May 2026 Summaries

Incorporating human expertise into the evaluation workflow of AI products is crucial for establishing an authoritative benchmark to compare outputs, ensuring the quality of the product doesn't regress. Human review is integrated into the process by turning production traces into "golden datasets" that evolve over time, helping to refine scorers as data changes. This involves categorizing traces by patterns such as failure mode and sentiment, with tools like Topics clustering traces automatically into named categories. Reviewers apply their expertise to confirm correct outputs, which are specified as "expected" values that guide the evaluation process. Setting up human review requires defining a clear rubric and using review queues to route traces to subject matter experts, ensuring that real-world expertise is applied rigorously. Over time, human-reviewed ground truths are used to develop scalable, automated evaluation systems, with human review shifting from primary evaluation to providing high-quality training signals. It is essential to avoid anti-patterns such as leaving "expected" values blank or mixing additional information into them, as they can undermine the effectiveness of the evaluation process.

In the evolution of AI agent architectures, the journey from simple prompt-based systems to sophisticated harnessed agents reflects significant advancements in model capabilities and evaluation strategies. Initially, AI agents operated through single prompts, providing basic responses without context or memory. As capabilities progressed, agents developed structured chains and ReAct loops, allowing for dynamic tool usage and iterative decision-making. Evaluations evolved from simple answer-quality assessments to complex trace evaluations, considering tool selection, cost, and safety. Modern agents integrate workflows with deterministic controls for reliability, while the latest generation utilizes harnesses to manage peripherals like memory and sandboxes, enhancing flexibility and capability. Evaluation strategies have become layered, incorporating offline tests, simulations, replays, and online scoring to ensure agents perform effectively and safely in dynamic environments. This iterative approach underscores the importance of continuous evaluation to adapt to real-world challenges, enabling AI agents to transition from basic functionalities to comprehensive incident response systems like Sentinel.

In 2026, Braintrust stands out as the leading hallucination detection tool for AI teams integrating evaluation, production monitoring, human review, and release control into their workflow. Braintrust excels by enabling teams to connect factuality checks directly to production traces and evaluation datasets, thus enhancing future regression coverage with hallucinated outputs from live traffic. It supports pre-deployment evaluation and production monitoring, utilizing various scoring methods such as LLM-as-a-judge, groundedness checks, and custom code scorers. While Braintrust is particularly suited for teams seeking a comprehensive solution, alternative tools like Galileo, Arize Phoenix, Patronus AI, and Promptfoo offer specialized features such as runtime guardrails, self-hosted observability, domain-specific evaluators, and CI-native evaluations. Braintrust's free Starter plan offers early evaluation capabilities, making it accessible for teams to test and define hallucination detection before scaling to broader production use.

RAG (Retrieval-Augmented Generation) observability is crucial for addressing failures in production that generic logs often miss, providing trace-level visibility into the retrieval, reranking, context assembly, and generation processes. This observability plays a vital role in ensuring the quality of AI-generated answers by scoring live traffic for groundedness, faithfulness, answer relevance, and retrieval quality, enabling teams to detect regressions before they impact users. Different tools like Braintrust, Arize Phoenix, Langfuse, LangSmith, and Galileo offer various features such as pipeline tracing, live quality scoring, drift detection, and debugging UX, catering to different needs based on criteria such as framework support, self-hosting options, and specific RAG metrics. Braintrust is highlighted for its comprehensive integration of evals, traces, and production-quality feedback, making it suitable for teams focusing on connecting production findings back to evaluation and debugging. Each tool has its strengths, catering to different deployment needs and technical requirements, from open-source solutions to managed services.

LLM call observability is a critical process in monitoring the detailed interactions between applications and language models, allowing for comprehensive tracking of requests, responses, and associated metadata for each API call. Unlike traditional APM tools that only capture HTTP-level signals, LLM call observability focuses on in-depth data such as the full request and response payloads, performance metrics, and cost analysis, which are pivotal for debugging and ensuring quality outputs. This observability is essential for various production LLM workloads, including chatbots and summarization, as it provides visibility into what the model received, returned, and the performance of each call. Tools like Braintrust offer robust solutions by integrating LLM call observability with evaluation and release decision workflows, supporting teams in debugging, detecting drift, and managing regression evaluations effectively. Additionally, Braintrust's platform connects call observability directly to CI quality gates and production-to-test-case workflows, facilitating continuous improvement and quality assurance in AI systems.

