This is a placeholder article. It exists so the journal's links resolve to a real, styled detail page. Swap this body for live content when the blog is wired to a CMS or Markdown source.
Every rollout starts the same way: a single agent, one clean workflow, a demo that lands. Then the surface area grows. More tools, more hand-offs, more edge cases the happy path never touched. The interesting engineering isn't in getting one agent to work — it's in keeping a hundred of them honest once they're all running at once.
What actually breaks first
It's rarely the model. It's the seams between steps — the retries, the stale context, the tool that silently changed its output shape last Tuesday. Reliability at scale is a systems problem wearing an AI costume.
Context windows are not memory. Treat them like a cache and design the real state layer deliberately.
The guardrails that hold
- A supervision layer that can pause, inspect, and resume any run.
- Typed, versioned tool contracts so a downstream change fails loud, not silent.
- Deterministic replay — every decision reproducible from its inputs.
- Human checkpoints on the few steps where being wrong is expensive.
None of this is exotic. It's the same discipline that made traditional software dependable, applied to systems that now make decisions on their own. That's the whole job: compounding, not just keeping up.