Date: August 18, 2026 Main source: Harvey Link: https://www.harvey.ai/blog/introducing-harvey-ii
Summary
Harvey introduced Harvey II, an evolution of its platform where agents inherit the context of matters and projects, plus memory of how each team works. The company also introduced Harvey Tenet, its first model tuned specifically for legal reasoning. The goal is for an agent not to have to rebuild context on every task and to be able to continue long-running work inside Spaces with persistent history, permissions and preferences. Harvey presents this system as the base for legal agents that run more steps of a process at lower operating cost.
Why it matters
The competitive edge of enterprise agents is migrating from the base model to memory, context, permissions and vertical specialization. Harvey shows a pattern that will probably repeat in other industries: a good-enough model is combined with internal data and persistent memory to create a system that is hard to replace with a generic chatbot.
Practical implications
- When designing agents for clients, treat memory, context and permissions as parts of the product, not extras.
- Verticals with repetitive processes and private knowledge can create more value than a generalist assistant.
- Measure how much context the user has to rebuild each time. Reducing that friction is a real product metric.