Trust and integrations drive agentic legal AI

Diving deeper into

Legal tech VP of cloud operations on evaluating legal AI tools

Interview
a product can be a smart assistant or copilot, or it can evolve into agentic AI
Analyzed 5 sources

The move from copilot to agent in legal AI will be won less by flashy chat output than by who is trusted to touch real systems and leave an audit trail. In legal work, an assistant can draft or answer questions at the edge, but an agent only becomes core infrastructure when it can pull from document management, matter systems, contract tools, and client uploads, then act inside those workflows with reliable controls and accountability.

  • In large European legal teams, the first gate is still accuracy. Domain experts create golden answers, tune outputs to reduce hallucinations, and spend most of the implementation effort on guardrails. The next gate is accountability, because autonomous action without clear traceability is not trusted in sensitive legal work.
  • The practical value of agentic AI comes from crossing data silos. The highest value setup is an agent that can read document management, CRM, ERP, contract systems, knowledge bases, and customer uploaded files together. Without those connections, the product stays closer to a drafting and research assistant than a system that can run work end to end.
  • The market already shows the split. Harvey has scaled fast, reaching an estimated $195M ARR by the end of 2025, on strong reasoning and drafting. Legora is seen as stronger on workflow structure, collaboration, and parallel agent style workflows, but weaker enterprise integrations can still limit how deeply it embeds. Ironclad shows the other path, workflow software becomes sticky once approvals, routing, and record keeping are built into daily operations.

The next phase of legal AI is a shift from answer engines to supervised systems that can retrieve, draft, route, and complete work across the legal stack. The winners will be the products that combine strong model behavior with deep integrations, regional compliance, and proof of every action, because that is what turns AI from a useful tab into infrastructure a legal organization runs on.