Glossary

Plain-language definitions for the AI-agent reliability lane: failure modes, error propagation, evaluation rigor, and the terms behind the research.

Blast radius (agent systems): Blast radius is the count or fraction of downstream agents and steps a single fault reaches before containment, measured across a defined topology such as an orchestrator with N workers or a chain of depth D.
Cascade resistance: Cascade resistance is a topology-level resilience score for how well an architecture keeps one fault from becoming a system failure, ranking chain, supervisor, orchestrator-worker, and debate designs on one axis.
Chaos engineering for AI agents: Chaos engineering for AI agents is the disciplined injection of controlled faults into an agent system to measure how far each fault propagates and what fraction of it the architecture contains, each result reported with a confidence interval.
Consensus and voting reliability: Consensus and voting reliability is the cascade-resistance score applied to a voting ensemble: how far a majority lowers a fault's odds of reaching the output. The Condorcet result holds only when ballots are independent, and a majority only counts its ballots and never audits them.
Construct validity (benchmarks): Construct validity is the degree to which a benchmark measures the specific capability it claims rather than a proxy a system can score high on without having it; a benchmark is construct-valid only when its top score cannot be earned without the capability it advertises.
Containment rate: Containment rate is the fraction of an injected fault a system stops before it crosses the first hop, reported with a confidence interval rather than as a bare point estimate.
Error propagation (multi-agent): Error propagation is the process by which one agent's error becomes trusted input for the agents downstream of it, compounding hop by hop along a chain or fanning out across a topology, so a single local mistake corrupts work far from where it began.
Eval confidence interval: An eval confidence interval is the range a procedure produces that, across repeated runs of a suite, brackets a metric's true value a stated fraction of the time (say 95%); its width combines a task-set term (closed-form binomial, or bootstrap) with the seed-to-seed spread, which one run omits.
Eval reproducibility: Eval reproducibility is getting the same result from an evaluation re-run on the same data and the same parameters; it breaks when uncontrolled non-determinism such as sampling temperature, an unpinned seed, or a drifting judge model moves the score while the declared inputs stay fixed.
Failure attribution (agents): Failure attribution is the diagnostic that traces an observed multi-agent cascade back to the agent and step whose output first went wrong (the root cause), by reconstructing the run as a cascade tree and isolating its earliest corrupted node.
Fault injection (agents): Fault injection (agents) is a controlled experiment that introduces a chosen fault at a known point in an agent topology and measures how far it propagates, so its reach and containment become recorded quantities a system can be scored on.
Multi-agent debate failure: Multi-agent debate failure is the mode where correlated agents debating a task converge on a confident wrong answer, so the exchange ratifies a shared error and returns it as consensus. The result is hallucinated consensus: agreement a transcript cannot distinguish from a correct result.
Orchestrator-worker reliability: Orchestrator-worker reliability is how well a fan-out (hub-and-spoke) topology keeps one worker's faulty return from reaching its siblings. Its worst-case blast radius approaches the fan-out width when the orchestrator redistributes an unvalidated return as a subtask input.
reliability@k and pass^k: pass^k is the probability an agent solves all k runs of one task (closed form p^k). reliability@k is the lane's suite-level aggregate of pass^k: the mean across a representative task suite. It is the consistency counterpart to pass@k (best-of-k capability), not its inverse.