December 2025 Summaries

Claude Code is an efficient tool for building AI agents directly in the terminal, allowing for rapid iteration and integration with a codebase, though traditional workflows are disrupted when debugging issues require context switching to Braintrust. To address this, a two-way integration has been established between Claude Code and Braintrust, facilitated by two plugins: trace-claude-code and braintrust. The first plugin automatically logs Claude Code sessions as structured traces in Braintrust, while the second enables developers to query logs and fetch experiment results directly from the terminal using natural language. This bidirectional flow, crucial for maintaining context during development, allows developers to retrieve past session data, monitor experiment outcomes, and log new data seamlessly from within their editor. This integration is particularly important as AI agents become increasingly complex, and understanding failures retrospectively poses a challenge. Once the plugins are installed and an API key is provided, trace capture begins automatically, enabling developers to access production data and experiment results on demand, thereby maintaining the full development context.

AI observability tools often lack support for languages beyond Python and TypeScript, posing challenges for developers working with Java, Go, Ruby, or C# in sectors like banking and AI infrastructure. These developers are typically forced to create custom instrumentation or use generic tracing tools that don't cater to AI-specific metrics such as token usage and prompt-completion pairs. In response, native SDKs for Java, Go, Ruby, and C# have been developed using OpenTelemetry for vendor-neutral observability, enabling automatic tracing that integrates seamlessly with existing infrastructures. These SDKs allow for the export of traces to platforms like Braintrust, Datadog, and Honeycomb, and include client wrappers for AI providers to capture essential data and evaluation frameworks for CI/CD environments. Additionally, the SDKs support prompt management by fetching prompts from Braintrust during runtime, and they are open-source, with full documentation and community support available on platforms like GitHub and Discord.

AI observability platforms are essential for monitoring and improving the performance of large language models (LLMs) in production, providing deeper insights beyond traditional metrics like uptime and latency. These platforms help teams understand not just whether an AI is functioning, but how well it is performing by evaluating the quality of its outputs. Key platforms include Braintrust, known for its systematic improvement loop and integration with CI/CD pipelines, and Langfuse, an open-source option for those comfortable with self-hosting. Other notable platforms include LangSmith for LangChain users, Fiddler AI for enterprises focused on compliance and explainability, and Evidently AI, which offers a unified monitoring solution for both traditional machine learning and LLMs. These tools capture detailed logs and traces, facilitate debugging, and enable continuous quality improvement, ultimately allowing teams to iterate rapidly and ship better AI systems.

Brainstore is a specialized database designed for AI observability that efficiently handles the vast amount of data generated by AI systems, offering query times under one second even with terabytes of data. It significantly outperforms traditional observability databases and competitors, enabling real-time search and filter operations through millions of traces, which is crucial for fast debugging in production environments. Unlike traditional databases that use persistent disks, Brainstore utilizes S3 or similar object storage, making it scalable and simple to operate. By partitioning each organization's data separately and employing Tantivy, an open-source library for handling nested JSON with evolving schemas, Brainstore ensures fast queries and strong consistency through a custom write-ahead log. This approach allows teams to resolve production issues swiftly without the delays associated with slower query times.

Voice AI technology is advancing rapidly, with companies deploying agents for tasks such as booking appointments and handling support calls, yet the main challenge now lies in testing these agents at scale. Traditional manual testing methods falter when voice agents, which face unique issues like accents, background noise, and real-time conversation dynamics, handle thousands of daily interactions. Voice agent evaluation encompasses testing and improving conversational AI's handling of audio input and output, involving both offline pre-deployment testing and online evaluations during live production. The complexity of voice interactions, including latency sensitivity and the impact of user tone or interruptions, necessitates sophisticated evaluation tools. There are specialized voice evaluation platforms like Roark, Hamming, Coval, and Evalion, which focus on realistic simulations and voice-specific challenges, while general AI evaluation platforms like Braintrust provide broader support for text, audio, and multimodal AI but rely on partner integrations for voice simulation. Evaluation criteria include simulation capabilities, voice-specific metrics, and integration with workflows, and tools like Braintrust offer features such as audio attachments for debugging, custom scorers for latency and conversation flow, and integration with Evalion for realistic caller simulations.

In the rapidly evolving landscape of 2025, AI features have become widespread, but success hinges on effectively managing and optimizing these capabilities, particularly through LLMOps, which adapts MLOps practices for large language models like GPT and LLaMA. LLMOps encompasses the entire lifecycle, from prompt engineering to deployment and continuous improvement, turning prototypes into reliable production systems. Companies excelling in this area utilize AI evaluation platforms and LLM monitoring tools to prevent quality regressions and ensure systematic testing, gaining a competitive edge through faster iteration and improved product quality. Braintrust, an industry leader, focuses on evaluation-first development, emphasizing systematic testing over mere observability, and has been adopted by major firms like Notion and Stripe for its ability to significantly boost accuracy and development speed. In contrast, platforms like PostHog and LangSmith cater to specific needs such as product analytics integration and LangChain-native observability, respectively. The choice of LLMOps platform depends on factors such as evaluation depth, observability, team composition, and infrastructure management, highlighting the importance of aligning tools with organizational goals for optimal outcomes in AI applications.

LLM applications require advanced monitoring due to their unique failure modes, such as prompt changes that may not affect test cases but can cause production issues, unexpected token cost spikes, and gradual quality degradation. Effective LLM monitoring goes beyond traditional metrics, focusing on the accuracy, relevance, and safety of AI responses in production environments. Tools like Braintrust, Loop, Vellum, Fiddler, and LangSmith provide various features to track performance, manage costs, and detect quality drift. Braintrust stands out for its unified approach to evaluation and production monitoring, offering real-time cost tracking, automated dataset generation, and a feedback loop that converts production traces into test cases. By harnessing online scoring and GitHub integrations, teams can preemptively identify and address quality issues before they impact users, optimize token usage, and ensure robust AI operations across different frameworks.