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Evaluation4 articles

Evaluation

Articles

  • AI Agent Evaluation in Production: Trajectory Benchmarks, Sandbox Harnesses, and Flakiness Mitigation

    Evaluating standard large language models relies on static input-output pairs: a fixed prompt produces a completion that an automated script compares against reference strings or grades with a calibrated judge. Autonomous AI agents break this paradigm completely. An agent executes a multi-step trajectory consisting of planning, tool invocation, environment state observation, error recovery, and variable-length decision loops. Evaluating an agent requires testing not just the final string output,

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  • IBM Research Evaluates Agentic Memory Sizing Across 8 Models: Dosage Calibrations, Ceiling Effects, and Token Efficiency

    In a technical report published on August 18, 2026, researchers at IBM Research detailed empirical evaluations on sizing and calibrating agentic memory across eight large language models. The study, conducted using the open-source ALTK-Evolve framework across the AppWorld benchmark, demonstrates that agentic memory performance is governed by capability-dependent dosage rather than uniform prompt accumulation. Agentic memory architectures typically extract procedural guidelines from prior execut

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  • Artificial Analysis Launches Search Index Benchmark for AI Agent Search APIs

    Artificial Analysis has released the Search Index, a benchmark suite designed to evaluate web search APIs for autonomous AI agents across retrieval quality, query latency, and end-to-end task economics. The initial evaluation tests seven dedicated search providers: Parallel, Exa, Firecrawl, You.com, Tavily, Keenable, and Brave. Benchmark Setup and Evaluation Methodology To isolate search API performance from model variance, the evaluation executes all tests with GPT-5.6 Luna inside Stirrup,

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  • LLM-as-a-Judge in Production: Biases, Calibration, and Architectural Trade-Offs

    Automating model evaluation with another language model (the LLM-as-a-judge paradigm) has become the standard mechanism for continuous integration, regression testing, and RLHF alignment across production AI pipelines. Traditional n-gram metrics such as BLEU and ROUGE fail to capture semantic accuracy, stylistic nuance, or complex reasoning, while human evaluation remains too slow and expensive for high-frequency deployment cycles. However, treating an LLM as an impartial arbiter introduces sig

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