What is LLM Ops for an agentic-first company?

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 agents take on the repeatable work so people get more done. The bots don’t take over the decisions. People 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, a pile of agents is just a lot of confusion running at once.

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)

Nothing outbound goes live until it passes offline evals, golden tests, and a person’s approval. Dry-run by default. No agent real-money authority. That’s LLM Ops, not theater.

What this is not

• Not a claim of unsupervised publish-without-review
• Not a percentage ROI pitch without Board-verified baselines
• Not “one mega-prompt replaces the org chart”

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.

MeltingFace