True Agentic AI isn't a single, all-powerful bot. It's an orchestrated system of multiple, specialized agents, each performing a single task (e.g., qualifying, booking, analyzing). This 'division of labor,' mirroring software engineering principles, creates a more robust, scalable, and manageable automation pipeline.

While current AI tools focus on individual productivity (e.g., coding faster), the real breakthrough will come from systems that improve organizational productivity. The next wave of AI will focus on how large teams of humans and AI agents coordinate on complex projects, a fundamentally different challenge than simply making one person faster.

Training AI agents to execute multi-step business workflows demands a new data paradigm. Companies create reinforcement learning (RL) environments—mini world models of business processes—where agents learn by attempting tasks, a more advanced method than simple prompt-completion training (SFT/RLHF).

Contrary to the view that useful AI agents are a decade away, Andrew Ng asserts that agentic workflows are already solving complex business problems. He cites examples from his portfolio in tariff compliance and legal document processing that would be impossible without current agentic AI systems.

The primary interface for managing AI agents won't be simple chat, but sophisticated IDE-like environments for all knowledge workers. This paradigm of "macro delegation, micro-steering" will create new software categories like the "accountant IDE" or "lawyer IDE" for orchestrating complex AI work.

Unlike coding with its verifiable unit tests, complex legal work lacks a binary success metric. Harvey addresses this reinforcement learning challenge by treating senior partner feedback and edits as the "reward function," mirroring how quality is judged in the real world. The ultimate verification is long-term success, like a merger avoiding future litigation.

Harvey's initial product was a tool for individual lawyers. The company found greater value by shifting focus to the productivity of entire legal teams and firms, tackling enterprise-level challenges like workflow orchestration, governance, and secure collaboration, which go far beyond simple model intelligence.

Don't view AI tools as just software; treat them like junior team members. Apply management principles: 'hire' the right model for the job (People), define how it should work through structured prompts (Process), and give it a clear, narrow goal (Purpose). This mental model maximizes their effectiveness.

A new ecosystem is emerging where law firms are not just end-users of Harvey's AI but also channel partners. They are leveraging their expertise to help their in-house legal clients adopt and implement the technology, creating a new, high-margin line of business for themselves as tech consultants and implementers.