What we still need to verify : 4 points in this profile are not yet confirmed against vendor documentation.
- Evaluator catalog and which checks are security specific: verify against vendor docs
- Integration and framework support: confirm, only partially known
- Boundary between the free and commercial tiers: confirm
- Self hosted deployment availability: unconfirmed
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
Future AGI is an evaluation and observability platform for applications built on language models. The core loop is familiar for this category: instrument the application so that prompts, retrieved context, intermediate steps and responses are captured, then run scoring over those records to say whether each output was acceptable. Scoring is handled by the platform's own evaluators rather than requiring every team to write and maintain judge prompts, which is the main thing it is selling against a do it yourself harness.
Alongside offline evaluation it offers checks intended to run against live traffic, applying the same criteria to inputs and outputs in the request path so a failing response can be caught before a user sees it. The security relevant portion of that is filtering for injection attempts on input and for sensitive or policy violating content on output. The platform also supports building test datasets and comparing runs, so a prompt or model change can be measured against a fixed set of cases instead of assessed by reading a handful of outputs.
Where it fits
This sits with the team building the AI feature, in development for iteration and in production for monitoring. Security's involvement is in defining what counts as a violation and reviewing the resulting rates, not in operating the tool day to day. Prerequisites are instrumentation of the application and a set of representative test cases, without which evaluation has nothing to measure against.
Strengths
- Prebuilt evaluators lower the barrier to systematic evaluation for teams that would otherwise ship on spot checks.
- One toolchain for offline testing and runtime checking keeps criteria consistent across both.
- Dataset and comparison workflow makes prompt changes measurable rather than anecdotal.
Limitations
- Evaluation is probabilistic. Scores are indicators, and treating them as pass or fail gates without human review produces both false confidence and false alarms.
- This is a quality and reliability platform with security checks attached, not a security product. It will not replace dedicated red teaming or an enforcement gateway.
- A younger vendor in a crowded category, so longevity, support depth and roadmap stability carry more risk than with established platforms. Verify current capabilities directly.
Who it suits
Small to mid sized teams shipping LLM features who want structured evaluation without building a harness themselves. Organizations with strict data residency requirements or a formal model risk process should confirm deployment and control details carefully before committing.
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