Decision Graph vs. Model Registry — Govern Every Decision, Not Just Every Model

ModelOp governs your AI models (deployment, versioning, monitoring). Mala governs every decision those models make (what was decided, why, who approved it, sealed as legal proof). You need both — ModelOp for the pipeline, Mala for the decision system-of-record.

ModelOp is the gold standard for enterprise MLOps: model inventory, deployment pipelines, drift monitoring, and model-level risk scoring. If you have hundreds of models in production, ModelOp is essential infrastructure. But ModelOp operates at the model level. It knows *which* model produced an output. Mala operates at the decision level — it knows *why* the agent decided what it decided, *which policy* applied at that exact moment, and provides a cryptographically sealed decision trace as legal proof. When a regulator asks 'Show me every AI decision that affected a credit application in Q3' — ModelOp shows you the model version. Mala shows you the decision graph: intent, context, policy applied, human approval if required, and the SHA-256 seal proving it hasn't been altered. For agentic AI in particular, ModelOp's model-centric governance has a gap: agents make chains of decisions, not single model calls. Mala's decision graph captures the full reasoning chain across multi-step agent workflows. Use ModelOp to govern your model lifecycle. Use Mala as the decision system-of-record for every outcome those models produce.

Does Mala replace ModelOp?

No. ModelOp and Mala operate at different layers. ModelOp governs your model inventory, deployment lifecycle, and drift monitoring. Mala governs the decisions those models produce — sealing each one as a cryptographic proof of what was decided, why, and under which policy. Most enterprises that take agentic AI to production will eventually need both.

What does ModelOp miss for agentic AI?

ModelOp was designed for ML model governance — a world of discrete model calls. Agentic AI involves chains of decisions across multiple models, tools, and policy checks. Mala's decision graph tracks this full reasoning chain, capturing each step's intent, context, and policy application. ModelOp sees the models; Mala sees the decisions.

How does Mala handle multi-agent decision chains?

Mala's Ambient Siphon instruments at the agent framework level — LangChain, CrewAI, AutoGen, custom agents. It captures each decision node in the chain, links them into a queryable decision graph, and seals the entire chain with a single integrity proof. This gives you a complete audit trail for multi-step agent workflows.