Your AI passed every test. Then it failed silently in production.
We find the silent failures standard evaluations miss: a guardrail quietly dropped mid-conversation, a fabricated tool call, a memory that compressed away what mattered. We engineer them out. Two of our fixes are merged into LangChain and Microsoft Semantic Kernel.
What breaks in production
A guardrail disappears mid-conversation
The instruction is still in context, but a truncation or summary step silently dropped it turns ago. The agent stops following the rule, and nothing logs that it lapsed.
Fix · mergedWe named the model-behavior failure in our taxonomy, then fixed a framework defect with the same operational failure shape in Microsoft Semantic Kernel. How we found it.
The same mistake returns
A user corrects the system, but the correction disappears into a long chat log. The next response repeats the same drift as if the repair never happened.
ToolOur open-source hermeneutic mines correction triples from chat logs and gates the next response before the same drift ships twice. The argument.
Compression changes the record
Your summary or memory layer compresses context to save tokens and quietly changes what it meant: a dropped qualifier, a paraphrase that is not what you wrote.
ToolOur open-source Fidelis Memory returns your context verbatim instead of paraphrasing it. The argument.
The run leaves no usable audit trail
Something went wrong in an autonomous run, and you cannot show what the agent actually did, in what order, or why.
ToolOur open-source agent-gorgon makes deterministic runtime control decisions and records forensic, offline-verifiable evidence of what the agent did. The argument.
Technical record
Zenodo 19042468
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