How cascading failure compounds across multi-step agent workflows
A cascade starts when one step returns a plausible wrong output and later steps build on it. Boundary validation and checkpoints contain it; retries do not.
Prefactor Blog8 min read
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Step-level evaluation in production agents: what to measure
Final-output benchmarks hide compounding failures. Instrumenting each step shows where accuracy drops and why workflows fail at scale.
Prefactor Blog6 min read
Multi-agent observability: spans, scores, and schema validation
Cross-boundary spans, per-agent quality scores, and schema validation isolate failures in multi-agent systems before they compound across the chain.
Prefactor Blog6 min read
How to stop AI agent quality decay before alerts fire
Silent output degradation is the leading cause of cancelled agent projects. Score per span, baseline tool calls, and trace full paths to catch it early.
Prefactor Blog6 min read
Why 85% step accuracy collapses to 20% end-to-end success
At 85% per-step accuracy, a ten-step agent completes the whole task about one time in five. Final-output scoring cannot see this; trajectory evaluation can.
Prefactor Blog8 min read
Step-level agent evaluation: measure every intermediate step
At 95% per-step accuracy, a 10-step agent succeeds only 59% of the time. Step-level spans show which step fails and why.
Prefactor Blog6 min read
How to evaluate agent behavior under model jailbreaks
Activity schemas and validation suites let you catch policy violations and capability regressions in AI agents before they reach production.
Prefactor Blog6 min read
Why multi-step agents fail in production but pass benchmarks
Step repetitions cause 17% of multi-agent failures and reasoning-to-action mismatches cause 14%, both undetected by final-output evaluation.
Prefactor Blog6 min read
How to detect agent misbehavior in production at scale
Behavior deviation detection, reasoning artifact capture, and adversarial prompt detection run continuously so human reviewers act on signals, not haystacks.
Prefactor Blog6 min read
How to detect unauthorized AI agent actions in production
Span-based instrumentation and activity schemas surface prompt injection, unauthorized access, and multi-turn drift before incidents compound.
Prefactor Blog8 min read
AI agent audit trails: what to capture and how to use it
Span records, not prompt logs, are what make AI agent investigations possible. Each field you skip is a gap you cannot close after the fact.
Prefactor Blog6 min read
Multi-agent coordination failures: why per-agent evals miss them
Agents that each pass individual evaluation can still conflict at the system level. Cross-agent span analysis and shared versioned context are required to cat
Prefactor Blog6 min read
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