June 2026 Summaries

Confident AI has introduced Report Templates, a customizable reporting feature that allows teams to create tailored reports for evaluating AI agent quality, addressing the challenge of fixed report formats that don't meet diverse team needs. By using Report Templates, users can design reports with specific pages, sections, and AI-generated summaries, enabling them to analyze and summarize data in a way that aligns with their workflow. This functionality allows teams to identify key metrics, performance issues, and usage patterns, transforming raw data into actionable insights without relying on generic dashboard outputs. The templates support a comprehensive approach to AI evaluation, observability, and governance across different projects, catering to product teams and engineers alike, and are now available on the Confident AI platform.

AI agent observability is crucial for understanding and improving the internal execution of AI agents, such as Garry, who autonomously handles customer service tasks. Observability involves capturing every execution detail, from large language model (LLM) calls to tool calls and retrieval processes, enabling teams to debug failures and enhance agent performance over time. This approach goes beyond traditional observability by prioritizing quality as a significant signal alongside latency, cost, and errors because AI agents can provide seemingly correct responses, like issuing a refund for the wrong invoice, without traditional systems detecting the error. Observability combines tracing, monitoring, and evaluation, creating a feedback loop that integrates production failures into benchmarks to prevent regression. It involves various components like spans, traces, and threads, and relies on both online and offline evaluations to maintain agent quality and performance. This system helps teams catch and prevent errors by turning every failure into a learning opportunity, ensuring that agents improve continually in production environments.

Confident AI has introduced Synthetic Data Generation Pipelines, a platform that allows teams to create synthetic data setups without losing the flexibility of local systems. This solution addresses the challenge of consolidating local pipelines, which often required flattening into rigid workflows when moved to a shared platform. By enabling teams to select and integrate various data sources like Snowflake, Salesforce, and Google Drive, the tool facilitates the creation of datasets that resemble production environments while preserving existing generation logic. Users can customize each step of the data generation process, ensuring that the synthetic examples generated are tailored to specific AI applications, such as support bots or document QA systems. This platform aims to standardize AI quality across organizations by providing comprehensive evaluation tools, observability for production traffic, and security measures, all while allowing for continuous improvement as evaluation needs evolve.

Confident AI has launched Annotation Forms, a new feature on its platform designed to enhance the data annotation process by allowing users to create customizable fields for capturing specific reviewer feedback directly on the platform. This innovation addresses the limitations of previous fixed annotation fields, which often necessitated exporting data to spreadsheets for further review and analysis, thereby causing inconsistency and inefficiency. Annotation Forms enable users to design their own fields, ensuring that reviews are structured and integrated into the platform's ecosystem, facilitating the seamless flow of structured data into evaluation workflows. This feature supports various field types, including text, numbers, single and multiple choice, and criteria scoring, allowing for a tailored approach to data annotation and evaluation. The forms can be built using a drag-and-drop editor, and the structured data can be utilized as quantitative signals in dashboards and observatory tools, aligning human judgment with AI evaluation processes.

Confident AI has launched AI Observability Workflows, a graph-based interface designed to streamline data management after traces, spans, and threads reach the platform. This new tool allows users to integrate various tasks such as dataset ingestion, queue ingestion, evaluation rules, and classifiers into a single pipeline, offering a comprehensive view of the entire post-ingestion process. The graph editor enables users to connect tasks in a specific sequence, ensuring that each step builds upon the previous one, enhancing efficiency and coherence in data processing. Users can customize workflows by setting up specific tasks for dataset ingestion, which automatically adds qualified data to datasets, and queue ingestion, which routes data for human review. Evaluation rules can be configured to run metrics on incoming data without code changes, while classifiers label data based on custom descriptions. This innovation aims to provide organizations with a unified quality standard for AI use cases, offering observability, evaluation, and governance tools to maintain high-quality AI deployments across multiple projects.

AI Governance on Confident AI introduces a standardized framework to enforce consistent evaluation, observability, and red teaming across all AI projects, ensuring that every AI use case adheres to a centralized set of policies and controls. This framework addresses the challenges faced by teams when scaling AI, as it eliminates fragmented standards and provides a clear, data-backed answer to whether an AI is ready to ship. By encoding policies that define measurable requirements, AI Governance automates the assessment process, continuously evaluating AI use cases and blocking deployments that do not meet the set criteria. This approach not only provides evidence of compliance but also shifts the focus from subjective evaluations to objective, evidence-based decision-making, enhancing the reliability and safety of AI applications across the organization.

Human-in-the-loop workflows for AI agent evaluation aim to integrate human judgment into the evaluation process, enhancing metrics, expanding coverage, and refining datasets to ensure AI systems remain trustworthy and adaptive. These workflows encompass three main areas: metric alignment, AI agent failure review, and evaluation dataset curation. Metric alignment ensures that automated scores correspond with human judgment, while failure reviews identify issues that metrics might miss, often surfacing in production environments. Evaluation dataset curation involves adding significant failures and new cases to a dataset to prevent future regressions. The ultimate goal is to create a dynamic evaluation system where human feedback informs improvements in metrics and datasets, reducing the need for constant human oversight as the AI system evolves. Confident AI supports this process by providing tools for structured annotations, metric alignment, and error analysis, ensuring that human insights lead to actionable improvements in AI performance.

