Compare

Compare HELM by operating model, not by hype cycle.

These pages compare categories: agents, workflow bots, and model-governance systems. The question stays the same: who is allowed to turn an AI proposal into a side effect, and what evidence proves the decision?

Compare AI agents Read architecture brief

Category map

Start with the category visitors already understand.

Each comparison names where the other category is useful, where it breaks, and what HELM adds before execution.

AI agents

HELM vs AI Agents

AI agents can propose and attempt work. HELM checks whether the proposed side effect is allowed before it runs.

Read comparison → RPA

HELM vs RPA

RPA can repeat known workflows. HELM controls whether AI-proposed work may become an approved side effect.

Read comparison → model governance

HELM vs Model Governance

Model governance tracks model posture. HELM governs action authority before company side effects run.

Read comparison →

AI agents

HELM vs AI Agents

Useful for: Planning, drafting, research, summarization, coding assistance, and proposing structured work.
Breaks when: The agent has enough tool access to turn a proposal into a real side effect without a separate authority boundary.
HELM difference: HELM treats model output as a proposal. It checks policy, approval, connector scope, risk, and proof before execution.

RPA

HELM vs RPA

Useful for: Stable, deterministic, screen-bound or API-bound workflows with known rules and low semantic ambiguity.
Breaks when: The work depends on AI-generated intent, changing policy, mixed evidence, or approval that must be replayed later.
HELM difference: HELM is not a bot runner. It is the authority and receipt boundary in front of AI-proposed work.

model governance

HELM vs Model Governance

Useful for: Model inventory, evaluation, risk review, policy documentation, and governance reporting.
Breaks when: A governed model still needs to use tools, move data, approve spend, publish code, or touch production systems.
HELM difference: HELM focuses on execution authority: who may turn an AI proposal into action, what proof is required, and how the decision is replayed.

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