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Contract review is one of those legal jobs that’s essential, repetitive, but also ripe for automation.
But not just any automation — it’s where contract review agents come into their own. Not dashboards. Not plug-ins. Not templates. Agents.
In this post, we’ll unpack what contract review agents actually are, how they work, and why they’re such a good fit for the daily grind of legal work — especially if you're dealing with high volumes of low-negotiation contracts.
What is a contract review agent?
A contract review agent is a specialised AI model (typically built on top of a large language model like GPT-4 or Claude) that’s been fine-tuned to carry out the contract review process with minimal human intervention. The key word here is autonomy.
Where traditional contract review tools show you the risks and ask you to make a decision, agents can go further: interpreting context, following internal playbook guidance, and actually suggesting revisions — or in many cases, making them.
Autonomy is the point — not just a bonus
The difference between a standard contract review feature and an AI agent is like the difference between a microwave and a sous-chef.
A feature will reheat yesterday’s NDA. An agent will prep it, season it, and serve it up with your company’s preferred fallback terms, flagged risks, and an internal confidence rating — all while you’re clearing your inbox.
This autonomy is what makes contract review such a natural home for agentic AI:
- The inputs (a contract, a playbook, and a known counterparty) are well-structured
- The tasks (identify risks, compare to standard, suggest fallback) are clearly defined
- The expectations (review fast, flag issues, reduce back-and-forth) are measurable
It’s the perfect storm for intelligent automation — and for lean legal teams, it means way less time spent reworking redlines and rechecking boilerplate.
What types of contracts work best with review agents?
Let’s be clear. Not every contract is built for automation. You’re not going to hand over a 300-page cross-border M&A agreement to an agent and hope for the best.
But there’s a large category of contracts that are perfectly suited for AI review:
1. High-volume, low-negotiation contracts:
These contracts are often required as a formality. They follow rigid templates, rarely get negotiated, and are primarily reviewed to catch glaring issues like wrong jurisdiction or missing terms. That makes them a perfect sandbox for autonomous agents: minimal variation, maximum efficiency.
2. Standard sales and procurement contracts:
These are critical to revenue and operations — but they can be repetitive and time-consuming. Contract review agents can enforce your playbook at speed, flag non-standard terms, and suggest fallback positions so deals don’t get stuck in legal limbo. Perfect for fast-growth teams trying to scale without the legal bottleneck.
3. Employment and HR-related documents:
These docs are high-frequency, especially in hiring sprints, and typically have low legal complexity. They’re governed by internal policies and employment law — both of which can be embedded into a review agent’s ruleset. That means consistent application of terms and fewer manual checks for the legal team.
These agreements are high-frequency, structurally predictable, and full of low-stakes risks, which means an AI agent can handle them confidently, consistently, and often faster than a a busy in-house lawyer.
How contract review agents actually work
Here’s what happens under the hood (simplified, but you get the idea):
- Ingest: You drop a contract in (via platform or API). The agent segments it into clauses and identifies the contract type.
- Compare: The agent checks each clause against your internal playbook. It asks: Is this acceptable? Is it compliant? Have I seen this before?
- Interpret: Using natural language processing, the agent understands nuance — spotting semantically risky clauses even if the wording is unfamiliar.
- Decide: Based on your rules, fallback language, and the deal context (e.g. jurisdiction, counterparty, value), the agent flags issues and suggests redlines.
- Surface: The agent presents the flagged issues with explanations — so a human lawyer can validate, revise, or override.
It’s smart enough to follow your guidance, but transparent enough to let you take back control at any time.
Your secret weapon: a robust, AI-ready playbook
Here’s the truth: a contract review agent is only as good as the playbook it follows.
Think of the contract playbook as the moral compass of the agent. It tells the agent what’s acceptable, what’s risky, and what to do when things fall outside the norm. Without it, even the best AI is guessing.
At Juro, we’ve seen the best results when legal teams invest in building clear, structured playbooks that:
- Define acceptable clause language (and redlines)
- Provide clear and actionable reasoning (with examples) on why decisions are made
- Include fallback positions for each issue
- Set rules by contract type and business unit
- Clarify how aggressive or conservative to be
The tighter the playbook, the more confident and autonomous your agent can be. Plus, the more trust your team can place in the workflow.
Fortunately, the Juro team has been proactively thinking about what an exceptional playbook looks like in the AI era. Here are some tips you can implement straight away:
1. State the purpose clearly
At the top of the playbook, explain exactly what it’s for — e.g. “This playbook is for [Insert company]’s sales team to use during MSA reviews.” This helps AI select and apply the correct playbook.
2. Use a structured, tabular format
Tables make content easier for AI to interpret. Include columns for clause title, preferred wording, fallback positions, escalation triggers, logic rules, and rationale.
3. Be specific and literal
Avoid vague language or legalese. Use exact thresholds and outcomes instead. For example, say “limit must be ≥ reasonable fees” instead of “commercially reasonable cap.”
4. List clause triggers and keywords
AI identifies clauses using patterns, so give it examples. For each clause type, include common phrasing, synonyms, and variations it might encounter.
5. Define if/then logic clearly
AI needs clear decision-making rules. Spell out the logic for each clause: “If clause = preferred, approve. If fallback, approve with note. Else, escalate.”
6. Break clauses into atomic parts
Split compound or multi-concept clauses into smaller, discrete entries. AI performs better when each clause serves one clear purpose.
7. Standardize clause labels
Use consistent naming conventions across templates and playbooks — e.g. always call it “limitation of liability,” not “liability cap” in one place and “caps” in another.
8. Include rationale and commentary
Explain the “why” behind preferred positions. This helps users understand the risk — and helps AI reason through the intent behind a clause.
9. Version your playbook with IDs
Assign rule IDs (e.g. GL-01 for a governing law rule). This helps with tracking changes and makes referencing easier — for both humans and machines.
10. Use diverse, practical examples
Examples help AI and users alike interpret guidance correctly. Include common, edge case, and non-compliant versions to frame acceptable boundaries.
Our General Counsel, Michael Haynes, kindly shared a template for AI contract playbooks in a recent webinar, which you can catch the recording of here. To see Juro's agentic contract review functionality in action, fill in the form below for a personalized demo.
Discover Juro's contract review agent
Juro’s AI-powered contract review agent supports third-party contracts, enabling them to be reviewed and redlined against your contract playbooks. This functionality is available directly in Juro, Slack and Microsoft Word, meaning legal and business teams can review contracts where they already work.
The AI agent reviews the contract, flags risks and deviations from your standard terms, and redlines problematic clauses with fallback language and clear reasoning.
Unlike many standalone AI contract review tools, Juro’s agent is embedded in your existing workflows. That means you get the speed and precision of AI, backed by a single system of record and dynamic contract repository.
Our bet is that with Juro you can have the agility and sheer power of AI agents, and the peace of mind that comes from an underlying system of record" - Richard Mabey, CEO at Juro
To see Juro's agentic contract review functionality in action, fill in the form below for a personalized demo. Otherwise, you can continue learning about legal AI in the resources below:
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