demand stricter thresholds for accuracy, faithfulness, and reliability to protect users and comply with laws. For example, a financial AI solution handling loan approvals or fraud detection may require a near-perfect accuracy threshold, whereas a customer support chatbot can allow for more flexibility. Business objectives and risk tolerance: Consider what matters most for your use case: accuracy, recall, speed, or user satisfaction. If missing a positive case is unacceptable (e.g., financial fraud detection), set high recall thresholds even at the cost of increased false positives. For applications where rapid response or cost savings are critical (e.g., retail chatbots), optimize thresholds accordingly. Operational impact: Evaluate how threshold sensitivity affects operations. Excessively strict thresholds may result in frequent false alarms and alert fatigue, while loose thresholds may let serious issues slip through undetected. Iterative adjustment: Start with benchmark values based on industry norms or pilot studies (e.g., 0.7–0.9 for accuracy in critical domains) and refine over time using feedback from real-world monitoring data. Engage both technical and business stakeholders in this review process. By taking these considerations into account, you can define metric thresholds that both align with your organization’s goals and ensure your AI systems operate safely, efficiently, and reliably in production. After setting thresholds, set the Mark Evaluation as either ‘Success’ or ‘Fail’ Toggle the ‘Send Notification’ option to enable alerting for this monitoring event.
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  22/34 Add a notificationFlow: Click ‘+ Add a Flow from the Flow Library’. This opens the Add a Flow panel. Search for and select the appropriate notification flow that suits your alerting needs, such as Gmail alerts, Slack notifications, MS Teams or other available options, if any. Test your monitor event: Use the ‘Test’ button to validate your setup. Enter a test message if required and review the results to ensure correct behavior. If adjustments are needed, click ‘Reset’ to modify and retest as necessary. Save your configuration: Click ‘Update’ to save and activate your monitoring event. Once activated, the monitoring event runs automatically at the specified evaluation frequency, such as every 30 minutes, daily, or weekly.
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  23/34 After configuring metricsand other settings, you can test the monitor and check results as shown in the image below. By resetting the test message, you can test evaluation settings for multiple scenarios. Monitor logs Once monitoring is enabled, ZBrain captures a comprehensive activity record for every configured event, making it easy to analyze and troubleshoot agent behavior over time. Monitor Log for an event comprises the following components:
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  24/34 Event information header EventID: The unique identifier assigned to each monitoring event (e.g., Event ID: f9dab8). Entity name and type: Shows which agent is being monitored—for instance, the Article Headline Optimizer Agent with type as Agent. Frequency: Indicates how often the monitoring is performed (e.g., hourly, daily). Metric: Performance criteria being measured across selected metrics. Log status visualization Below, event information, status visualization uses color-coded bars to signal outcomes instantly: Colored bars provide a quick visual indicator of recent execution results Green for successful evaluations Red for failures This visual summary helps teams quickly spot trends or anomalies. Filtering options Status dropdown: Filter by All/Success/Failed/Error status Log time dropdown: Filter by active/inactive Log details table A detailed breakdown of each event with essential context: Log ID: Unique identifier for each log entry. E.g., f9dfe8, f9dfe1, etc. Log time/date: Time when the evaluation occurred LLM response: The output that LLM provides Credits: Credits used for each execution. Cost: Corresponding expense, deducted based on credits used. Metrics: Success/Fail results for each metric under review. Status: Final outcome, clearly color-coded for immediate clarity (Success/Failed/Error).
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  25/34 ZBrain Monitor isdesigned for enterprise environments and supports robust role-based user permissions. Administrators can assign specific access rights to users, ensuring that only authorized team members can view or configure monitoring events. Select Monitoring Event: From the Monitor page, select the desired monitoring event. Open User Management option: Navigate to the ‘User Management’ tab. Review Entity Details: A user management panel opens with the following elements: Entity name: Name of the agent. Entity type: AGENT Builder: A Builder is someone who can add, update, or use ZBrain knowledge bases, apps, flows, and agents. Select the builder you want to invite. The options include custom or everyone. When selecting the ‘Custom’ option, you can use the search field to search for builders to invite. Enter the user’s name and click the ‘Invite’ button to assign them the Builder role. User access: Once accepted, the user will see the assigned event in the main interface under the Monitoring tab upon login and will be able to manage it.
