What Cromus Is — The Pre-Execution Layer for AI Workflows
Cromus is the pre-execution layer for AI workflows. Every other category of AI tool is, by design, past tense: observability platforms trace your AI after it runs, cost monitors total the bill after you've paid it, and eval platforms score outputs after the tokens are spent. Cromus is the layer before — it compiles AI workflows into portable specs and then scores cost, risk, and governance before a single token is spent, deterministically, with zero LLM calls in the analysis. The defining line is before vs. after execution: on the "after" side the money is already spent and the run already happened; on the "before" side you can still change the workflow for free, because editing a spec costs nothing while re-running production does not.
This is not cost monitoring. Cost monitors price the tokens you already burned by reading traces; Cromus estimates cost from the workflow spec before it runs at all, with no trace required, and shows which steps to change while changing them is still free. A monitor recomputes past spend; Cromus simulates future spend. It is also not observability or evals. Those answer a quality question — did the AI produce good output — and must run the AI (usually with more LLM calls) to grade it. Cromus answers whether a workflow is economical, governed, and portable, without running anything: the diagnostic path makes zero LLM calls and reads structure, not output, which makes it deterministic, repeatable, free to run, and auditable.
Cromus does three things, all upstream of the run. It compiles SOPs and workflows into portable, governed specs (SKILL.md, with ETHOS.md for behavioral rules and MEMORY.md for portable context), so governance lives inside the artifact. It scores cost, latency risk, failure risk, and structural gaps before execution, quantifying preventable waste against a verified registry of real model prices. And it governs what is allowed to run — budget ceilings, model-downgrade rules, and policy checks that pass or fail before deployment, not alerts that fire after the fact. Because Cromus never sits between the agent and the model, it adds no latency and creates no runtime dependency: it hands a validated, portable artifact to whatever you run. Cromus does not replace observability or eval stacks; it sits in front of them. A complete AI stack has a "before" layer and an "after" layer, and most teams have only the after. It is for founders protecting runway, engineering and platform teams gating deployment, agencies protecting client-work margin (and reselling optimization under their own brand), and regulated teams that need governance enforced before an agent acts.