The ALTK-Evolve team introduced a Consistency Analyzer and consistency guidelines that target the gap between average agent accuracy (Mean@k) and all-runs success (Pass^k). On AppWorld with a GPT-4.1 ReAct agent, the approach cut the 24.4-point consistency gap to 12.0 points without lowering average accuracy. The tools ship in the open-source repo and apply to production traffic since diagnosis needs no ground truth or end-to-end replay.
An arXiv paper names and quantifies the 'consistency gap': LLM agents can average strong pass rates while rarely succeeding on every run of the same task. It argues closing this gap is a precondition for trustworthy agent deployment and proposes self-evolving agents that learn to stay on course.
An arXiv paper names and quantifies the 'consistency gap': LLM agents can average strong pass rates while rarely succeeding on every run of the same task. It argues closing this gap is a precondition for trustworthy agent deployment and proposes self-evolving agents that learn to stay on course.
A practitioner argues agent evaluations wrongly collapse everything into a single pass/fail rate, conflating outcome failures with reliability failures. The post distinguishes agents that complete the wrong task reliably from agents that complete the right task despite provider errors and retries, and calls on benchmark designers to separate the two axes.
A practitioner argues agent evaluations wrongly collapse everything into a single pass/fail rate, conflating outcome failures with reliability failures. The post distinguishes agents that complete the wrong task reliably from agents that complete the right task despite provider errors and retries, and calls on benchmark designers to separate the two axes.
A new paper introduces TRACE, a self-evolving skill bank that helps LLM agents maintain consistency and recognize their limits, addressing a reliability gap exposed by CAR-bench, a benchmark for in-car assistants. The approach enables agents to handle ambiguous user requests through multi-turn dialogue and tool use while adhering to domain policies.
A new paper introduces TRACE, a self-evolving skill bank that helps LLM agents maintain consistency and recognize their limits, addressing a reliability gap exposed by CAR-bench, a benchmark for in-car assistants. The approach enables agents to handle ambiguous user requests through multi-turn dialogue and tool use while adhering to domain policies.
A new article argues that evaluation infrastructure, not model building, is the key to shipping reliable AI agents, citing McKinsey data showing 72% of organizations have deployed agents but only 11% reached production scale. The piece frames Eval Engineering as a systematic discipline for designing and maintaining agent evaluation systems.
A new article argues that evaluation infrastructure, not model building, is the key to shipping reliable AI agents, citing McKinsey data showing 72% of organizations have deployed agents but only 11% reached production scale. The piece frames Eval Engineering as a systematic discipline for designing and maintaining agent evaluation systems.
Prefactor, an AI agent evaluation company, reports that agents which pass staging evals often drift in production traffic, performing slightly different tasks or touching unintended data. Standard evals miss this because they are snapshots, not continuous checks.
Prefactor, an AI agent evaluation company, reports that agents which pass staging evals often drift in production traffic, performing slightly different tasks or touching unintended data. Standard evals miss this because they are snapshots, not continuous checks.