2 posts tagged with "llm-agents"

Most teams shipping LLM agents with code execution capabilities make the same miscalculation: they treat sandboxing as a binary property. Either they skip isolation entirely ("we trust our users") or they deploy Docker containers and consider the problem solved. Neither position survives contact with production.

The reality is that sandboxing exists on a spectrum with five distinct levels, each offering a different isolation guarantee, performance profile, and operational cost. The mismatch between chosen isolation level and actual risk profile is the root cause of most agent security incidents — not the absence of any sandbox at all.

Most agent systems fail in production not because the underlying model is incapable. They fail because the engineering around the model is improvised. The model makes a wrong turn at step three and nobody notices until step eight, when the final answer is confidently wrong and there are no guardrails to catch it. This is not a model problem. It is an architecture problem.

Agent engineering has gone through at least two full hype cycles in three years. AutoGPT and BabyAGI generated enormous excitement in spring 2023, then crashed against the reality of GPT-4's unreliable tool use. A second wave arrived with multi-agent frameworks and agentic RAG in 2024. Now, in 2026, more than half of surveyed engineering teams report having agents running in production — and most of them have also discovered that deploying an agent and maintaining a reliable agent are different problems. The teams that are succeeding are treating agent engineering as a structured discipline. The teams that are struggling are still treating it as a vibe.