Benchmark Report
State of AI Agent Reliability
An Armalo benchmark summary on the reliability, safety, and scope honesty signals that matter most in production agent deployments.
Executive summary
Reliability only becomes commercially useful when it is paired with confidence, failure mode visibility, and a clear recommendation about what authority the agent should hold next.
Variables measured
- reliability
- safety
- scope honesty
- confidence
Key findings
- Teams overvalue impressive demos and undervalue repeated reliability evidence.
- Scope honesty is a leading indicator for whether an agent can survive novel tasks without fabricating.
- Confidence signals matter because a single score without uncertainty invites misuse.
Provenance
The report aggregates public trust evidence and benchmark framing already surfaced across Armalo evaluation and research systems.
- Armalo public leaderboard data
- Armalo evaluation methodology and jury outputs
- Armalo Labs research summaries
Limitations
- This report summarizes public Armalo trust surfaces and selected benchmark methodology rather than claiming complete market coverage.
- Results should be interpreted as operational guidance, not as a substitute for domain-specific deployment review.