When the precedent hasn’t been set yet, we get to write it
Contextual AI's Agent Composer makes the case that the real enterprise AI bottleneck isn't the model — it's context, auditability, and governance baked into the infrastructure from day one.
Enterprise AI doesn't need models that can do everything. It needs models scoped to the problem. Constraint isn't a limitation — it's a governance feature.
Most companies are still debating whether to adopt AI agents. Reload is already building the HR platform to manage them. That's the gap between strategy decks and product roadmaps.
Engineers call this context management. Lawyers should call it something else: selective deletion with no retention policy.
I rebuilt the project from scratch to understand what it actually measures, where it's useful, and where it breaks down.
AI agents with memory aren't just smarter — they're harder to govern. Each memory layer creates distinct privacy and retention obligations product counsel needs to address at the architecture stage.
AI and platform engineering are converging. For governance teams, that means the platform — not the policy doc — is where your AI guardrails actually live. The architecture matters.
Six data shifts are reshaping enterprise AI in 2026 — from RAG's evolution to contextual memory becoming table stakes. Product counsel need to be in these infrastructure conversations now.
