Reliability infrastructure for AI agents

Models violate their own instructions up to 80% of the time.We catch every violation — deterministically.

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The Problem

42–80%

of the time, models violate their own instructions under conversational pressure

IBM CRAFT, EMNLP 2025

44%

of agent failures are architectural — not capability. Better models won't fix them.

MAST, NeurIPS 2025

11%

detection accuracy of frontier LLMs reviewing agent traces. The industry standard is a coin flip with a broken coin.

TRAIL, Patronus AI 2025

Policy Intelligence

Your prompt is the spec. We make it enforceable.

Paste a prompt, get enforceable rules. Edit the prompt, rules update. When a trace breaks a rule, we show you the exact line to fix.

Structural Detection

Schema validators catch shape.

LLM-as-judge attempts quality.

Neither catches sequence.

We do, on every trace.

  1. search_ticket320 ms
    get_customer180 ms
    process_refund410 ms
    Right tool, wrong timeprecondition verify_identity skipped
    send_confirmation240 ms

Monitoring

AI is nondeterministic. It needs its own type of monitoring.

Same request passes at 2pm and fails at 3pm. We evaluate every trace against your rules and point to the instruction producing the drift.

Outcome-Linked Severity

Fix what broke something.
Not what fires most.

Every violation scored by the failures it caused — escalations, reverts, refunds, whatever your team tracks. The noisiest rule rarely hurts you most.

Get Started

5 minutes, not 5 sprints

No scorers. No criteria files. Paste your system prompt and Aiyara extracts the rules automatically.

pip install aiyara

import aiyara

aiyara.init(
    api_key="ak_…",
    prompt="…your system prompt…",
    auto_instrument=True,
)
# Rules extracted. Every trace evaluated. Prompt diffs flagged.
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