Building Eval Platforms: Harder Than It Looks

The ultimate goal for teams is to maintain confidence in AI agents as they interact with real-world users.

LLMs are non-deterministic and highly variable, which offers significant flexibility but also introduces substantial risk due to their unpredictable nature.

This variability, if unchecked, can lead to inconsistent agent behaviors, resulting in brand damage, compliance issues, and increased maintenance costs. Evals are crucial to mitigate this uncertainty and ensure agents perform predictably before deployment.

Many teams initially adopt spreadsheet-based evaluations as a common and acceptable first step in their workflow.

A basic evaluation system requires three fundamental components: the ability to execute agents against specific inputs, a mechanism to view the results, and a collection of test examples. Hussein asserts that while spreadsheets are useful for initial steps, they represent only the 'tip of the iceberg' when it comes to comprehensive evaluation needs, indicating that scaling requires more robust tooling.

Professional evaluation systems necessitate superior datasets, automated scoring, debugging tools, and integrated feedback loops that connect production performance back to development. This expanded scope is essential for advancing beyond rudimentary testing.

LLMs are fundamentally different from classical software due to their non-deterministic nature, making traditional unit and regression tests insufficient for managing their ongoing behavioral drift.

Effective evaluation requires a cross-functional team, encompassing software engineers, AI engineers, product managers (PMs) who now engage in evaluation, and subject matter experts (SMEs) providing domain expertise. This continuous process transforms evaluation into an ongoing 'hill climbing' workflow to improve agent quality.

This continuous iteration is vital because user interactions and model behaviors constantly evolve, mirroring the drift observed in classical machine learning. The flywheel mechanism ensures that quality enhancements are implemented without inadvertently introducing new regressions.

Spreadsheets offer an accessible, zero-barrier entry point for teams beginning their evaluation process, making them a common and acceptable starting point.

The setup is straightforward, involving a simple loop of inputs and execution to observe agent outputs. However, this method quickly diminishes in utility due to difficulties in analyzing data over time, limitations in collaborative efforts, and the exclusion of critical domain expert input.

Product engineers often develop bespoke internal UIs to integrate product managers into the evaluation loop, improving the visual presentation of results.

Despite better visualization, these UIs primarily function as 'reporting tools,' still struggling with collaborative challenges and the difficulty of conducting long-horizon analytics on persistent data, limiting their effectiveness for deep iterative improvements.

The evolution of evaluation platforms allows non-technical users to adjust system prompts, models, and parameters, with side-by-side comparison becoming a standard feature.

Despite these advancements, a significant gap persists: the lack of visibility into production environments to understand actual agent behavior. Teams without robust production telemetry are effectively 'operating in the blind,' unable to fully assess the real-world impact of their agents.

While initial evaluation with spreadsheets is suitable for proofs of concept, it quickly becomes unmanageable as data scales, proving that building an eval platform is a complex systems problem, not merely a UI/UX challenge.

Agent traces are massive, semi-structured JSON objects, often hundreds of megabytes in size, which causes traditional cloud data warehouses to fail under the sheer volume and nested structure. Developers are then burdened with maintaining custom infrastructure for real-time querying and long-running analytical tasks.

Developing custom abstraction layers, such as BTQL, introduces yet another layer of technical complexity. This highlights that internal evaluation platforms often struggle to scale due to the inherent difficulty of handling such data at volume and complexity.

AI agent payloads present significant technical challenges because they are tens to hundreds of megabytes, far exceeding traditional kilobyte-sized heartbeat logs.

The data structures are deeply nested and semi-structured, complicating text querying and requiring read patterns that support both massive aggregations and precise point-in-time snapshots. The system must simultaneously accommodate access for AI engineers, product managers, and subject matter experts.

Agents have become integral to evaluation platforms, enabling headless operations through natural language commands.

Coding agents can proactively identify poor user experiences by analyzing recent data and trigger evaluations, automating the discovery of 'unknown unknowns' such as silent failures or repetitive user frustration. The improvement loop is evolving from manual intervention to agent-suggested iterations, shifting human responsibility to reviewing outcomes and selecting which versions to deploy.

Coding agents serve as a key mechanism for executing evaluations and logging their results, leveraging their access to underlying infrastructure and codebase.

The objective is to create a headless experience where developers can use natural language prompts to initiate tasks, such as instructing an agent to "Find me all traces in the last 24 hours where the user had a poor experience. Run the evals for me."

Manual evaluation by engineers and product managers is a laborious process that Braintrust aims to automate.

By running inference on logged tracing data, the platform automatically surfaces insights and 'unknown unknowns,' identifying silent failures or instances of repeated user prompts due to frustration, thereby moving beyond manual processes to achieve scaled operations.

Underlying databases, permission management, and data masking are critical requirements for creating a platform that moves beyond simple UI or spreadsheets. Each one of these components highlights how evaluation must function as a systems engineering challenge.

Critical backend components include underlying databases, comprehensive management of controls and permissions, and secure data masking capabilities. The platform must also support iterative improvement loops where coding agents suggest application changes, ultimately shifting human responsibility to reviewing outcomes and choosing which version to ship to production.