What we still need to verify : 2 points in this profile are not yet confirmed against vendor documentation.
- Current module names and the evaluator catalog: verify against vendor docs
- Self hosted deployment options and supported frameworks: confirm
Treat these points as unconfirmed. They are open items in the catalog's verification queue, and this note stays until each is checked against the vendor's documentation.
What it does
Galileo instruments an LLM or agent application and scores what it produces. Instrumentation captures the full request context, which for a retrieval augmented or agentic system means the user input, the retrieved chunks, each intermediate model call, each tool invocation and its result, and the final response. Scoring then runs over those records against metrics such as groundedness of an answer in its retrieved context, retrieval relevance, instruction adherence, and for agents whether the correct tool was selected and whether the execution path completed sensibly.
The distinguishing technical choice is in how scoring is done. Rather than sending every span to a large general purpose model acting as judge, the platform uses small evaluation models trained for specific scoring tasks. That changes the economics enough to evaluate a meaningful share of production traffic rather than a thin sample, which matters because rare failures are the ones that hurt. The same evaluators can be placed in the request path as guardrails, where a response that fails a check is blocked, retried or overridden before a user sees it. Security relevant checks sit alongside quality ones in the same catalog: prompt injection detection on input, sensitive data detection on output, and policy adherence.
Where it fits
Owned by the AI engineering team, used across development and production. During development it supports prompt and model comparison against pinned datasets. In production it runs as continuous evaluation, with the guardrail path optionally inline. Security's role is to specify which checks constitute a violation and to consume the resulting metrics. You need to instrument the application and to define what a correct answer looks like for your domain, which is the real work.
Strengths
- Purpose built scoring models make high volume evaluation affordable, so coverage is broad rather than sampled thin.
- Agent aware tracing scores tool selection and execution path, not just the final text.
- The same evaluator definitions serve offline testing and inline enforcement, which keeps development and production aligned.
- Groundedness scoring against retrieved context directly targets the dominant failure mode in retrieval systems.
Limitations
- Any automated evaluator is itself a model with an error rate, and that error rate is harder to inspect in a proprietary scoring model than in a prompt you wrote.
- Inline guardrails add latency and a dependency in the serving path.
- Captured traces contain prompts, retrieved documents and responses, so the platform inherits the sensitivity of everything your application touches.
Who it suits
Teams running LLM or agent features at scale who need continuous measurement rather than spot checks, and who have the engineering capacity to instrument and define domain metrics. Too much machinery for a prototype or an internal tool with a handful of users.
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