A person stays in charge

What Is LLM Ops for Agentic-First Companies?

LLM Ops is the discipline of running large-language-model systems in production: deploying, observing, evaluating, routing, retrieving, and gating changes so they stay safe and useful.

Agentic-first means the organization is designed so human employees amplify through agent workforces — not so bots replace human authority. Humans own goals, brand, legal identity, and spend. Agents own scoped, reviewable execution.

Together: LLM Ops for agentic-first companies is how you operate multi-agent systems without turning the company into an unsupervised chat demo.

The four pillars we use

1. Orchestration

Who works which leaf? How do handoffs and recovery work? Sole-leaf discipline prevents GPU and human attention from thrashing. Without orchestration, “agent armies” are just concurrent confusion.

2. Routing

Which model answers? Local MoE for long context, small models for classify, remote only when policy allows, human when the route fails closed. Routing is a policy problem before it is a networking problem.

3. RAG (retrieval)

Agents need institutional memory: brand kits, runbooks, issue history, approved public pages. Retrieval-augmented generation is how answers cite house truth instead of inventing product or legal claims.

4. Ops gates (HITL / dry-run / eval)

Promote only after offline eval, golden tests, and human unlock for anything outbound. Dry-run by default. No agent real-money authority. That is LLM Ops, not theater.

What this is not

Where MeltingFace fits

We research and demonstrate this stack, and we ship Presence as governed corporate presence under the same rules — brand kit as identity corpus, content CRM as orchestration for publishing agents, dry-run as the ops gate.

Start at the LLM Ops hub, then Approach and Platform.

*Research narrative for MeltingFace.io. Commercial packaging TBD; live channels require Board unlock.*

MeltingFace