Turn LLM traces, evals, and costs into a debuggable control plane

An AI observability platform becomes a control plane when traces, evals, cost, latency, safety, and user feedback drive prompt, routing, caching, and rollback decisions. The trade-off is instrumentation overhead against production control: you pay for tracing, evals, and cost accounting, and you get the ability to reroute, cache, roll back, or change prompts before users notice. A request's cost line is input tokens plus output tokens plus cache-creation tokens plus cache-read tokens; a dashboard that only shows the total hides the lever.

Traces are the minimum viable control plane

Without a trace, an agent failure is a guess. You see a bad answer, a timeout, or a user complaint, but you cannot tell whether the model, the prompt, a tool call, or the retry logic caused it. Tracing is the baseline for LLM monitoring because it preserves the sequence of work.

groundcover extended its AI Observability product to agentic AI systems and Google Vertex AI. The update added agent trace visibility that shows the full execution path for each model call and tool invocation, with arguments, results, and reasoning. The trace turns a production incident into a diff: compare the failing path against a known-good path and isolate the changed span.

Datadog's Agent Observability covers AI agents and LLM applications, with performance, quality, security, and cost views. Gartner named Datadog a Leader in the 2026 Gartner Magic Quadrant for Observability Platforms. New Relic released AI Coding Observability, an open-source monitor for AI-assisted software development workflows. The vendor signal matters less than the operational shape: AI workloads are being folded into the same monitoring surface as other services.

Cost attribution changes the routing decision

Token cost is not a single line item. Prices for input, output, cache creation, and cache reads can differ by model, provider, and tier. Total spend hides the decision.

groundcover splits span-level token spend into input, cache-creation, and cache-read costs. The separation makes caching a control action. If a prompt prefix is expensive to create but cheap to read, route repeated work to a cached path. If a tool call adds a large context window, shrink it or move it to a cheaper model.

Tracing and evals add overhead. The overhead is justified only when the output changes behavior: routing, cache policy, model selection, or prompt version. If the data only explains yesterday, the next request still pays the same cost.

Evals, safety, and feedback close the loop

LLM evals are the test harness for production behavior. They should score the same traces used for debugging, so a regression in answer quality, tool use, or safety appears next to the latency and cost that caused it. Safety checks belong in the same loop: a prompt that improves eval score but increases refusal or leakage is a failure, not a win.

Latency SLOs make the loop concrete. Set a target for the user-facing response, then set targets for the agent steps that compose it. When a step exceeds its SLO, the system should have a named action: reroute to a faster model, drop a nonessential tool call, use a cached result, or roll back a prompt change.

User feedback is the last sensor. A thumbs-down, a correction, or a support ticket should attach to the trace. The attachment lets you decide whether the issue is model quality, prompt wording, tool output, or routing. The control plane then chooses the next move: reroute, cache, rollback, or change the prompt.

• Trace every model call and tool invocation, including arguments, results, and reasoning path.
• Attribute input, cache-read, and cache-creation token cost at the span level.
• Score evals and safety on the same trace, not in a separate report.
• Set latency SLOs for user-facing paths and agent steps.
• Close the loop with user feedback, then reroute, cache, rollback, or change prompts.