Enterprise-grade agent observability requires four layers that don't exist in traditional monitoring: reasoning traces, behavioral baselines, cognitive drift detection, and cost attribution.

Reasoning traces capture not just what the agent did but why it did it. A reasoning trace records the chain from input to output: what context was retrieved, how the prompt was constructed, what the model returned, what tool calls were made, and how the agent arrived at its final response. This is the agent equivalent of distributed tracing in microservices. Without it, debugging an incorrect agent output requires guessing which step in the chain went wrong.

OpenTelemetry is emerging as the standard for agent telemetry, extending its existing trace and span model to cover LLM calls, retrieval operations, and tool invocations. The GenAI semantic conventions in OpenTelemetry define standardized attributes for model name, token counts, temperature settings, and completion content. Organizations that instrument their agents with OpenTelemetry today gain compatibility with the growing ecosystem of observability platforms (Langfuse, Arize, LangSmith, Datadog's LLM Monitoring) while maintaining the ability to query raw trace data for custom analysis.

Behavioral baselines establish what "normal" looks like for each agent. An agent's baseline includes its typical response patterns (length, tone, confidence level), its tool use patterns (which tools it calls, how frequently, in what order), its resource consumption (tokens per interaction, cost per task, latency distribution), and its output quality (accuracy rates, user satisfaction, escalation frequency). The baseline is not static. It should update as the agent's workload, data sources, and model versions change. But updates should be controlled: a sudden shift in tool use patterns should trigger an investigation, not an automatic baseline adjustment.

Cognitive drift detection monitors whether an agent's behavior is changing in ways that weren't caused by intentional updates. Four types of drift matter in production. Goal drift occurs when the agent's outputs start optimizing for something other than its intended objective. A sales support agent might drift toward longer, more detailed responses that impress in demos but slow down actual sales conversations. Context drift occurs when the underlying data sources change in ways that shift the agent's behavior. A knowledge base update that introduces contradictory information can cause an agent to produce inconsistent outputs. Reasoning drift occurs when model updates or prompt changes subtly alter the agent's decision patterns. A model provider's silent update (a common occurrence) can change how the agent weighs competing information. Collaboration drift occurs when changes in connected systems or other agents alter the agent's operating environment. An API change in a downstream system that modifies response formats can cause an agent to misparse tool outputs. Learn more

Cost attribution tracks resource consumption per agent, per team, per use case, and per interaction. Without cost attribution, AI spend is a single line item that grows opaquely. With it, you can identify which agents are cost-efficient, which are consuming disproportionate resources, and where routing optimizations would yield the greatest savings. Cost attribution also enables chargeback models where business units pay for the agent resources they consume, creating natural cost discipline across the organization.