February 2026 Summaries

AI agents, unlike single-response models, execute multi-step actions that interact with external systems, requiring comprehensive evaluation to ensure reliability. Agent evaluation, distinct from single-turn large language model (LLM) assessments, focuses on both the final outcome and the sequence of decisions within a workflow, identifying errors that might affect subsequent steps. It involves end-to-end testing to determine if agents achieve intended goals and step-level analysis to assess decision accuracy and tool use. The process accounts for non-deterministic behavior by running multiple trials, ensuring stable pass rates. Braintrust offers an integrated workflow for agent evaluation from development to production, supporting offline evaluations using stubbed data, simulations, and sandboxed environments to replicate realistic scenarios. Success criteria are defined with measurable outcomes, employing code-based, model-based, and human graders to evaluate performance. By providing a continuous evaluation pipeline, Braintrust helps teams maintain agent reliability, enforce quality standards via CI/CD integration, and adapt to evolving workflows.

LLM-as-a-judge is an evaluation technique that employs one large language model (LLM) to assess the outputs of another based on clearly defined, natural-language criteria, such as relevance, factual accuracy, and tone, which manual review or traditional metrics like BLEU or ROUGE might miss. This method scales evaluations efficiently, allowing for structured scoring or verdicts on subjective dimensions that rule-based checks cannot reliably measure. Despite its advantages, the technique requires careful implementation to avoid biases and inaccuracies, such as factual verification without reference, position bias, verbosity bias, and self-enhancement bias. Using a calibration set, running adversarial tests, and maintaining ongoing human spot checks are recommended to ensure reliable results. Braintrust offers a comprehensive platform to facilitate this evaluation approach, supporting teams in creating custom scorers, running evaluations across development and production, and maintaining consistent quality standards through CI/CD integration.

Agent observability is crucial for ensuring the reliability of AI systems as they navigate multi-step workflows, offering insights into every stage of an agent's task execution by capturing tool calls, memory accesses, and decision points. Unlike standard LLM observability, which focuses on individual model calls, agent observability provides a comprehensive view of the entire execution flow, allowing teams to trace errors back to their origins rather than just seeing the final outcome. This capability is essential for debugging complex workflows where failures can emerge from various steps, such as incorrect tool arguments or outdated memory retrievals. Braintrust facilitates this process by offering infrastructure that integrates tracing, evaluation, and CI enforcement into a cohesive workflow, enabling teams to monitor quality, identify failure points, and maintain control over production AI agents. By logging execution paths and linking them to measurable quality signals, Braintrust ensures that teams can investigate issues effectively and apply improvements confidently, with major companies like Dropbox, Stripe, and Zapier leveraging these tools to maintain observability in their AI systems.

Retrieval-Augmented Generation (RAG) systems aim to generate grounded responses by retrieving relevant documents from a knowledge base and using this context in language models. However, RAG pipelines can fail silently, returning unrelated documents or generating hallucinated facts, highlighting the need for systematic RAG evaluation. This evaluation involves measuring the quality of both retrieval and generation stages independently to diagnose and fix issues, using metrics such as context precision and recall, answer groundedness, and faithfulness. Effective RAG evaluation requires both offline testing with curated datasets and online monitoring of real-world queries to capture unexpected variations in user input. Braintrust provides a comprehensive platform for RAG evaluation, integrating tracing, scoring, experimentation, and monitoring to ensure consistent measurement and improvement of RAG pipeline quality across development and production environments.

Braintrust is tackling the challenges of AI observability and scalability by developing a platform that enhances the ability to surface meaningful insights from vast data volumes, enabling continuous iteration and automation. The platform leverages Brainstore, a purpose-built database providing rapid query performance and real-time trace visibility, to assist teams in diagnosing and resolving issues effectively without data loss. It introduces features like semantic search, SQL-first trace analytics, and trace-level scoring to streamline debugging and performance assessment. Braintrust's Topics and Loop functionalities allow for proactive issue discovery and iterative improvement by summarizing patterns and connecting observability with evaluations in a unified workflow. The introduction of the Braintrust CLI and Gateway further standardizes AI operations, enabling efficient model management and observability across various providers. By focusing on rapid iteration and systematic integration of AI SDLC processes, Braintrust aims to help teams create and maintain high-quality AI products.

