Agent observability gap
- Observability vendors are beginning to add agent-specific tracing, but capabilities are still early and inconsistent. - Groundcover added agentic AI tracing and other vendors are positioning AI-native observability for structured agent telemetry. - The vendor activity shows intent to make tool calls, planner steps, and execution traces first-class telemetry for debugging and governance (siliconangle.com).
AI agents are getting their own monitoring tools, but the market is still stitching together basic ways to see what those systems actually did. (siliconangle.com) An agent is software that does work in steps: it plans, calls tools, keeps state, and hands tasks across models or services instead of answering with one model response. OpenAI’s current Agents SDK describes agents that “plan, call tools, collaborate across specialists, and keep enough state to complete multi-step work.” (developers.openai.com) That changes what has to be monitored. OpenAI’s tracing docs say an agent run can include model generations, tool calls, handoffs, guardrails, and custom events, which is a much richer record than a standard application error log. (openai.github.io) Groundcover said on April 22 that it added native support for agentic artificial intelligence systems compatible with Google Vertex AI, and said the feature is available automatically to customers at no extra cost. The company said teams can trace every large language model interaction and tool execution inside their own cloud environment. (siliconangle.com) The company framed the problem as scale, not just visibility. Groundcover’s vice president of product, Orr Benjamin, told The New Stack that teams can end up with two-hour sessions and 50,000 tool calls, which makes short traces and basic logs hard to use for debugging. (thenewstack.io) The standards layer is still moving underneath all of this. OpenTelemetry’s generative artificial intelligence semantic conventions remain in “development” status, and the spec now explicitly includes agent spans alongside model spans, metrics, and events. (opentelemetry.io) Vendors are filling that gap with their own platforms. Arize says its platform offers agent tracing, evaluation, and monitoring powered by OpenTelemetry, while Langfuse says its hierarchical traces capture every model call, tool invocation, and retrieval step and that it processes more than 10 billion observations a month. (arize.com, langfuse.com) The pitch is shifting from “is the service up” to “what did the agent decide.” Microsoft’s guidance on observability for generative and agentic AI says traditional uptime, error, and latency signals are too narrow for systems whose behavior can change with prompts, retrieval context, tool outputs, and policy decisions. (learn.microsoft.com) That push lines up with the products arriving above the stack. OpenAI introduced ChatGPT workspace agents on April 22, expanding the number of systems that can take actions across tools and teams and increasing the need for execution traces that can be inspected after the fact. (siliconangle.com) What is emerging is not one settled category but a race to make planner steps, tool use, and execution history into first-class telemetry. The demand is already here; the shared schema for recording it is still catching up. (opentelemetry.io, siliconangle.com)
