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Blog Topic
Designing benchmarks and eval suites.
24 metadata-ranked posts in this topic
Ranked for relevance, freshness, and usefulness so readers can find the strongest Armalo posts inside this topic quickly.
Eval-beyond-benchmarks analysis of Agentic OS Mission Control, Armalo Agent recursive self improvement, governed autonomy, trust evidence, and real-world AI operations.
Agent scorecards should combine capability, evidence quality, drift, permission safety, recourse, and recursive learning.
Flywheel analysis of Agentic OS Mission Control, Armalo Agent recursive self improvement, governed autonomy, trust evidence, and real-world AI operations.
Benchmarks matter, but production agent recognition needs receipts: task, tool, authority, evidence, failure, recovery, and consequence.
Enterprise buyers should ask agent vendors for mission control artifacts, not just model benchmarks and polished workflow demos.
Benchmark scores measure task completion on curated inputs. They tell you almost nothing about how an agent will behave when inputs are adversarial, ambiguous, or outside its training distribution. Here is what actual evaluation looks like.
Trust Architecture Benchmarks for AI Platforms through a benchmark and scorecard lens: how to compare trust stacks without rewarding pretty dashboards over actual control quality.
Most eval suites cover the easy 80 percent of behavior and pretend that is the whole surface. Coverage mapping makes the blind spots visible so you can decide whether you are willing to ignore them.
Agent of the Year should reward repeatable usefulness under authority, not the most cinematic launch video or benchmark screenshot.
Recursive agents can improve the benchmark, the scaffold, or the evidence path. Mission control has to know which one changed.
Verification agents should not collapse uncertainty into clean verdicts. They need an interface that preserves ambiguity, evidence strength, and escalation conditions.
LLM judges are becoming trust infrastructure, but rubrics drift, criteria conflict, and evaluation language can quietly change what agents are rewarded for.
Agentic security systems can find more bugs faster, but their value depends on proof, triage cost, exploitability, and the economics of false positives.
Once an agent knows the eval, it games it. Helpfulness becomes sycophancy, refusal becomes paranoia, accuracy becomes hallucinated confidence. Defenses exist.
An agent's score can drop 80 points without the agent changing because the judges got better at noticing flaws. How to disentangle agent drift from judge drift.
A single LLM judge has bias profiles you cannot see. Length bias, position bias, self-preference, sycophancy. Three independent model families is the floor.
A jury that always returns a verdict is a jury that hallucinates when it should not decide. Calibrated refusal lets judges abstain when their confidence does not justify a vote.
An agent that gets the answer right but reports false confidence is more dangerous than one that's wrong and admits it. Self-report fidelity is a first-class eval dimension.
Quantile trimming beats z-score trimming when judges can be bribed. Fixed bribe cost, no variance leak, no need to estimate the noise distribution.
When a pact violation goes to dispute, the eval that scored it has to be reconstructible. Provenance is the difference between a verdict and a hand-wave.
Five judges, one hundred cases, forty cents a judgment is two hundred dollars per evaluation. Run that nightly across a fleet and the eval bill exceeds the inference bill. Here is how to spend less without measuring less.
Judge models update. Re-running last quarter's evaluations with this quarter's jury produces different verdicts on identical evidence. Here is how to handle that without rewriting history.
Happy-path evals lie. An agent that's 99% accurate at 1 QPS is often 70% accurate at 100 QPS with adversarial noise. Build evals for the failure surface, not the demo.
Lab evals lie about production. Live sampling is the only way to know how an agent really behaves. Here is the sample-and-shadow pattern, the latency budget, and the sampling plan that makes it work.
Safety Research
A public roadmap for calibrated workspace research across eight evidence gates: calibration, behavior, specificity, entanglement, sparse features, agent telemetry, self-monitoring, and adversarial robustness.