Braintrust's Topics feature addresses the challenge of managing and interpreting the vast amounts of trace data generated by AI applications in production by automatically clustering and classifying traces based on recurring patterns, enabling teams to review high-level topics instead of individual traces. Utilizing AI-powered clustering methods such as UMAP dimensionality reduction, HDBSCAN clustering, and c-TF-IDF keyword extraction, Topics organizes traces into descriptive groups with representative keywords and examples, facilitating quick understanding of emerging issues like failure modes, user behavior shifts, or prompt drifts. It includes built-in facets for commonly sought patterns—such as user tasks, agent issues, and sentiment analysis—while also allowing for custom facets and preprocessors to tailor the analysis to unique data dimensions. Topics integrates seamlessly into existing Braintrust workflows, offering filterable fields and comparison capabilities across projects, and is available in beta for Pro and Enterprise users, with an option for Free plan users to request access.

Eval-driven development (EDD) is a methodology designed to improve the quality and reliability of applications powered by large language models (LLMs) by using evaluations as a core aspect of the development process. Unlike traditional testing that relies on a limited set of examples and binary pass/fail results, EDD involves defining quality criteria in advance, scoring changes across multiple dimensions, and using these scores to guide development decisions. EDD functions as a continuous loop where evaluation criteria are refined as business needs change, ensuring that every modification to the system is assessed against a consistent standard before deployment. This approach allows for clear identification of the impact of changes, helping to prevent regressions and optimize the system based on measurable outcomes. By integrating evaluations directly into CI/CD pipelines, EDD offers a structured framework for managing quality at every stage of development, from initial prompt modifications to production monitoring. Braintrust, a tool supporting EDD, provides infrastructure for managing datasets, scoring, and release controls, enabling teams to maintain alignment between development and production criteria and ensuring that changes are validated against defined quality standards.

Large Language Model (LLM) systems require a distinct approach to monitoring and observability due to their unique ability to produce incorrect or irrelevant outputs while appearing healthy. Traditional monitoring tracks system metrics like latency and error rates to confirm operational health, but it doesn't explain why issues occur, whereas observability provides insight into the system's behavior by tracing specific requests through the pipeline. Evaluations play a key role in both processes by scoring output quality and offering data to investigate failures. Braintrust unifies these elements, offering a platform that integrates monitoring, observability, and evaluations into a cohesive workflow, allowing teams to detect, investigate, and address issues efficiently. This integration is crucial, as separating these functions across different tools often leads to inefficiencies and complicates issue resolution.

Prompt evaluation is a systematic approach for assessing the quality of AI model prompts by measuring their performance against structured test data across dimensions such as correctness, relevance, and safety. This method enables teams to objectively evaluate prompt changes using automated scoring and LLM-as-a-judge tools, which analyze outputs based on meaning and intent rather than surface text. The process emphasizes evidence-based assessments over subjective judgment, ensuring that prompt modifications yield real improvements before deployment. Prompt evaluation differs from prompt engineering by focusing on measuring the impact of prompt modifications against defined quality criteria. Tools like Braintrust facilitate end-to-end prompt evaluation by providing infrastructure for building golden datasets, employing built-in and custom scorers, and integrating evaluations into CI/CD workflows. This approach allows production teams to maintain prompt quality through automated regression testing and continuous monitoring, ultimately supporting faster iteration and more reliable AI feature deployment.

Prompt versioning is essential for managing the evolution of AI prompts in production by tracking every change as an immutable, uniquely identified version, which includes metadata, evaluation results, and the execution context such as model and parameters. This practice enables teams to understand the impact of updates, safely roll back changes, and maintain a clear history of prompt iterations, which is crucial when slight modifications can inadvertently affect output quality. Unlike traditional code, AI prompts yield different outputs even with identical inputs due to the influence of model configuration and parameter settings, necessitating a comprehensive versioning approach that encompasses the entire execution context. Prompt management extends this foundation by organizing prompts, controlling access, reviewing, testing, and deploying them through structured workflows across different environments, ensuring that only validated changes reach production. Tools like Braintrust streamline this process by integrating prompt versioning, automated evaluations, deployment workflows, and production monitoring into a cohesive system, allowing teams to efficiently iterate on prompts while maintaining quality and reliability. This approach mitigates the risk of silent regressions, facilitates rapid recovery from quality drops, and transforms production failures into test cases, thus turning prompt iteration into a controlled engineering process that scales with demand.