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  26/34 This role-based approachensures security, accountability, and flexible collaboration for large teams operating critical AI solutions deployments. By integrating ZBrain Monitor, enterprises ensure their AI solutions consistently meet defined standards for accuracy, performance, and business value. Streamline your operational workflows with designed to address enterprise challenges. Explore Our AI Agents Best practices for monitoring AI agents and applications Robust monitoring is essential for ensuring the accuracy, quality, and compliance of both AI agents and applications at scale. By leveraging ZBrain Monitor’s full capabilities, organizations can implement the following best practices to maximize oversight and drive continuous improvement: Align monitoring to business outcomes Define success criteria: Specify what constitutes “success” for each AI app and agent—whether it’s task accuracy, user satisfaction, compliance, or operational efficiency. When defining success criteria, use SMART(Specific, Measurable, Achievable, Relevant, Time-bound) goals to ensure each metric and KPI is actionable, relevant, and time-bound. For example, a customer support agent’s response time goal might be to reduce average response latency by 20% within a quarter. Map metrics to business impact: Select evaluation metrics (LLM-based, non- LLM-based, and LLM-as-a-judge) that reflect both technical and business priorities for each monitored entity. Utilize multi-layered metrics and establish baselines Comprehensive metric coverage: Monitor a range of metrics (quality, latency, cost etc.) for all apps and agents, providing a holistic view of entity behavior. Move beyond single-metric (LLM-based or non-LLM based) evaluation by employing composite or multi-objective metrics, ensuring agents and apps are assessed on quality, speed, and cost. Baseline comparisons: Regularly compare performance data against historical benchmarks or defined targets to identify improvements, degradations, or anomalies. Set and tune effective thresholds Context-aware thresholds: Start with conservative thresholds and refine them based on real-world usage and the risk profile of each application or agent.
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  27/34 Criticality-based segmentation: Setmore stringent thresholds (e.g., higher accuracy) for business-critical or customer-facing apps and agents. For internal or less critical use cases, thresholds can be adjusted as needed to strike a balance between resource usage and business risk. Continuously refine metrics and thresholds Iterative improvement: Revisit your metrics and thresholds regularly, especially after major product updates, scaling events, or as your understanding of business impact matures. Stakeholder feedback: Incorporate input from both technical and business teams to ensure metrics remain aligned with operational and strategic goals. Blend automated and human-in-the-loop evaluation Human oversight for subjective criteria: Supplement automated metrics with regular human review, particularly for subjective factors such as clarity, tone, or regulatory risk. User feedback integration: Collect and utilize user feedback on both apps and agents to continuously improve outcomes. Drive iterative improvement through regular review Periodic data reviews: Establish a schedule for reviewing monitoring results and updating evaluation strategies. Evolve metrics and policies: Add or revise metrics as business needs and AI system roles evolve; adjust thresholds based on observed trends. Maintain comprehensive monitoring documentation Policy documentation: Maintain detailed records that outline what is monitored, which metrics are tracked, how those metrics are calculated, and the rationale behind the selected thresholds. Response playbooks: Document escalation paths, notification recipients, and clear, actionable steps for responding to different types of incidents or alerts. Regular reviews: Periodically update documentation to reflect system changes, new regulatory requirements, or evolving business needs. Track credits and cost transparency Monitor credit usage: Ensure that every execution for a monitoring event, whether by an AI agent or application, logs the exact number of credits consumed. Tracking credit consumption at the session and query level helps organizations understand usage patterns and optimize allocation. Enable cost visibility: Provide real-time visibility into the cost associated with each monitored event. Make it easy for stakeholders to view cost breakdowns, both per query and in aggregate, directly from the monitoring interface.
