Standard benchmarks fall short for multi-turn AI agents. A new approach is the 'job interview eval,' where an agent is given an underspecified problem. It is then graded not just on the solution, but on its ability to ask clarifying questions and handle changing requirements, mimicking how a human developer is evaluated.

Simply giving an AI agent a list of tasks is a recipe for misalignment. To get the desired business outcome, you must clearly define what success looks like for its specific role. Without this, the agent will define success on its own terms, often incorrectly.

The "Outcomes" feature requires a markdown "rubric" to define success. This forces developers to codify what "done" looks like, allowing the AI agent to self-grade and iterate up to 20 times. This introduces a structured, testable approach to achieving reliable results from agentic systems.

Mozilla's agent worked well because it had a definitive verification signal: a fuzzing build that clearly reports 'you win or you lose'. For projects with more ambiguous outcomes, defining a crisp, automatable success metric is a critical prerequisite for effective agentic work.

As you manage a fleet of agents, you cannot manually review every output. Platforms like HyperAgent use "Rubrics"—an evaluation framework where one LLM judges another's work against predefined criteria. This automates quality control, which is essential for scaling an agent-first business.

Treating AI evaluation like a final exam is a mistake. For critical enterprise systems, evaluations should be embedded at every step of an agent's workflow (e.g., after planning, before action). This is akin to unit testing in classic software development and is essential for building trustworthy, production-ready agents.

Building reliable AI agents requires a developer mindset shift. The most critical task is not writing the agent's code but creating robust evaluations ('evals') that define and verify the desired business outcome. This makes a test-driven development approach non-negotiable for enterprise AI.

Evaluating AI language models requires assessing five distinct dimensions: factual accuracy, logical coherence, contextual relevance, completeness, and calibrated confidence. Focusing on just one dimension, like factual accuracy, can lead to misleading quality scores and dangerously wrong outputs.

OpenAI identifies agent evaluation as a key challenge. While they can currently grade an entire task's trace, the real difficulty lies in evaluating and optimizing the individual steps within a long, complex agentic workflow. This is a work-in-progress area critical for building reliable, production-grade agents.