AI/LLM Benchmarks

How do you know an AI claim is real and not marketing theater?

Benchmarks provide the reference context required to judge performance.

Benchmark Role

Without Benchmarks	With Benchmarks
"Our model is better" claims	Comparable, repeatable evaluation
Vendor storytelling dominates	Evidence dominates
Optimization is random	Improvement targets are clear
Decisions follow hype cycles	Decisions follow measured thresholds

Benchmark Types

Type	What It Measures	Why It Matters
Capability	Reasoning, coding, retrieval, tool use	Can it do the task at required quality?
Reliability	Consistency, hallucination rate, failure modes	Can it be trusted in production?
Efficiency	Latency, throughput, cost per output	Can it scale economically?
Safety	Policy adherence, harmful output controls	Can it operate within governance bounds?
Robustness	Performance under distribution shift	Does it degrade gracefully in the real world?

Decision Thresholds

Define thresholds before model selection:

Dimension	Example Threshold
Quality	>= target pass rate on domain eval set
Latency	<= required response budget for workflow
Cost	<= allowed unit economics per task
Reliability	<= failure rate trigger threshold
Safety	0 critical policy failures in test suite

No threshold, no valid decision.

Workflow Benchmarks

For AI agents and assistant workflows, benchmark task utility directly:

Dimension	Benchmark Question
Task completion	Does the system complete the intended workflow end to end without retries?
First-pass accuracy	What percentage of outputs pass human or automated checks on first run?
Tool reliability	How often do tool calls fail, timeout, or require manual intervention?
Recovery behavior	When failures occur, does the system recover safely and continue?
Human override rate	How often does an operator need to intervene to finish the task?

If these do not improve, the AI layer adds complexity without operational leverage.

Bullshit Detection

A claim fails if any item is missing:

1. Named benchmark or dataset
2. Baseline comparison
3. Reproducible evaluation method
4. Error bars or confidence context
5. Cost and latency alongside quality

If a vendor shows only one metric, assume selection bias until proven otherwise.

Benchmark Stack

Layer	Standard Need	Output
Data	Canonical eval set definitions	Comparable tests
Protocol	Shared scoring rules	Repeatable results
Infrastructure	Logging and traceability	Audit-ready evidence
Governance	Acceptance and kill criteria	Controlled deployment decisions

This is the same logic as industrial quality control.

Operating Cadence

Cadence	Activity
Pre-deploy	Baseline and candidate benchmark run
Weekly	Drift checks on core tasks
Monthly	Cost/latency/quality re-optimization
Quarterly	Benchmark suite revision and threshold reset

Context

• Benchmark Standards — Shared trigger logic across benchmark families
• Performance — Scoreboard logic and metric discipline
• Standards — Why standardization enables valid comparison
• Process Optimisation — Improvement loop for benchmark operations
• A2A Protocol — Inter-agent coordination requires shared protocol standards
• Quality Assurance — QA/QC process controls to reduce benchmark drift
• Manufacturing — Practical quality-system parallels for repeatable operations