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  28/34 Set budget andusage alerts: Implement thresholds or automated alerts for credit usage and overall spending. E.g., ZBrain Monitor can support automated alerts for cost-specific metrics. Notify teams proactively if usage approaches or exceeds predefined limits to prevent cost overruns. Foster a monitoring-first culture Training: Educate teams on the importance of monitoring, how to interpret metrics, and the process for responding to alerts. Transparency: Make monitoring dashboards, alert histories, and performance reports available to all relevant stakeholders. Accountability: Assign clear ownership for each monitored entity and for maintaining the health of the monitoring infrastructure itself. Key benefits of monitoring AI agents and applications with ZBrain Monitor Effective monitoring isn’t just about compliance or operational health; it unlocks business value across multiple dimensions. Here’s how ZBrain Monitor delivers measurable benefits: Actionable performance insights ZBrain Monitor offers real-time visibility into agent and app performance through metrics such as response relevance, faithfulness, and health checks. For example, if an agent’s relevance score drops below a set threshold, teams can immediately investigate and resolve the issue before it affects business outcomes. Faster troubleshooting and root-cause analysis Granular, query-level monitoring enables teams to quickly pinpoint the cause of errors or suboptimal performance, reducing investigation time and accelerating resolution. Stronger alignment with business objectives By aligning monitoring metrics with business KPIs, organizations ensure that their AI systems remain focused on delivering measurable value, whether it’s operational efficiency, risk reduction, or enhanced customer experience. Enhanced debugging and troubleshooting By logging detailed execution traces, including inputs, outputs, and metric outcomes, ZBrain Monitor makes it simple to isolate root causes. For example, if an agent’s F1 score or Levenshtein similarity consistently falls short for specific input types (like PDF files), the team can review the logs, replicate the scenario, and roll out targeted fixes. Elevated user experience
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  29/34 Monitoring satisfaction andrelevance metrics helps product owners identify when AI- generated responses become less helpful or clear, triggering timely improvements. In customer-facing scenarios, maintaining high clarity and helpfulness scores ensures continued trust and engagement. Greater agility and innovation With visibility into performance and the ability to iterate quickly, product teams can launch, test, and refine new AI-powered features with greater confidence. Early detection of issues accelerates feedback cycles, reducing the risk and cost of failed experiments. Improved customer trust and retention Consistent, high-quality AI interactions lead to enhanced end-user experiences. When customers see that your applications deliver reliable, accurate, and timely responses, their trust and loyalty to your brand increase, directly impacting retention and revenue. With ZBrain Monitor, organizations achieve real-time, automated oversight that drives higher performance, faster innovation, and ongoing business value from every AI solution deployment. Endnote As AI agents and applications rapidly reshape enterprise operations, robust monitoring is not just a technical necessity; it is a driver of business resilience and competitive edge. ZBrain’s monitoring module empowers organizations to move beyond surface-level oversight, delivering automated, granular, and actionable intelligence for every AI agent and application. By enabling real-time performance tracking, accelerating root-cause resolution, and aligning AI outcomes with business KPIs, ZBrain transforms AI from an experimental tool into a reliable, scalable business asset. In today’s landscape, embracing advanced monitoring is essential to unlock the full value and longevity of your AI investments. Ready to unlock the full potential of AI agents and applications? Explore the to build, deploy, and continuously monitor enterprise-grade AI solutions with seamless integration, real-time visibility, and actionable insights at every step. Listen to the article Author’s Bio 0:00 0:00 / 38:25 / 38:25
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  30/34 Akash Takyar CEO LeewayHertz Tableof content Frequently Asked Questions What is AI agent/application monitoring, and why is it critical for enterprises? AI agent and application monitoring is the systematic process of tracking, evaluating, and analyzing the real-time behavior, responses, and performance of AI-powered systems in production. Unlike basic uptime monitoring, it includes a granular evaluation of response quality, operational health, compliance, and cost efficiency. For enterprises, this practice is crucial because AI agents and applications frequently manage complex, business-critical workflows that demand both trustworthiness and transparency. Proactive monitoring enables the early detection of errors, operational bottlenecks, and compliance risks, while facilitating continuous optimization and accountability for business outcomes.
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