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BekchiAI introduces a comprehensive benchmark and platform designed to measure and control the agentic skills of large language model (LLM) agents across various tasks. By utilizing a suite of 13 tool-using ReAct agents and 2,057 deterministic test tasks, it evaluates models based on behavioral metrics that extend beyond mere accuracy, such as tool-call adherence and URL hallucination. The results reveal significant differences in performance among four leading models, emphasizing the importance of nuanced evaluation in understanding LLM capabilities.
LLM agents can be rigorously evaluated for their complex reasoning and tool-using skills, revealing critical performance disparities that traditional accuracy metrics overlook.
Large language model agents reason, call tools, and act autonomously over many steps, but their agentic skills-correctly sequencing tools, planning under dependencies, judging untrusted inputs, and grounding generated arguments-are hard to measure with accuracy-only leaderboards. We present BekchiAI, which addresses both sides: a benchmark for measuring agentic skill and a platform for observing and controlling live agents. The BekchiAI-Benchmark, a suite of 13 tool-using ReAct agents across 7 task categories (arithmetic, structured/SQL, security detection, URL grounding, planning, orchestration, and tool-policy), totalling 2,057 deterministic, committed test tasks. Every task is verifier-checkable gold answers are computed by running canonical SQL against a real database, computing the exact schedule of a directed acyclic graph (DAG), or evaluating closed-form lambdas including adversarial security samples paired with deliberately imperfect signature scanners so a score reflects the model's own judgment, not the copying of an oracle. We define a small set of behavioral metrics beyond accuracy-tool-call adherence, URL hallucination and source-match, and per-model token cost and report a four-model comparison (Qwen3.7-Max, gemma-4-31B-it, gemma4:26b, gpt-oss-120b) whose story is in the per-family spread, not the aggregate. The benchmark runs are executed using the provided evaluation scripts. BekchiAI-Platform is a complementary web-based observability and control layer for deployed agents, providing full token and latency telemetry as well as remote run termination. The benchmark, evaluation tools, and platform are publicly released.
