GPT-5.5 raises the baseline for complex production workflows. It is suited for: coding scenarios, heavy tool-calling agents, grounded assistants, long-context retrieval, product-spec-to-plan workflows, and customer-service scenarios that demand execution quality and polished responses.

Anthropic released Claude Mythos.

Then hid it.

Not unreleased. Released, and not sold in public.

Today (April 14, 2026), OpenAI officially confirmed GPT-6 pretraining completed on March 17. Pretraining lasted 18 months—far longer than GPT-5’s development cycle.

Without an Agent Harness, an AI Agent in charge of code review can look fine on day one, then forget yesterday’s criteria, delete code because it “looked unused,” and quietly edit config.yaml with nobody asking. A week later the styles are inconsistent, the test suite is broken, and the Agent has gone off the rails for reasons nobody can explain.

The problem isn’t the model. It’s the runtime environment. What determines Agent reliability is the Harness, not the model version.

OpenClaw is a personal-assistant trust model, not hostile multi-tenant SaaS. One Gateway maps to one operator boundary. Lock the official install, loopback, pairing, and allowlists first. Upgrades only matter if they can be rolled back.

The hard part is operational: official install path, who can reach the Gateway, whether skills / plugins / browser / nodes expand the blast radius, and whether a change can be validated and rolled back. What follows is the official baseline plus sanitized failures from real deployments.