Eval debt is today's technical debt
Without custom evals, cheaper models cost more and agents leak money.
Three teams converge on the same fix: evals from your own work history, not benchmarks.
The hidden cost floor. Token price hides real cost: a model that costs 14% more per task while returning worse results.
One team of 10 engineers burned $1.5M in a month on unlimited model access, then cut spend 90% after building custom evals from their own work history.
Guardrails before wallets. Agent payments introduce attacks no model prevents: price manipulation, duplicate charges, prompt injection upselling.
Three sources converge on price checks, dedup tokens, cache behind auth, bound plans, and a human escalation tier before any agent touches a wallet.
Testing without containers. Distributed protocol testing is flaky and slow because of I/O.
The sans-I/O pattern isolates logic into a state machine, enabling deterministic tests of thousands of nodes in virtual time on a laptop.
My take
Evaluation is the new observability.
I treat it as ongoing infrastructure, not a one-time benchmark.
Teams that build evals from their own history cut costs by 90% and catch failures models miss.
The all-in agent framing is dangerous without those guardrails.
Start narrow, measure hard, expand only after your eval suite proves it works.
The videos behind this brief
We watched these so you don't have to.
Let Your Agent Pay? 6 Attack Vectors You Need to Patch FirstAI EngineerWatchConcrete attack vectors and fixes every team building agent payments should see.
The Cheaper Model That Costs More: Why Custom Evals MatterAI Engineer
AI in 2026: From Model Selection to System EngineeringInfoQ
The real recipe for AI support: structure, not model sizeDave Ebbelaar
Distributed protocol testing: ditch containers, use state machinesGOTO ConferencesVideo and thumbnails remain the property of their respective creators. tldw.news provides editorial analysis, commentary, and discovery links to original content.