# Agent Evaluation vs Prompt Evaluation: When Braintrust Isn't Enough

Prompt evaluation tools like Braintrust excel at scoring model outputs. Production agents need trajectory evals on real tasks: here's how to know when to add sandboxed agent testing.

Source: https://www.agentclash.dev/blog/agent-evaluation-vs-prompt-evaluation-braintrust
Markdown export: https://www.agentclash.dev/md/blog/agent-evaluation-vs-prompt-evaluation-braintrust
Published: 2026-06-11
Author: Atharva

Braintrust is a strong fit when the unit you evaluate is a prompt, a dataset row, or a logged model response. That workflow breaks when the product you ship is an agent: multi-turn, tool-using, stateful, and sensitive to sandbox conditions.

This post is not a product dunk. It is a workflow map. Many teams should keep Braintrust for prompt iteration and add a separate layer for agent release gates.

## What prompt evaluation answers well

Prompt evaluation answers: *given this input, is the model's output acceptable?*

That is the right question for:

- copy and classification tasks
- RAG answer quality on fixed contexts
- prompt regression during model upgrades
- scoring functions over traces you already log

See [LLM agent evaluation](https://www.agentclash.dev/llm-agent-evaluation) for how that layer differs once tools enter the picture.

## What changes when you ship an agent

Agents answer a different question: *can this system complete the job under policy, budget, and tool constraints?*

That requires evidence prompt evals usually do not capture:

- tool call sequences and retries
- files and artifacts the agent created or edited
- latency and cost per successful task
- failures that happen mid-trajectory, not in the final string

Two runs can share a "good" final answer while one violated your refund policy in step three. Prompt graders miss that unless you reconstruct the whole path.

## When Braintrust is enough

Stay prompt-first when:

- your surface is mostly single-turn generation
- tools are thin wrappers around one API call
- humans review every high-risk output before it ships
- your regression signal is text quality, not operational behavior

For a deeper side-by-side on workflow fit, read [AgentClash vs Braintrust](https://www.agentclash.dev/compare/agentclash-vs-braintrust).

## When to add agent evaluation

Add sandboxed agent evaluation when:

- the agent edits code, tickets, or customer records
- you need same-tools comparison between models or harnesses
- CI should block a release when behavior regresses
- compliance asks for replay evidence, not screenshots of scores

That is the gap [agent evals](https://www.agentclash.dev/agent-evals) and the [agent evaluation platform](https://www.agentclash.dev/platform/agent-evaluation) are built for: frozen workloads, head-to-head races, replay, and gates.

## A practical split for platform teams

| Layer | Question | Typical owner |
| --- | --- | --- |
| Prompt eval (e.g. Braintrust) | Is this output good on logged inputs? | App / ML team |
| Agent eval (e.g. AgentClash) | Does the agent complete the task safely and repeatably? | Platform / release committee |

Run both. Do not ask prompt eval to stand in for release gates on tool-using agents.

Book a discovery call from any post footer, or start the [enterprise rollout](https://www.agentclash.dev/enterprise) if you want self-serve product access first.