AI agent observability vs evals vs corrections

Observability shows what your agent did. Evals test a change against known cases before you ship it. Corrections change what your agent receives in one situation while it runs. They answer different questions, and a team with a production agent often needs more than one.

What does each one do?

	Observability	Evals	Corrections
Question it answers	What did the agent do?	Did this change help or hurt?	What should the agent be told in this situation?
When it runs	In production	Before release, on a schedule or on live traffic	In production, on matching turns
What you get	Traces, dashboards and alerts	Scores and comparisons	An instruction added to the prompt
Changes what the agent receives?	No	No	Yes
Main limit	You still have to act on what you see	It only catches what your cases and scorers check	It only covers situations you’ve found and scoped, and the model can still ignore it

Anthropic’s guide to agent evals makes the same point about layers: “no single evaluation layer catches every issue.” Evals are the first line of defense, and production monitoring catches what they miss, though by then problems have already reached users.

What do these tools do about recurring failures?

Many platforms now go beyond showing traces. This table comes from each vendor’s docs in October 2026. These products change quickly, so check their current docs.

Tool	Built around	Recurring failures	How a fix reaches the agent
Langfuse	Open-source tracing, prompt management and evaluation	Documents an error analysis workflow: annotate traces, then cluster failure categories	You change the code or publish a new version of a managed prompt
LangSmith	Tracing, monitoring, evaluation, prompt engineering and deployment	Insights categorizes traces. Engine surfaces recurring issues, diagnoses them and proposes prompt or code fixes	You review the change. Engine can open a pull request in a connected repository, and it never merges one
Braintrust	Tracing and experiments, described as “active observability”	Patterns, in public preview, finds recurring problems with evidence and a suggested fix	Loop can open a pull request in a connected repository (public preview), or you hand the brief to a coding agent
Arize Phoenix	Source-available tracing, evaluation, prompt versioning and experiments	Evaluators score traces, and datasets let you rerun them across versions. PXI, in beta, walks failing traces and proposes root causes	You change the prompt or code and test it with an experiment
Mirrors	Recurring mistakes and corrections	Surfaces the mistakes your agent repeats across production runs	You write a scoped correction. The SDK returns it when its trigger matches, and your harness appends it

The last column is where corrections differ. A new prompt version goes to everything that uses that prompt, unless your own code picks versions by situation. A correction goes only to the conversations its trigger matches.

Which one do you need first?

• You can’t see what your agent is doing. Start with observability. Everything else depends on having full runs to look at.
• You change prompts or models and can’t tell what broke. Add evals, and turn production failures into test cases.
• You find the same mistakes again and your system prompt keeps growing. Add corrections or conditional instructions.
• A rule must hold every time. None of these is enough. Enforce it in code.

Looking for a Langfuse, LangSmith or Braintrust alternative?

It depends on why you’re looking. If the issue is hosting, framework fit or budget, compare tools inside the same category. Langfuse is open source, Phoenix is source-available under the Elastic License, and LangSmith works with many frameworks beyond LangChain.

If the issue is that you can see failures and they keep coming back, a new dashboard will show you the same failures. The gap sits between finding a problem and changing what the agent does the next time it comes up.

Does Mirrors replace observability or evals?

No. Mirrors captures your agent’s conversations so it can surface recurring mistakes and preview corrections, and it works alongside your tracing and eval tools.

It has limits worth knowing. Preview shows where a correction would apply, and it doesn’t show that the correction improves answers. You or your coding agent write each correction, and Mirrors doesn’t rewrite your agent on its own. See how it works.