Human-in-the-loop workflows are essential for AI agent evaluation, offering a systematic approach to improving AI quality by integrating human judgment into metric alignment, failure review, and evaluation dataset curation. These workflows ensure that automated evaluations remain aligned with human expectations by allowing specific cases to be routed to the right reviewers, who provide structured feedback that enhances evaluation metrics. This process helps identify and rectify metric misalignments, surface AI failures not caught by metrics, and curate evaluation datasets with cases that reflect real-world interactions and challenges. Confident AI supports these workflows by offering tools for trace review, annotation queues, and automated suggestions that streamline the feedback process, ultimately strengthening the evaluation system to scale quality effectively without heavily relying on human reviewers.

Product managers working on AI products often face challenges in evaluating and improving AI quality without direct engineering involvement. Modern tools, like Confident AI, have emerged to bridge this gap by enabling product managers to directly build on and monitor AI products. These tools allow managers to edit prompts, run evaluations, compare model variants, and track AI performance through custom dashboards, signals, and alerts. This shift empowers product managers to take ownership of AI product quality, using custom metrics aligned with human judgment to ensure enhancements are effective. By reducing reliance on engineering for iterative changes, product managers can make more informed decisions quickly, using structured workflows that integrate trace reviews, metric alignment, and production monitoring. This holistic approach transforms AI product management from intuition-based decisions to data-driven strategies, fostering continuous improvement and better alignment with user expectations.

The comprehensive guide to LLM experimentation explores the process of systematically comparing different versions of language model applications under controlled conditions to determine which version performs better, as opposed to merely assessing their quality. It emphasizes the importance of maintaining consistent datasets and evaluation metrics while altering only one variable at a time to ensure reliable results. The guide contrasts LLM experimentation with LLM evaluation and A/B testing, highlighting the benefits of using platforms like Confident AI to facilitate the experimentation process. By running experiments offline first to identify weak variants and subsequently confirming results in production environments, teams can continuously improve their AI applications. The text also discusses the significance of curating representative evaluation datasets, selecting appropriate metrics, and avoiding common pitfalls, such as changing too many variables simultaneously or neglecting cost and latency considerations. Through disciplined experimentation and iteration, teams can make informed decisions about deploying AI app updates, enhancing both the quality and reliability of their applications.

LLM experimentation involves the structured comparison of multiple versions of a large language model (LLM) application to determine which performs best under consistent conditions, using the same datasets and evaluation metrics. This approach contrasts with LLM evaluation, which assesses the quality of a single version, and involves a disciplined methodology to ensure fair comparisons. The process includes selecting specific parameters to optimize, such as prompts or models, curating representative datasets, and employing a balanced set of metrics to capture various quality dimensions. By maintaining control over variables, LLM experimentation allows for the accurate identification of improvements, while avoiding common pitfalls like changing too many variables at once or relying solely on averages. The use of platforms like Confident AI facilitates this experimentation by providing tools to run offline experiments, monitor production performance, and continuously refine datasets and metrics based on real-world feedback. This systematic experimentation ultimately informs better release decisions and supports ongoing enhancements, ensuring that changes to LLM applications are both evidence-based and aligned with product goals.

LLM evaluation for startups is an essential yet often overlooked process aimed at assessing the outputs of language model applications using a small, trusted dataset and a focused set of metrics. This evaluation process helps startups make informed changes to prompts, swap models, and refactor pipelines without the risk of introducing silent regressions. The process can begin with a starter dataset of around 25 test cases, along with a 2 + 3 metric collection, which includes two general-purpose metrics and three custom ones that reflect product-specific criteria. By integrating evaluations into CI/CD and leveraging production traces, startups can continuously grow their evaluation suite and ensure quality control. Platforms like Confident AI streamline this process by providing an integrated solution that allows startups to manage datasets, metrics, and evaluations without the need for extensive resources or a large team, ultimately enabling them to iterate quickly and maintain high product quality.

LLM evaluation for startups is crucial for ensuring quality and rapid innovation without introducing silent regressions. Startups often struggle with LLM evaluation due to limited resources and the complexity of creating robust evaluation datasets and metrics. However, it's essential as it allows for prompt changes, model swaps, and pipeline adjustments without compromising performance. The recommended approach involves starting with a small, trusted dataset of around 25 cases and a 2 + 3 metric rule, which includes two general-purpose metrics and three custom metrics tailored to the product's needs. Continuous evaluation through CI/CD integration and production monitoring ensures that any regressions are caught early, with production traces helping to grow the evaluation dataset over time. Confident AI offers a comprehensive platform for startups to manage this process efficiently, supporting dataset generation, metric alignment, and online evaluations, thereby enabling startups to iterate quickly while maintaining quality and reliability.