Datadog vs Dynatrace for AI agents: compare the investigation workflow

Datadog and Dynatrace both document AI-agent observability and production evaluation. A useful comparison starts with a failure your team needs to investigate, rather than assuming either product only monitors infrastructure.

This is a Sentrial-authored documentation review, checked November 1, 2026. We have not run a controlled product benchmark or measured either vendor’s detection accuracy.

What their documentation establishes

Datadog documents managed evaluators, custom LLM-as-a-judge evaluators and user feedback. Its custom evaluation documentation describes span, trace and session scopes. Confirm site, provider and integration compatibility for your deployment.

Dynatrace documents agent tracing, online LLM-as-a-judge evaluation and results linked to source traces. Its platform also correlates AI telemetry with the surrounding application and infrastructure. Documentation demonstrates supported workflows; it does not establish accuracy on your application.

Use one incident to compare both

Consider a synthetic support agent that says a replacement was dispatched. The shipping tool actually returned queued, and the carrier has not issued a dispatch receipt. The HTTP request succeeded, but the business action is unresolved.

Give each product the same messages, tool arguments, tool results and later events. Check whether an engineer can locate the exact replacement, distinguish queue acceptance from dispatch, and see which evidence supports the evaluator’s decision. Then add a real receipt and repeat the review. A later successful action for another order must not resolve the original one.

Record the tradeoffs

• How much instrumentation is required to join asynchronous receipts to the originating action?
• Can a reviewer inspect the evaluator version, evidence and original result after a correction?
• Which eligible traces were evaluated, skipped, truncated or still pending?
• What are the ingestion, retention, evaluator and review costs for your actual workload?

Start with the platform your team already operates, then measure whether a dedicated agent workflow improves investigation enough to justify another system. Apply the same acceptance test to Sentrial. Our current semantic evaluation uses general models; this article makes no claim of proprietary-model superiority or universal traffic coverage.

For the underlying evidence design, read connecting agent traces to business outcomes. For the broader selection process, see the agent observability buyer’s guide.