Insights · Field Notes 01 · Featured
Wait. Remember. Recover.
We spent the last ~10 months building, deploying, and operating agents in production across energy, supply chain, and logistics. Every company we worked with already had access to OpenAI, Anthropic, Gemini, and other models.
Access to models is not the bottleneck. The actual problem is putting agents into production to complete jobs end-to-end, create measurable value, and compound knowledge from enterprise-wide usage.
Enterprise operational work is especially hard. Agents need to operate across people, documents, equipment, approvals, and legacy systems. They need to wait for hours or days, work with partial context, survive interruptions, and recover without creating irreversible mistakes.
Meanwhile, enterprises are still stuck with copilots, limited cost visibility, legacy vendors with no AI roadmap, and renting their learning and feedback loops to Claude Code, Claude Managed Agents, Codex, or other closed-source harnesses.
Executives are now asking urgent questions about token costs, security, privacy, AI sovereignty, and measurable impact.
We believe most existing attempts are weak solutions — like harness routers, adding new instructions to system prompts, custom skills, agent orchestration, and custom hooks — because they do not address the root cause.
We are proud and humbled to have finally solved these problems at scale.