Braintrust has secured $80 million in Series B funding, led by ICONIQ, with participation from previous investors such as Andreessen Horowitz and Greylock, to enhance its role as an observability layer for production AI. Founded to address the challenges of AI observability experienced by its creator at companies like Impira and Figma, Braintrust focuses on solving real user needs. As AI becomes embedded in everyday workflows, traditional observability tools struggle to keep up with the complexity and scale of modern AI systems. Braintrust aims to treat AI observability as a core infrastructure component, providing clarity and accountability in rapidly changing AI environments. The company emphasizes a customer-centric approach, engaging directly with users to improve their products. ICONIQ's General Partner Matt Jacobson praises Braintrust's dedication to meeting customer needs, seeing it as a key trait of successful companies. With the new funding, Braintrust plans to expand its engineering and go-to-market teams and develop new products, maintaining its commitment to customer focus as it grows.

The text discusses the complexities of evaluating AI models, emphasizing the limitations of subjective impressions and the need for standardized, rigorous benchmarks. It outlines five pillars of model evaluation, which include complex agentic task execution, domain-specific performance, operational metrics, community-defined evaluations, and anecdotal assessments. The text compares two specific models, Claude Opus 4.6 and GPT-5.3 Codex, across these pillars, highlighting their respective strengths and weaknesses based on published benchmarks. While Anthropic's approach focuses on detailed quantitative data to prove Opus 4.6's superiority in specific domains, OpenAI emphasizes practical use cases and developer testimonials for GPT-5.3 Codex. The analysis reveals strategic differences in how both companies position their models, with Anthropic catering to technical decision-makers and OpenAI targeting developers interested in product versatility. The text concludes that while benchmarks provide valuable insights, they do not capture the full picture, and practical experience, along with custom evaluations, is essential for choosing the right model for specific tasks.

Large Language Model (LLM) monitoring is crucial for ensuring the optimal performance of AI applications in production environments. Unlike traditional application monitoring, which focuses on system health metrics like CPU usage and memory, LLM monitoring encompasses both operational metrics—such as latency, error types, and token costs—and quality metrics that assess the accuracy, relevance, and safety of model outputs. This approach fills the gap left by traditional monitoring tools that may report technical success while overlooking the quality of content generated by LLMs, which can be non-deterministic and produce hallucinated or harmful content. LLM monitoring involves several layers, including tracking user prompts and responses, measuring latency, and attributing costs, while also assessing safety and compliance to prevent unsafe outputs. Advanced solutions like Braintrust offer comprehensive monitoring by integrating tracing, quality evaluation, and cost analytics, helping teams detect and address issues before they impact users. By implementing a layered monitoring approach, organizations can transition AI development into a predictable engineering practice, ensuring reliable and safe AI software deployment in real-world scenarios.

LLM observability is an advanced framework that enhances the management of Large Language Model (LLM) applications by providing insights into system behavior and output quality beyond traditional monitoring metrics like uptime and latency. By tracing each step of a request—from user input to final output—using tools like OpenTelemetry, and evaluating the quality of responses through both offline and online evals, LLM observability allows teams to debug incorrect outputs, track performance variations over time, and manage operational costs more effectively. It addresses common production challenges such as debuggability, reliability, cost control, and safety compliance, enabling teams to identify the root causes of issues and validate improvements before deployment. Braintrust offers an integrated solution for LLM observability, combining tracing, evaluation, and monitoring in a unified platform to ensure reliable, cost-efficient, and safe AI operations. This approach helps organizations like Stripe, Notion, and Dropbox to effectively manage their AI systems, ensuring quality and reliability as usage scales.

LLM evaluation is a crucial process for ensuring the quality and reliability of LLM-powered applications by systematically measuring their performance against defined criteria. It involves both offline and online evaluation modes to test changes before deployment and monitor live production traffic for unanticipated issues, respectively. The evaluation process is not limited to assessing the entire system but also includes component-level checks to identify specific sources of failure, such as prompt changes, retrieval quality, and generation accuracy in RAG pipelines, as well as safety compliance. Effective LLM evaluation requires building a comprehensive workflow that includes dataset construction, rubric definition, evaluator selection, scoring, and CI/CD integration to automate the process and prevent regressions. Additionally, organizations can benefit from platforms like Braintrust, which offer integrated evaluation infrastructure, enabling teams to conduct systematic evaluations, manage datasets, trace failures, and monitor production performance, ultimately leading to more stable and user-aligned systems.

