I run a personal agent called Saarthi. It is built on OpenClaw, an open-source agent framework. I have configured Saarthi around my own information processing workflows: research synthesis, content management, and journaling. OpenClaw comes with a set of underlying primitives such as scheduling, memory management, file access and so on. I assembled these primitives to help with my use cases.
OpenClaw uses a similar architecture to what powers Claude Code and Codex: tool calling, persistent context, planning loops, sub-agent spawning. These are the capabilities that turn a frontier model into something that can do useful work.
But Claude Code, Codex, and OpenClaw are all general-purpose tools. They are powerful because the architecture is well engineered. They are limited because they are general-purpose. They can call tools and manage context, but they can’t tell you which workflow to automate or where an agent creates more value than it consumes in review overhead.
That limitation is getting easier to solve. LangChain’s Deep Agents library ships the same kind of primitives as open building blocks on top of LangGraph. The scaffolding that makes Claude Code effective can be assembled by any developer.
The next wave of useful agents won’t come from better frontier models and general-purpose harnesses. They will come from the people who understand a domain deeply enough to know exactly where agentic capabilities can help, what workflows to target, which edge cases matter, and where the real complexity lies.
If you’re a senior engineer who has spent years building that understanding, Claude Code isn’t going to replace you. It is the demo. Deep Agents and frameworks like it are the toolkit. Your knowledge of the systems, the failure modes, and the corner cases: that’s the part nobody else can supply.