Agent Builder

Harvey's Agent Builder is the feature that justifies the $1,200+/seat/month price tag. It lets law firms create custom AI agents — purpose-built workflows that execute multi-step legal tasks without manual intervention. As of 2026, firms have built 25,000 custom agents on the platform, processing everything from M&A due diligence to antitrust filing analysis.

This isn't prompt engineering. Agent Builder is a no-code platform for constructing AI workflows that mirror how your firm actually works — your checklists, your review standards, your risk flags. Here's how it works and what you can build.

What is Harvey AI Agent Builder?

Agent Builder is Harvey's workflow automation platform that lets firms design, test, and deploy custom AI agents without writing code. Each agent is a multi-step workflow that takes an input (a document, a dataset, a question), processes it through a series of AI-powered steps, and produces a structured output.

Think of it as building a digital associate with your firm's institutional knowledge baked in. An M&A due diligence agent might: (1) ingest a data room, (2) identify material contracts, (3) flag change-of-control provisions, (4) check for non-compete restrictions, (5) generate a risk summary — all automatically.

The 25,000 agents already running across Harvey's client base prove this isn't vaporware. Firms are building real workflows that handle real legal tasks at production scale — 700,000 daily tasks across the platform.

How to build a custom agent in Harvey AI

The Agent Builder workflow follows a straightforward pattern:

Step 1: Define the task. What does this agent do? "Review NDAs for non-standard terms" or "Analyze SEC filings for material risk disclosures" or "Extract key dates and obligations from lease agreements."

Step 2: Design the workflow. Map out the steps your best associate would follow. Agent Builder lets you chain AI actions: document ingestion, clause extraction, comparison against standards, risk flagging, output generation. Each step feeds into the next.

Step 3: Set parameters. Define what "good" looks like. What constitutes a non-standard NDA term? What risk thresholds trigger flags? What output format does the partner want? These parameters make the agent firm-specific.

Step 4: Test and iterate. Run the agent against known documents where you already know the right answer. Compare agent output to human output. Adjust parameters until accuracy meets your standard.

Step 5: Deploy. Make the agent available to your team. Track usage, accuracy, and edge cases. Refine over time.

Top Harvey AI agent workflows for law firms

M&A Due Diligence Agent. Ingests virtual data rooms. Identifies material contracts. Flags change-of-control, assignment restrictions, and termination provisions. Generates a structured diligence memo. Firms report cutting due diligence time by 40-60% on mid-market deals.

Contract Review Agent. Processes 50 million contract terms per week across the platform. Extracts key terms, compares against firm-standard playbooks, flags deviations, suggests redlines. Works across NDAs, MSAs, employment agreements, and lease agreements.

Litigation Document Review Agent. Ingests production sets. Classifies documents by relevance, privilege, and issue tags. Generates privilege logs. Identifies hot documents. Handles the bulk categorization that used to require teams of contract attorneys.

Regulatory Compliance Agent. Monitors regulatory changes across specified jurisdictions. Maps new requirements against client obligations. Flags compliance gaps. Generates action item lists for compliance teams.

Antitrust Filing Agent. Analyzes merger notifications, HSR filings, and competition authority requirements across jurisdictions. Identifies potential concerns before filing. Cross-references against precedent decisions.

Agent Builder best practices from Harvey power users

Start narrow. Don't build a "do everything" agent. Build an agent that handles one specific workflow for one specific practice group. A "Review Series A term sheets for YC companies" agent will outperform a generic "review any investment document" agent every time.

Use your best associate's process. Interview the top-performing associate in the practice group. Map exactly how they approach the task. Agent Builder works best when it mirrors an existing expert workflow, not when it tries to invent a new one.

Test against gold standards. Before deploying, run the agent against 20-30 documents where a senior associate has already done the work. Compare outputs side-by-side. If the agent misses something the associate caught, adjust parameters.

Version control matters. Document what each agent does, what parameters it uses, and what changes you make over time. When a partner asks "why did the agent flag this?" you need to trace the logic.

Measure ROI per agent. Track time saved, errors caught, and matter efficiency. Some agents will deliver 10x ROI. Others won't justify the setup time. Kill the underperformers and double down on winners.

Agent Builder limitations and workarounds

No export or portability. Agents built on Harvey stay on Harvey. There's no way to export your 50 custom workflows to a competitor's platform. This is Harvey's stickiest lock-in mechanism — and it's intentional.

Quality depends on design. Agent Builder is a tool, not magic. A poorly designed agent produces poor results. Firms that invest in agent architecture — mapping workflows carefully, testing rigorously, iterating based on feedback — get dramatically better results than firms that rush deployment.

Complex multi-jurisdictional work is harder. Agents that work perfectly for Delaware corporate law may struggle with UK or EU equivalents. Cross-border workflows require careful parameter tuning for each jurisdiction.

Human review is still mandatory. Even the best agents make errors. Harvey's own documentation recommends human review of all agent outputs. Agent Builder automates the first 80% of the work — the last 20% still requires a lawyer's judgment.

The Bottom Line: Agent Builder is Harvey's true competitive moat — 25,000 custom agents processing 700K daily tasks prove it works, but it requires serious workflow design investment to deliver real ROI.