LLM Evals vs Agent Sandboxes: What Each One Actually Catches

What evals catch

Eval platforms shine on a specific set of problems. Braintrust's 2026 overview is a good reference for the current state of the bucket.

• Output-quality regression on a curated test set. When a prompt change or model upgrade causes answer quality to drop on known inputs, an eval catches it and fails CI.
• Model-to-model comparison. Which model should you use for this feature? Claude, GPT, or Gemini? Evals give you a scorecard across your own tasks.
• Prompt-change regression. Changing a system prompt is a regression risk. Evals run the new prompt against the reference set and show where it got worse.
• Cost and latency per call. Evals track tokens and time per scorer, giving you a ruler for tradeoffs.
• Scorer signals. Faithfulness, helpfulness, groundedness, and other per-response scorers run at scale.

If your problem is any of the above, evals are the right layer.

What evals do not catch

Evals stop where the reference set stops and where the output is the thing being scored.

• Wrong-path tool calls that still produce correct outputs. The agent used to make two tool calls. After a prompt tweak it makes five, and the final answer is still correct. The eval passes. Your token bill does not.
• Drift on live production traffic. Your test set is frozen. Production traffic moves. Uptime Robot's 2026 monitoring guide breaks drift into semantic, response-distribution, and retrieval drift. None of those trip a single eval.
• Hallucination inside multi-step workflows. Step 3 fabricates a value. Step 4 accepts it. Step 7 produces a coherent final output. If your eval tests any single step in isolation, all of them pass.
• Boundary violations that emerge from unanticipated combinations. 80% of teams running production agents have seen this happen. Eval suites test what the author wrote. Boundary escapes live in combinations nobody wrote a test for.

These are not "bad evals." They are a structural gap in what an answer-scoring layer can see.

What sandboxes catch

A sandbox runs the agent in an isolated environment that mirrors production and records everything. Paragon is built for this and checks the following on every run.

• Tool-call trajectory regressions. Which tools did the agent call, in what order, with what arguments. The sandbox compares to prior successful trajectories and flags a new version that takes a different or longer path, even if the final output matches.
• Drift against rolling production baselines. Live traffic slices replay through the sandbox continuously. When the distribution of agent actions shifts, the sandbox surfaces the delta before quality drops to user-visible levels.
• Multi-step workflow correctness. Workflows replay end to end with injected faults, edge cases, and adversarial inputs. Hallucinations propagating across steps get caught because the sandbox checks the full trace, not just the endpoint.
• Policy enforcement at tool-call time. Teams declare what the agent is allowed to do. The sandbox verifies every call. Microsoft's 2026 Foundry guidance covers this pattern; sandboxes enforce it.
• Regression detection via production-trace replay. The strongest signal. Real traffic from production replays through a proposed new agent version; the sandbox compares behavior and flags anything that diverges.

What sandboxes do not catch

Sandboxes are not where you run model comparison benchmarks or quality scorers on fixed reference sets.

• Raw output quality on curated test sets. Evals have a three-year head start on this. Don't try to replicate it in a sandbox.
• Single-turn comparison between model checkpoints. Again, eval territory.
• Deep scorer libraries. Eval platforms have large open-source scorer ecosystems (DeepEval, Ragas, custom metrics). Sandboxes complement those rather than compete with them.

If your problem is "is GPT-5 better than Claude on my RAG pipeline," reach for an eval.

When to use which

How teams run both together

A typical 2026 setup looks like this.

An eval platform sits in CI for model and prompt changes. Every prompt PR triggers the eval suite; failing a scorer blocks merge. The eval platform owns the "is the answer good on known inputs" question.

A sandbox sits between merge and deploy. When an agent version is about to ship, the sandbox replays a representative slice of recent production traffic through the new version, compares behavior to the current production version, and either gates the deploy or issues a report explaining what changed. The sandbox owns the "is the behavior correct on real traffic" question.

An observability tool watches what happens in production and catches drift or incidents post-release.

Three layers, three questions, three jobs. The failure mode is trying to answer "is the behavior correct on real traffic" with an eval platform. It is not what evals are built for.