Braintrust

The observability layer for production AI.

[Braintrust @braintrust]

Topics is now GA on all plans.

Continuously find the patterns worth investigating across your production traffic.

[Braintrust @braintrust]

Join us → https://t.co/DeeIXkO3Gi

[Braintrust @braintrust]

Production traces capture where your AI falls short and what users are trying to do. Building evals with that data is how you catch failures earlier and decide what to ship next.

Braintrust is leading a workshop on how to:
- Use the patterns Braintrust surfaces automatically
- Turn them into a labeled eval dataset
- Run the same workflow every time a new pattern shows up

[Braintrust @braintrust]

Read more → https://t.co/slGEfkJ3ZD

[Braintrust @braintrust]

Vibes-based testing and manual review don't scale.

Automated evals are easy to set up and can make an immediate impact on AI development speed. Learn about three automated approaches to get started quickly with evals: LLM judges, heuristics, and comparative evals.

[Ankur Goyal @ankrgyl]

https://t.co/LPoal6Z2sw

[Braintrust @braintrust]

This is the next chapter of Braintrust: active observability. We work behind the scenes to find answers to questions before you have to ask them.

Trace everything → https://t.co/KmbQs1DgGq

[Braintrust @braintrust]

Topics is now GA on all plans.

Continuously find the patterns worth investigating across your production traffic.

[Braintrust @braintrust]

Topics reconstructs conversational threads, runs the right model at the right cost, stores vectors for on-demand clustering, and surfaces the output in a UI built for humans.

[Braintrust @braintrust]

Read more → https://t.co/X9tKecAoPb

[Braintrust @braintrust]

Loop can create and manage dataset snapshots, tag them with environments, and prompt you to save before making changes. Your AI agent handles dataset versioning so you can focus on building better evals.

[Braintrust @braintrust]

Braintrust presented on these challenges at @aidotengineer Europe. Watch the full session → https://t.co/kuv5FfsC3A

[Braintrust @braintrust]

Most traditional enterprises gave responsibility for AI to their ML team, but the model providers own the data pipeline. What's left is prompt engineering, context management, distributed systems, and evals, which require a diverse set of teams to get right.

[Braintrust @braintrust]

Thanks to @Redpoint and congratulations to all the companies included on the 2026 InfraRed 100.

[Braintrust @braintrust]

Read more → https://t.co/GPfknoY1us

[Braintrust @braintrust]

Zero-code AI observability for Java applications. Attach the Braintrust Java agent at JVM startup to automatically trace OpenAI, Anthropic, Spring AI, LangChain4j, and Google GenAI calls without touching your code.

[Braintrust @braintrust]

Read more → https://t.co/11VKdUnfhC

[Braintrust @braintrust]

Without validation of what good looks like, it's impossible to judge whether AI quality is improving or regressing.

Human expertise turns production traces into golden datasets that improve over time.

[Braintrust @braintrust]

@AleksaMiti1 Thank you for your kind words.

Please visit: https://t.co/Zy2K3r4CGt

And use the code: thank-you

For some swag from our team.

[Braintrust @braintrust]

@daRubberDuckiee Join us → https://t.co/g6iAm7gf8i

[Braintrust @braintrust]

Most AI failures don’t appear in testing. They show up later in support tickets, vague feedback, and production traces that are hard to interpret.

Braintrust's @darubberduckiee leads a workshop on using Topics to uncover those patterns, turn them into evals, and investigate regressions before they become bigger issues.