LLM Agents That Actually Ship

The AI agent space is full of impressive demos and zero production systems. Here's what I've learned building agents that actually work in the real world.

The Demo Trap

Most agent frameworks optimize for the demo, not production. They work great when:

But production is different. Production has edge cases, angry users, and uptime requirements.

Production-Ready Principles

1. Constrain the Problem Space

Don't build a general-purpose agent. Build one that's really good at one specific thing.

2. Design for Failure

Agents will fail. Design graceful degradation and clear escalation paths.

3. Human-in-the-Loop by Default

Start with human oversight. Remove it gradually as confidence builds.

4. Observability First

You need to understand what your agent is thinking and why it made specific decisions.

A Real Example: Document Processing

At ScopeLabs, we built an agent that processes insurance documents. Here's what made it production-ready:

The Path Forward

Start with a narrow use case. Build confidence through iteration. Scale gradually.

The future belongs to agents that ship, not agents that demo.