When simple tracing is enough
You don't need failure detection or an eval platform on day one. Tracing alone, any tool that records prompts, completions, tool calls and latency, is enough when:
- There is a single model call or a short fixed chain, not an agent that decides its own steps.
- Traffic is low enough that someone on the team reads every transcript, or a real sample of them, each week.
- Users are internal and tell you quickly when an answer is wrong.
- Nothing the model says can cost money, break a policy or reach a customer unreviewed.
When any of those stops being true, reading traces by hand stops scaling, and you need something that reads them for you.
By agent type
| Agent type | What tends to go wrong | Look for | Start with |
|---|---|---|---|
| Support and operations agents bound by policy | Policy violations, invented lookups, tickets left undone | Detection against the agent's instructions, issue grouping, Slack alerts | Lemma |
| Consumer agents with open-ended conversations | Lost context across turns, failure modes too many to list | Thread-linked traces, detection without predefined scorers | Lemma |
| Browser and computer-use agents | Wrong page, stuck loops, silent fallbacks | Screen recording with the trace, failure clustering | Laminar |
| RAG and question-answering pipelines with a test set | Regressions when prompts, models or retrieval change | Offline experiments, versioned datasets, scorers | Braintrust |
| Many agents across an enterprise, including no-code platforms | Inconsistent standards, audit gaps | OpenTelemetry, policies, RBAC, hybrid or self-hosted deployment | HoneyHive |
By framework
Check the vendor's integration docs for your framework before anything else: a native integration nests tool calls and sub-agents correctly. Listed on each vendor's docs as of November 2026:
| Framework | Lemma | Braintrust | HoneyHive | Laminar |
|---|---|---|---|---|
| OpenAI Agents SDK | Listed | Check docs | Check docs | Listed |
| Vercel AI SDK | Listed | Listed | Check docs | Listed |
| LangChain | Listed | Listed | Listed | Listed |
| LangGraph | Listed | Listed | Listed | Check docs |
| Mastra | Listed | Check docs | Check docs | Listed |
| OpenTelemetry | Alongside its SDK | Listed | Native | Check docs |
"Check docs" means we did not find it on the vendor's integration pages we read, not that it is unsupported; framework lists change often.
By team size
- One to five engineers, agent in production: you don't have time to write scorers for every failure. Pick the tool that tells you what's broken with the least setup: Lemma.
- A product team with a dedicated AI engineer and a test set: an eval platform pays off. Braintrust, with production monitoring alongside if failures still reach users.
- A platform team serving many product teams: you need standards, permissions and deployment options. HoneyHive, or Laminar if open source is a requirement.
Questions to ask in any trial
- Point it at a week of real traffic. What did it find that you didn't already know?
- How long from install to the first useful finding, counting scorer and threshold setup?
- Open one finding. Can you see the evidence, and does it lead to a fix?
- Where does the alert land, and would the right person act on it?