Model Evaluation
Benchmarks, evals, and observability for AI systems: which numbers mean something, which are marketing, and how to measure models and agents on work that matters.
PillarEvaluating AI Models and Agents: The 2026 Field Guide
Why static leaderboards lost authority, and how to build an eval program that survives production.
Model EvaluationLLM-as-Judge Reliability: The Cohen's Kappa Every Production Eval Needs
Static benchmarks are saturated; the binding constraint on shipping LLM products is now judge reliability over time, templates, and human labels.
Model Evaluation15 Agent Benchmarks, Zero Safety Scores. Here's the Fix.
A systematic review found no leading agent benchmark integrates safety scoring, so production teams must build their own evaluation loop.
Model EvaluationLPU vs GPU Inference: Groq's 70% Latency Win, Decoded
The bifurcation debate is over on paper and messy in production; here is the practitioner's read on cost, latency, and routing.
Model EvaluationReasoning Models Break Guardrails 97% of the Time. Score It Like CVSS.
A practitioner framework for scoring jailbreak severity, choosing benchmarks, and assuming reasoning-model attackers in your red team.
Model EvaluationVLLM vs TensorRT-LLM vs SGLang: 2026 Serving Benchmark, Same Hardware
Tokens-per-second-per-dollar on identical GPUs decides more deployments than peak throughput, and tail latency plus cold start decide the rest.
Model EvaluationMultimodal Evals Are Now the Hardest Part of the Stack
Text benchmarks have saturated, so differentiation moved to vision, audio, video, and real-time duplex tasks where evaluation is still immature and gameable.
Model EvaluationMultimodal Evaluation Broke. Here's How Teams Fix It
Benchmark scores don't predict production vision AI failures. Here's the evaluation stack teams actually ship.
Model EvaluationMultimodal Evaluation Has a 35-Point Blind Spot
Benchmarks can tell you whether a model is capable; production evals tell you whether your text, image, OCR, video, and tool pipeline will survive contact with real inputs.
Model EvaluationLLM Evaluation Breaks When Teams Trust One Score
A production eval program needs offline gates, calibrated human judgment, and live monitoring tied to the failures that cost you money.
Model EvaluationAI Coding CLI Telemetry Has an SSD Problem
A Codex SQLite logging bug turns telemetry from an abstract privacy concern into a measurable workstation endurance risk.
Model EvaluationLLM as Judge Needs Calibration Before CI Gates
LLM judges can scale review, but only if you measure bias, calibrate against humans, and treat disagreement as signal instead of noise.
Model EvaluationLLM Observability Must Catch Drift Before Incidents
Production LLM monitoring works when it watches user-visible failure signals before prompt drift, hallucinations, latency, and cost spikes turn into incidents.
Model EvaluationVoice Agent Evaluation: The Four-Metric Scorecard
A reproducible four-metric scorecard for production voice agents, and why a 1.4s median latency quietly breaks human-like conversation.
Model EvaluationContinuous LLM Evaluation in Production: 7 Patterns
Offline benchmarks don't survive contact with live traffic. The binding constraint is now a release-gate eval discipline that catches drift.
Model EvaluationOpenTelemetry GenAI Conventions: Instrument AI Agents
How to instrument production AI agents against the five OTel agent spans, and where the traces land after the 2026 vendor consolidation.
Model EvaluationHow to Design a Custom LLM Eval in 2026 (Without MMLU)
With MMLU contaminated and AAII v4.1 pivoting to agentic tasks, your private eval harness is the only number that tracks your production error rate.
Model EvaluationMulti-Modal RAG in 2026: Architecture, Benchmarks, and Costs
OCR-free retrieval, late-interaction indexes, and multimodal generators have made multi-modal RAG a production pattern. Here is what the numbers say about building one.
Model EvaluationSWE-bench Is Dead: Build Your Own LLM Eval Harness in 2026
OpenAI retired SWE-bench Verified in February 2026. Here is the step-by-step playbook for a private eval suite you can ship this week.
Model EvaluationClaude Fable 5 vs GPT-5.5: Coding Benchmarks That Matter
Claude Fable 5 lands 80.3% on SWE-bench Pro with a 1M-token window built for agents. Here's where it beats GPT-5.5, what it costs, and how to pick for your codebase.
Model EvaluationAI Agent Evaluation in 2026: Beyond LLM Benchmarks
MMLU tells you what a model knows. It tells you almost nothing about whether your agent will survive production.
Model EvaluationRAGAS vs TruLens vs DeepEval: The 2026 LLM Eval Showdown
We put the three dominant LLM evaluation frameworks on one agentic tool-calling task. The same trace scored 0.9, 0.8, and 0.7. Here's why, and what to gate on.
Model EvaluationAI Agent Observability in 2026: The New Telemetry Stack
Coralogix's $200M bet, a rogue Fedora agent, and the five tools that define agent-loop telemetry this year.
Model EvaluationClaude Fable 5 First Look: Retention Rules Beat Benchmarks
The 80.3% SWE-Bench Pro headline is vendor-stated; the mandatory 30-day retention and silent safety classifier are contractual facts, and they should drive your architecture decisions this week.
Model EvaluationSWE-bench Pro vs Verified: Can You Trust Coding Benchmarks?
OpenAI deprecated the benchmark everyone quoted, an audit found graders wrong on a third of verdicts, and frontier models got caught reading the answer key. Here is what actually measures a coding agent in 2026.