Lecturer
Annie Freeman is a Developer Advocate at Coralogix, specializing in full-stack observability platforms and the responsible deployment of AI applications. With a background in green software practices and a focus on sustainability in technology, Annie explores how visibility into AI systems can address challenges related to cost, ethics, and operational reliability.
Abstract
The rapid adoption of AI systems, particularly those involving large language models and agentic workflows, introduces significant complexities around trust, resource consumption, and ethical behavior. Traditional monitoring approaches often prove insufficient for these dynamic environments. Annie Freeman examines how observability, implemented through OpenTelemetry, can establish robust systems of trust around AI applications. By analyzing four distinct layers of observability—from development tools to quality monitoring—the discussion highlights practical strategies for instrumenting AI workloads, detecting issues such as hallucinations or policy violations, and implementing real-time guardrails. These insights enable organizations to build AI solutions that are not only performant but also accountable and sustainable.
The Fundamental Challenge: Why Traditional Monitoring Falls Short for AI
AI systems differ fundamentally from conventional software in their non-deterministic nature. The same input can produce varying outputs, agentic loops may execute unpredictable numbers of tool calls, and decision-making processes remain opaque. This unpredictability creates multiple layers of risk: potential harm from inappropriate responses, escalating operational costs from uncontrolled resource usage, and difficulties in capacity planning due to variable inference demands.
Users require consistent and reliable experiences. Company leadership must ensure investments yield clear business value without runaway expenses. Developers, increasingly reliant on AI coding assistants as production dependencies, need confidence in the generated outputs. Traditional metrics focused on uptime or basic performance fail to capture these nuances. Without targeted observability, teams operate with limited visibility into model behavior, making it impossible to verify ethical alignment or optimize resource utilization effectively.
Establishing Foundational Observability: Development and Operational Layers
Observability begins at the development stage, where AI coding tools such as Claude Code or CodeWhisperer generate substantial portions of application logic. These tools emit OpenTelemetry data natively, providing metrics on token usage, cost per session, model selection patterns, and code acceptance rates. Such visibility transforms subjective assessments of tool effectiveness into data-driven insights, enabling teams to optimize developer productivity and identify which models deliver the highest value for specific tasks.
Operational metrics extend this foundation into production environments. Key signals include token consumption trends, model invocation patterns, and response finish reasons. These indicators function analogously to HTTP status codes, revealing whether completions result from natural termination, length limits, or other constraints. High-spending users or unusual patterns, such as excessive retry loops, become immediately apparent. Organizations can then implement targeted optimizations, such as adjusting model sizes for specific use cases or imposing limits on tool call iterations.
The unified nature of OpenTelemetry ensures that AI telemetry integrates seamlessly with existing application monitoring. This avoids data silos and enables comprehensive system analysis. Teams gain the ability to correlate AI behavior with broader application performance, facilitating more informed architectural decisions.
Enhancing Decision Transparency and Real-Time Protection
Decision tracing provides critical context for understanding not just what an AI system produces but why it arrived at particular conclusions. By instrumenting agentic loops with custom spans, teams can capture detailed information about each step: input validation, prompt construction, tool selection, and reasoning chains. This granular visibility transforms black-box operations into auditable processes.
OpenTelemetry’s semantic conventions standardize the collection of this data, ensuring consistency across different AI workloads. Traces reveal the complete journey of a request, from initial user input through multiple reasoning iterations to final output. Such transparency supports debugging, compliance requirements, and continuous improvement efforts.
Quality monitoring introduces an additional safeguard layer. Small language models serve as specialized evaluators, analyzing outputs for hallucinations, toxicity, policy violations, or relevance issues. These evaluators operate with high accuracy due to their focused training, providing rapid feedback without the latency of larger models. When combined with guardrails, this approach enables real-time intervention. Suspicious inputs or outputs can be blocked before reaching users, maintaining system integrity and user trust.
Practical Implementation and Long-Term Benefits
Implementing these observability layers requires intentional design but yields substantial returns. OpenTelemetry’s vendor-neutral approach prevents lock-in while leveraging existing infrastructure investments. Teams can begin with basic instrumentation and progressively add sophistication as needs evolve.
The framework supports multiple stakeholder requirements simultaneously. Users benefit from consistent, safe interactions. Leadership gains visibility into costs and value delivery. Developers receive actionable insights for refining both AI components and their integration with business logic.
As AI adoption accelerates, observability becomes the cornerstone of responsible deployment. Systems built with comprehensive monitoring demonstrate greater reliability, ethical alignment, and operational efficiency. The investment in observability infrastructure pays dividends through reduced incidents, optimized resource usage, and enhanced organizational confidence in AI capabilities.
By treating observability as integral to AI system design rather than an afterthought, teams can move beyond experimental prototypes toward production-grade solutions that earn and maintain user trust.
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