Research.

Experiments, technical comparisons and documented findings from our engineering team.

3 articles

Research · AI Research

7 min

Evaluating an AI agent in production: why traces matter

An average success rate hides an agent's path failures. This method links traces, outcome evidence, cost and reliability so teams can compare versions without trusting a single score.

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Research · AI Research

8 min

Test-time compute in LLM reasoning: what does it actually measure?

An LLM can spend more inference compute by sampling, verifying or adapting its reasoning budget. The useful measure is not answer length, but the trade-off between quality, cost, latency and reliability on representative tasks.

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Research · AI Research

7 min

How to evaluate an LLM beyond public leaderboards

A public leaderboard measures capabilities under one protocol, not whether a system can perform your process. Use this reproducible method to evaluate quality, risk and cost on real tasks.

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