Engineering teams aiming to optimize and track LLM costs in production are increasingly focused on tools that provide detailed visibility into token spending associated with various workflow steps, including long prompts, retries, and tool calls. Among the tools evaluated, Braintrust stands out for its comprehensive approach, offering trace-level visibility that captures every LLM call, retrieval step, and tool invocation, allowing teams to attribute costs precisely and experiment with cheaper prompts or models. Braintrust's system integrates cost data with quality checks, ensuring that any cost-saving changes maintain output integrity before release. It enables prompt and model experimentation with real production traces and includes an AI assistant, Loop, that automatically proposes optimizations based on failure patterns. With its focus on seamless integration of cost tracking and quality assurance, Braintrust is particularly recommended for production teams requiring robust cost control measures, as evidenced by its adoption by companies like Stripe, Vercel, and Airtable.

Evaluating AI chatbots requires a dual-layer approach that combines single-turn and multi-turn scoring to effectively assess both individual responses and entire conversations. While single-turn evaluations focus on aspects like tone, empathy, and adherence to company guidelines for each interaction, they are insufficient for determining whether an AI has satisfactorily resolved a customer's issue. Multi-turn scoring, therefore, is crucial to understanding the overall quality of an interaction by examining whether the customer's problem was successfully addressed across the conversation. Implementing this involves logging conversations as structured data, using AI models like GPT-5 Mini to assess interactions, and setting up automated scoring processes in tools like Braintrust. The integration of automated scoring with features like Topics, which clusters and summarizes conversations into categories, allows for the identification of recurring issues and optimization of AI performance at scale. This comprehensive evaluation framework supports continuous improvement of conversational AI systems by surfacing patterns and guiding engineering efforts towards resolving frequent customer pain points.

AI regressions often go unnoticed on dashboards because they typically manifest as incorrect outputs rather than latency or error rate issues, with the first indications usually emerging from support queues rather than the engineering stack. Teams that efficiently catch these regressions integrate evaluations and trace logs within the same tool, reducing the manual process and context-switching that typically delays fixes. When traces and evaluations are combined, suspicious traces can be quickly turned into datasets, and scoring functions can be reused to monitor fixes in production, significantly speeding up debugging processes. Automating the quality assurance process is the next step once traces and evaluations are centralized, as it allows the system to propose solutions, run evaluations, and even suggest new scoring functions for unaddressed patterns, leading to a more proactive approach in identifying and resolving issues. Centralizing these processes is crucial for enabling automation and gaining a comprehensive understanding of the system's performance, thus streamlining workflows and allowing many tasks to be completed automatically.

In the context of managing rising costs associated with Large Language Models (LLMs) in production workflows, AI observability emerges as a crucial tool for understanding and controlling expenses at a granular level. As LLM systems scale, costs can quickly accumulate due to complex workflows involving multiple model calls, tool interactions, and retries, compounded by expanding context windows. Aggregate dashboards often fail to pinpoint the exact sources of increased spending. By providing trace-level visibility, AI observability exposes the specific prompts, models, and tool calls that drive costs, enabling teams to identify and optimize costly workflow steps. This approach is supported by Braintrust, which integrates observability with prompt experimentation, model comparison, and evaluation-backed release control, ensuring that cost reductions do not compromise output quality. Engineers can utilize trace trees to inspect token usage and estimated costs for each span, facilitating prompt optimization and model comparison to reduce token usage and switch to more cost-effective models while maintaining quality. The integration of evaluation processes ensures that changes are validated before deployment, turning cost management into a structured engineering discipline.

Agent observability is a practice that tracks every action an AI agent takes during execution, such as tool selection, model responses, and memory operations, providing a structured trace to understand the agent's behavior. Unlike traditional Application Performance Management (APM) systems, which measure request rate, latency, and error rate, agent observability captures semantic behavior, revealing issues like tool misselection or plan drift that APM cannot detect. This approach uses a schema with four span types—tool calls, reasoning steps, state transitions, and memory operations—to make failure modes visible and connects these traces to evaluation processes, ensuring continuous quality improvement. Braintrust offers a platform that integrates tracing, evaluation, and release enforcement into one workflow, enabling teams to monitor real agent runs, score live production traces, and convert failures into evaluation cases, thereby preventing regressions in continuous integration (CI) pipelines. The platform supports multiple frameworks and provides native adapters and OpenTelemetry instrumentation to ensure compatibility across various agent frameworks, with a free tier offering ample resources for initial implementation.