Is AI Workflow Intelligence just linting for agents?

Linting is the entry point: static, deterministic checks on specification files. The discipline extends beyond it in three ways: continuity (scheduled monitoring rather than one-shot checks), memory (a longitudinal findings record), and proof (attestation a third party can verify). A linter tells you; workflow intelligence vouches.

Does AI Workflow Intelligence replace evals?

No. Evals measure behavioral quality and need datasets and judges; workflow intelligence measures declared fitness and needs specs and rules. Teams shipping serious agents run both, typically with workflow intelligence gating earlier in the pipeline.

Why must the evaluation be deterministic?

Because the decisions it feeds are consequential and contested: blocking a release, signing an attestation, satisfying an auditor. Those decisions require that the same input always yields the same verdict for a stateable reason.

Who needs AI Workflow Intelligence first?

Anyone who ships AI workflows on someone else's behalf: agencies, consultancies, and platform teams, because they are asked to vouch to clients. Regulated industries follow closely, because their auditors ask the same question with legal weight.

What is AI Workflow Intelligence? Definition, Components, and Why It Exists

AI Workflow Intelligence is the discipline of evaluating, governing, and proving the fitness of AI workflows and agents before they execute. It answers a question that testing and monitoring do not: should this workflow ship, and can anyone prove the answer?

The discipline has four components: declarative specification (agents declare their skills, dependencies, budgets, and governance rules in inspectable files), deterministic evaluation (fitness judgments are reproducible and traceable to a versioned rule ID), longitudinal record (a maintained record of findings over time that catches drift), and verifiable attestation (a document a client, auditor, or regulator can independently verify).

AI Workflow Intelligence is not evaluation (which measures output quality after execution), not observability (which watches production behavior), not runtime guardrails (which intercept bad actions as they happen), and not governance documentation software (which maintains policies and risk registers). A mature AI operation runs all four; workflow intelligence is the one that acts before execution and the only one whose output is proof.

Delivered as a service (WIaaS), the practice becomes continuous: specifications checked on every change, drift and cost monitored on a schedule, findings accumulated into a record, attestations produced on a cadence. Cromus is the reference implementation.