Ascent AI Agent Monitoring

Ascent AI & LLM Observability Deployment Model

The guide will enable monitoring, analyzing, and optimizing the performance, cost, security, and quality of your AI-driven applications. Unlike traditional observability, which focuses on infrastructure and application performance, AI observability with Ascent extends to:

  • Model Performance – Tracking inference latency, response times, and failure rates.

  • Full Workflow Tracing – Understanding how data flows through AI pipelines using distributed tracing.

  • Cost & Resource Efficiency – Optimizing GPU/CPU usage, API call expenses, and token consumption.

  • Security & Compliance – Detecting data leakage, unauthorized access, and enforcing privacy policies.

  • Response Quality – Evaluating model accuracy, bias, hallucinations, and user engagement.

With AI models becoming core to modern applications, observability ensures reliability, efficiency, and trust in AI-driven decision-making.

Key use cases for AI and LLM Observability

The primary Ascent use cases for AI and LLM observability include the following:

1. Service Health & Performance Monitoring – Similar to traditional APM solutions but tailored for AI workloads, ensuring model inference times and API performance are stable.

2. Full Application Workflow Tracing – Distributed tracing helps identify bottlenecks across AI pipelines, which is crucial for debugging complex LLM applications.

3. LLM Stack Cost Analysis – A key differentiator, focusing on managing compute, API, and infrastructure costs related to AI workloads.

4. Safeguarding LLM and User Data – Likely involves monitoring data leakage, security policies, and compliance.

5. AI Response Quality – A major area of concern in AI observability, ensuring that model responses are accurate, relevant, and unbiased.

Key metrics

Metrics for each of Apica Ascent’s AI & LLM Observability use cases are as follows:

1. Service Health & Performance Monitoring - Ensuring AI services are running efficiently and reliably. Key Metrics:

  • Latency (P50, P90, P99 response times)

  • Throughput (Requests per second)

  • Error Rates (HTTP 4xx/5xx, model failures)

  • Infrastructure Utilization (CPU, GPU, memory usage)

  • Model Load & Inference Time

2. Full Application Workflow Tracing (via Distributed Tracing) - Tracing LLM interactions across different system components. Key Metrics:

  • Trace Duration (End-to-end execution time)

  • Span Latencies (Delays in different parts of the pipeline)

  • Dependency Health (Performance of databases, APIs, vector stores)

  • Failure Points (Where requests drop in the workflow)

  • Token Flow Analysis (How tokens are processed across requests)

3. LLM Stack Cost Analysis - Optimizing costs for model inference and resource consumption. Key Metrics:

  • Compute Costs (GPU/CPU usage per request)

  • API Call Costs (Cost per token for third-party LLMs)

  • Memory Utilization Efficiency

  • Cost Per Query (Total cost per processed request)

  • Idle vs. Active Compute Utilization

4. Safeguarding LLM and User Data - Ensuring compliance, security, and privacy. Key Metrics:

  • Personally Identifiable Information (PII) Detection Rate

  • Unauthorized Access Attempts

  • Data Leakage Incidents

  • Compliance Adherence (SOC2, GDPR, HIPAA)

  • Input/Output Sanitization Effectiveness

5. AI Response Quality - Evaluating the accuracy, relevance, and fairness of AI-generated responses. Key Metrics:

  • Response Accuracy (Ground Truth Comparison)

  • Toxicity Score (Bias/Fairness Evaluation)

  • Response Consistency (Same input = Same output?)

  • Hallucination Rate (Fact-checking %)

  • User Engagement & Satisfaction Scores

Each of these aligns well with AI and LLM observability trends.

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