# What are the top tools for tracing and debugging multi-step AI agent workflows?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">I've been digging through the <a class="a a--md" elv="true" href="https://www.g2.com/categories/ai-agent-observability">AI Agent Observability</a> category trying to figure out which platforms actually let you see what an agent did at each step, not just whether the final output looked wrong. Once an agent chains together a few LLM calls, tool calls, and retrieval steps, "it gave a bad answer" tells you almost nothing. Here's what stood out from real reviews.</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/langsmith/reviews"><strong>LangSmith</strong></a>: reviewers keep coming back to the end-to-end trace view, dropping from a high-level run into individual LLM calls, tool calls, and latency numbers instead of guessing where a chain broke. The tradeoff a few mention is that between projects, runs, datasets, and evaluators, the UI takes a while to feel natural.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/arize-ax/reviews"><strong>Arize AX</strong></a>: people like that tracing and LLM-as-judge evaluation live in one platform, so you can go from "the agent is failing" to the exact span that caused it. New users say the sheer number of concepts, traces, spans, datasets, experiments, means real ramp-up time before it clicks.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/arize-phoenix/reviews"><strong>Arize Phoenix</strong></a>: being open source, a few reviewers self-hosted it specifically to keep control over where trace data lives, and still got the same step-by-step waterfall most paid tools offer. Some dashboards feel less polished than a managed platform, so you end up interpreting results manually more often.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/braintrust-2024-12-22/reviews"><strong>Braintrust</strong></a>: the eval-dataset-experiment loop is what people bring up most, comparing a prompt or model change against the same dataset and catching regressions before they ship rather than eyeballing outputs. One thing worth flagging if you go searching for it yourself: there's a talent marketplace product with a near-identical name, and it's easy to land on reviews for the wrong company.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">For anyone who's run these against a fully custom agent stack instead of LangChain or LangGraph, did tracing stay just as clean, or did you lose some of that plug-and-play advantage? And if you started on Phoenix and later moved to a managed option like Arize AX or LangSmith, was that migration smooth or did you end up rebuilding your evals from scratch?</p>

##### Post Metadata
- Posted at: 12 days ago
- Author title: SEO Content Specialist
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;One thing none of these reviews get into is cost as trace volume scales. Has anyone hit a point where per-trace or per-eval pricing became the actual constraint on how much you could instrument, rather than the tooling itself?&lt;/p&gt;

##### Comment Metadata
- Posted at: 10 days ago
- Author title: Marketing Executive





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