Legal teams are sitting on the perfect AI use case: high volume, document heavy, and repetitive, but with real consequences when things go wrong. We have spent the last year building agentic systems for law firms and in house teams. Here is what we have learned about what works, what fails, and where to draw the line.
What works: structured triage
The single highest-ROI deployment we have seen is inbound document triage. A new matter walks in the door (contracts, depositions, discovery) and an agent classifies, summarizes, and routes it. That is it. No autonomous decisions, no client facing replies. Just an extra pair of eyes that never gets tired.
Specifically:
- Classification (matter type, jurisdiction, urgency)
- Extraction (parties, dates, governing law, key clauses)
- Summarization (with citations to source pages)
- Conflict-check pre-flight against the firm's matter database
The lawyer still does the work. The agent does the first 30 minutes of the work.
What fails: autonomous reasoning at the edges
We tried, in an early prototype, to let an agent draft suggested redlines. It worked beautifully on the easy cases, and then confidently produced subtly wrong language on the edge cases. Worse, the wrongness looked right to anyone not paying close attention.
The lesson: anywhere the cost of being subtly wrong is high, keep the human in the loop. The agent's job is to lower the cost of checking, not to replace the judgment.
The defensibility question
Every legal-AI conversation eventually arrives at: "what if the agent gets it wrong?" Two design decisions matter most:
- Citations everywhere. Every claim the agent makes points back to a specific page, paragraph, line. No untraceable assertions.
- Audit trails by default. Every prompt, every retrieval, every model output is logged. When a partner asks "where did this come from?", the answer is one query away.
These aren't features you bolt on later. They're the architecture.
Where we'd start tomorrow
If you're a legal team thinking about AI, our advice is short:
- Pick one high volume document type (NDAs, MSAs, leases, whichever one consumes the most associate hours).
- Ship a triage + summarization agent for that type only.
- Measure hours saved over six weeks.
- Use that data to decide what to automate next.
Don't try to do everything at once. The teams that win are the ones who pick a beachhead and prove it.