Prompt management is essential for production large language model (LLM) applications due to the frequent changes required in prompts, which can significantly impact user experience and product reliability. By treating prompts as production assets, teams can implement version control, review, testing, and independent deployment from application code, thus reducing risks associated with prompt modifications. This structured approach addresses challenges such as version chaos, deployment friction, and invisible quality degradation by providing a single source of truth, enabling safe and speedy iterations, and facilitating cross-functional collaboration. Braintrust is highlighted as a comprehensive platform that integrates prompt management components, including versioning, evaluation, deployment, and monitoring, to ensure that prompt changes are observable, testable, and reversible. It supports safe deployment through progressive rollout strategies and CI/CD integration, preventing untested or regressive changes from affecting production. As LLM applications mature, prompt management becomes crucial in maintaining quality and scalability, transforming prompts from experimental inputs into reliable production dependencies.

Organizations developing AI applications often face challenges when prompts that perform well during development fail in production, typically due to prompt updates being deployed without proper impact measurement. Prompt engineering tools address this by linking prompt changes to measurable outcomes, allowing teams to test and evaluate prompts systematically before users encounter issues. These tools offer features such as interactive prompt playgrounds, version control, dataset management, evaluation frameworks, and production monitoring, which help improve AI application reliability. Among these tools, Braintrust stands out by providing an integrated infrastructure for prompt development, utilizing an AI assistant called Loop to automate the optimization and evaluation process, ensuring prompt changes are validated against quality metrics before deployment. The platform's comprehensive approach, including its evaluation-first infrastructure, makes it a preferred choice for teams that require robust quality assurance in prompt engineering.

AI agent evaluation focuses on assessing how well agents perform multi-step tasks, contrasting with traditional LLM evaluation, which scores single response outputs. This comprehensive evaluation process examines the agent's reasoning, tool selection, action execution, and result processing while considering both the outcome and the journey taken to achieve it. Due to the non-deterministic nature of agents, which can produce different sequences of actions for identical requests, evaluation requires a detailed analysis of efficiency and logical decision-making. A robust evaluation framework involves tracing every decision during execution, employing scoring mechanisms for performance metrics, and integrating with development workflows to ensure agents are reliable in production environments. Platforms like Braintrust offer these capabilities by providing tools for exhaustive tracing, real-time monitoring, cost analytics, and seamless integration with popular frameworks, allowing teams to build and refine evaluation infrastructure effectively. Such systems enable proactive quality management by identifying failures early and preventing regressions, thus enhancing the reliability and efficiency of AI agents.

AI agent observability is a critical practice for understanding and enhancing the reliability of AI systems as they execute complex tasks, involving multiple decisions and tool selections. This approach goes beyond traditional monitoring by capturing detailed traces, logs, metrics, and evaluations of agent workflows, providing insights into their reasoning processes and performance. Key platforms like Braintrust, Vellum, Fiddler, Helicone, and Galileo offer diverse features tailored to different needs, such as evaluation-driven iteration, visual workflow development, compliance monitoring, cost optimization, and real-time safety checks. Braintrust, in particular, stands out for its integration of evaluation into observability, allowing teams to diagnose issues, optimize performance, and ensure quality consistently across development and production. This platform supports CI/CD integration, enabling automated quality checks and facilitating a continuous feedback loop that aids in identifying and resolving issues efficiently. By offering tools that capture comprehensive data on agent decisions and performance, these observability platforms help teams build robust AI systems capable of scaling effectively in production environments.

Prompt management tools are essential for efficiently versioning, organizing, testing, and deploying prompts across various environments, treating them as configurable assets independent of software releases. Braintrust stands out by integrating versioning directly with quality measurement, allowing teams to test prompt updates against real data before they reach users, and providing live monitoring to surface any quality drops. Other tools, like PromptLayer, offer a visual workspace for non-technical teams to manage prompts without coding, while LangSmith integrates with LangChain or LangGraph frameworks for seamless versioning and testing. Vellum facilitates visual workflow building, and PromptHub provides Git-style version control for managing prompts akin to code changes. W&B Weave integrates prompt management with ML experiments, and Promptfoo offers CLI-based testing with built-in security scanning for regulated industries. These tools cater to different needs, from non-technical editing and collaboration to advanced testing and compliance, making them vital for teams looking to improve AI feature reliability and user experience.