AI agents complicate observability: the same request can lead to different chains of actions. Without tracing, the system becomes opaque.
The problem manifests when generative systems transition from simple LLM calls to agents. An agent plans steps, invokes tools, and makes decisions dynamically. Behavior becomes non-deterministic: the same prompt can result in different call sequences and varying costs. Traditional APM captures latency and infrastructure but does not explain why the agent chose a specific path. As a result, incident diagnostics turn into guesswork.
The proposed solution is to extend observability to the agent level. The approach is built around OpenLIT — an SDK with native OpenTelemetry support – and Grafana Cloud as the visualization system. The key idea: treat each agent step as part of a distributed trace. This allows linking reasoning, tool calls, and the final response into a single chain. The trade-off is obvious: the volume of telemetry and the complexity of its analysis increase, but in return, causal transparency is gained.
The implementation relies on automatic instrumentation. OpenLIT is embedded alongside the agent framework (e.g., CrewAI, LangChain, OpenAI Agents, AutoGen) and does not require manual span creation. After initialization, the SDK captures:
- agent planning steps
- tool calls
- model invocations
- token usage and errors
These data are sent as traces and metrics via OpenTelemetrydi — rectly to Grafana Cloud or through the OpenTelemetry Collector. Pre-configured dashboards are used on the Grafana side. They aggregate latency, error rate, throughput, token usage, and cost. Additionally, agent-level entities are captured: agent name, actions, call sequence. This turns observability into a feedback loop rather than just monitoring.
The result is more detailed diagnostics of system behavior. It is possible to see exactly which step led to an error or cost increase, and how the agent arrived at a decision. This is especially important for production workloads where agent behavior is difficult to reproduce. Specific numerical improvements are not provided in the source material, but the qualitative effect is a reduction in uncertainty during incident analysis and the ability to optimize agent action chains.