Agent Evaluation Readiness Checklist
📰 LangChain Blog
A practical checklist for agent evaluation, covering error analysis, dataset construction, and production readiness
Action Steps
- Manually review 20-50 real agent traces before building eval infrastructure
- Define unambiguous success criteria for a single task
- Separate capability evals from regression evals
- Assign eval ownership to a single domain expert
- Rule out infrastructure and data pipeline issues before blaming the agent
Who Needs to Know This
This checklist is beneficial for AI engineers, data scientists, and product managers working on agent development, as it provides a step-by-step guide for building, running, and shipping agent evaluations
Key Insight
💡 Start with simple evaluations that give signal and add complexity only when necessary
Share This
🚀 Improve your agent's performance with a practical evaluation checklist! #AI #AgentEvaluation
Key Takeaways
A practical checklist for agent evaluation, covering error analysis, dataset construction, and production readiness
Full Article
Published Time: 2026-03-27T14:00:00.000Z
# Agent Evaluation Readiness Checklist
[Skip to content](https://blog.langchain.com/agent-evaluation-readiness-checklist/#main)
[](https://blog.langchain.com/)
* [Website](https://www.langchain.com/)
* [Docs](https://docs.langchain.com/)
* [Case Studies](https://blog.langchain.com/tag/case-studies/)
* [Harrison's In the Loop Series](https://blog.langchain.com/tag/in-the-loop/)
* [Try LangSmith](https://smith.langchain.com/)
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# Agent Evaluation Readiness Checklist
A practical checklist for agent evaluation: error analysis, dataset construction, grader design, offline & online evals, and production readiness.
17 min read Mar 27, 2026
_By Victor Moreira, Deployed Engineer @ LangChain_
This checklist is a practical companion to ["Agent Observability Powers Agent Evaluation"](https://blog.langchain.com/agent-observability-powers-agent-evaluation/), which covers _why_ agent evaluation is different from traditional software testing, introduces the core observability primitives (runs, traces, threads), and explains how they map to evaluation levels. **Read that post first if you're new to agent evaluation.**
This post focuses on the **_how,_** a step-by-step checklist for building, running, and shipping agent evals.
**Start with the simplest eval that gives you signal.** A few end-to-end evals that test whether your agent completes its core tasks will give you a baseline immediately, even if your architecture is still changing. Only add complexity when you have evidence that simpler approaches are missing real failures.
👉
Don’t care for the deep dive? Skip to [the full checklist](https://blog.langchain.com/agent-evaluation-readiness-checklist/#the-full-checklist).
## Before you build evals
0:00
/0:42
1×
Use LangSmith to go from traces to the annotation queue to datasets & experiments
**☑️ Manually review 20-50 real agent traces before building any eval infrastructure**
**☑️ Define unambiguous success criteria for a single task**
**☑️ Separate capability evals from regression evals**
**☑️ Ensure you can identify and articulate why each failure occurs**
**☑️ Assign eval ownership to a single domain expert**
**☑️ Rule out infrastructure and data pipeline issues before blaming the agent**
### Deep dive
#### Manually review 20-50 real agent traces before building any eval infrastructure
Use LangSmith to go from traces to the annotation queue to datasets & experiments.
Before building any infrastructure, spend 30 minutes reading through real agent traces. You'll learn more about failure patterns from this than from any automated system. LangSmith's [traces](https://docs.langchain.com/langsmith/observability?ref=blog.langchain.com) and [annotation queues](https://docs.langchain.com/langsmith/annotation-queues?ref=blog.langchain.com) are excellent for this.
#### Define unambiguous success criteria for a single task
If two experts can't agree on pass/fail, the task needs refinement:
* _Unclear success:_ "Summarize this document well."
* _Clear success:_ "Extract the 3 main action items from this meeting transcript. Each should be < 20 words and include an owner if mentioned."
#### Separate capability evals from regression evals
You need both because they serve different purposes. Capability evals push your agent forward by measuring progress on hard tasks, while regression evals protect what already works. Without the separation, you'll either stop improving because you're only guarding existing behavior,
# Agent Evaluation Readiness Checklist
[Skip to content](https://blog.langchain.com/agent-evaluation-readiness-checklist/#main)
[](https://blog.langchain.com/)
* [Website](https://www.langchain.com/)
* [Docs](https://docs.langchain.com/)
* [Case Studies](https://blog.langchain.com/tag/case-studies/)
* [Harrison's In the Loop Series](https://blog.langchain.com/tag/in-the-loop/)
* [Try LangSmith](https://smith.langchain.com/)
[Sign in](https://blog.langchain.com/agent-evaluation-readiness-checklist/#/portal/signin)[Subscribe](https://blog.langchain.com/agent-evaluation-readiness-checklist/#/portal/signup)
# Agent Evaluation Readiness Checklist
A practical checklist for agent evaluation: error analysis, dataset construction, grader design, offline & online evals, and production readiness.
17 min read Mar 27, 2026
_By Victor Moreira, Deployed Engineer @ LangChain_
This checklist is a practical companion to ["Agent Observability Powers Agent Evaluation"](https://blog.langchain.com/agent-observability-powers-agent-evaluation/), which covers _why_ agent evaluation is different from traditional software testing, introduces the core observability primitives (runs, traces, threads), and explains how they map to evaluation levels. **Read that post first if you're new to agent evaluation.**
This post focuses on the **_how,_** a step-by-step checklist for building, running, and shipping agent evals.
**Start with the simplest eval that gives you signal.** A few end-to-end evals that test whether your agent completes its core tasks will give you a baseline immediately, even if your architecture is still changing. Only add complexity when you have evidence that simpler approaches are missing real failures.
👉
Don’t care for the deep dive? Skip to [the full checklist](https://blog.langchain.com/agent-evaluation-readiness-checklist/#the-full-checklist).
## Before you build evals
0:00
/0:42
1×
Use LangSmith to go from traces to the annotation queue to datasets & experiments
**☑️ Manually review 20-50 real agent traces before building any eval infrastructure**
**☑️ Define unambiguous success criteria for a single task**
**☑️ Separate capability evals from regression evals**
**☑️ Ensure you can identify and articulate why each failure occurs**
**☑️ Assign eval ownership to a single domain expert**
**☑️ Rule out infrastructure and data pipeline issues before blaming the agent**
### Deep dive
#### Manually review 20-50 real agent traces before building any eval infrastructure
Use LangSmith to go from traces to the annotation queue to datasets & experiments.
Before building any infrastructure, spend 30 minutes reading through real agent traces. You'll learn more about failure patterns from this than from any automated system. LangSmith's [traces](https://docs.langchain.com/langsmith/observability?ref=blog.langchain.com) and [annotation queues](https://docs.langchain.com/langsmith/annotation-queues?ref=blog.langchain.com) are excellent for this.
#### Define unambiguous success criteria for a single task
If two experts can't agree on pass/fail, the task needs refinement:
* _Unclear success:_ "Summarize this document well."
* _Clear success:_ "Extract the 3 main action items from this meeting transcript. Each should be < 20 words and include an owner if mentioned."
#### Separate capability evals from regression evals
You need both because they serve different purposes. Capability evals push your agent forward by measuring progress on hard tasks, while regression evals protect what already works. Without the separation, you'll either stop improving because you're only guarding existing behavior,
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