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.