AloLab researches how to make AI agents that are reliable, safe, and auditable, for the highest-stakes operations in enterprise. Peer-reviewed research, open-source tooling, production-derived benchmarks.
The industry optimizes for model capability. We optimize for execution reliability. A frontier model that produces the right answer in the wrong format is as unusable as one that gets it wrong. The harness is what closes the gap.
Without a harness, every model we tested, open and proprietary, achieved strong task accuracy but produced zero parsable output. The bottleneck was never reasoning. It was structured execution.
GSM8K benchmark, naive prompting — no formatting instruction. Task accuracy measures mathematical correctness; output accuracy measures correctness + valid structured output.
We measure the work. Every tool call, reasoning step, retry loop, and failure mode, logged and classified. Standard pass/fail scoring masks the execution pathologies that make agents unreliable in production.
The direction that detects a behavior is not the direction that controls it. We measure the angle between knowing and steering — a weight-computable signature of the detection–intervention gap.
Models can be 85% accurate yet 0% usable. AloLab, an iterative prompt optimizer, reaches 84–95% structured-output accuracy from a 0% baseline — without fine-tuning.
More papers on the way. The framework and benchmarks are open-source at github.com/alomana-lab/alolab.
The paper, framework, and benchmarks are public. We publish because reproducibility is the standard, not the exception.
AloLab is the research arm of Alomana, an enterprise AI platform that runs in a private, ISO 27001-certified instance. Your production traces, models, and optimized prompts never leave your environment.
Trajectory intelligence evaluates every step an AI agent takes — tool calls, reasoning, retries, failures — not just the final output. AloLab’s research shows this step-level analysis reveals failure modes invisible to standard pass/fail evaluations.
Standard benchmarks measure task accuracy alone. AloLab research demonstrates that models can achieve 85% task accuracy while producing 0% usable output — the structured-output reliability gap makes benchmark scores misleading for production deployment.
AloLab is an iterative system-prompt optimizer requiring only black-box API access. It analyzes agent execution traces, identifies recurring failure patterns, and rewrites the orchestration harness — reaching 84–95% output accuracy at near-zero latency overhead.
Yes. The AloLab framework and benchmarks are open-source at github.com/alomana-lab/alolab. The peer-reviewed paper is available at arXiv 2605.02363.
Run AloLab on your own models and production traces. Benchmark agents against a deployed baseline. Close the reliability gap before it reaches your customers.