How to Evaluate Agentic AI for Your Legal Team
Over the last few months, we have talked with more than 100 general counsel and heads of legal about AI. Not sales calls. Conversations. What they are trying, what is working, what is not, and what they wish they had known earlier.
Four themes came up again and again.
We use Copilot across the company, but when it comes to contracts, it does not really know our terms. It is helpful for drafting emails, not for reviewing redlines against our playbook.
We had our engineering team build an internal tool. It worked for a while, but it is fragile. Every time something changes, it breaks, and legal is not exactly the engineering team's top priority.
We tried a legal AI product last year. The demo was impressive. In practice, it added another tool to manage without really reducing the time we spend on contracts.
We are mapping alternatives and honestly, making a decision is hard. Everyone claims to be AI-powered. Everyone claims to be agentic. How do you tell the difference?
These are real patterns, not edge cases. If any of these sound familiar, this framework might help.
This article is based on a presentation our CEO Aku Pollanen gave at Global Legal Forum in February 2026 on the effective utilization of agentic AI.
The Five Questions That Actually Matter
When you strip away the marketing language, evaluating agentic AI for legal comes down to five questions. Not feature comparisons. Not analyst quadrants. Five questions that reveal whether a platform will actually change how your team works.
These questions came from those 100+ conversations. They reflect what GCs and heads of legal actually struggled with when making decisions, not what vendors thought mattered.
1. Is it truly agentic?
This is the first and most important filter. The term "agentic AI" has been stretched to mean almost anything. Vendors apply it to basic workflow automation, rules engines with a chatbot on top, and systems that do little more than auto-fill templates. Calling something agentic does not make it so.
Ask for a live demo of something the product was not specifically pre-built to handle. Any vendor can show a polished demo of their three best use cases. The real test is what happens when you throw something slightly different at it.
A truly agentic system reasons through novel situations. It breaks down a complex request, figures out the steps, uses its tools to execute them, and adapts when something unexpected comes up. A scripted workflow dressed up as "AI" will stumble the moment you go off-script.
Here is a practical test: bring your own contract to the demo. Not the vendor's sample NDA. Your messy, real-world agreement with non-standard clauses and unusual formatting. Watch how the system handles it. Does it reason through the unfamiliar parts, or does it fall back to generic responses?
Another signal: ask the vendor what happens when the AI encounters something it cannot handle. A truly agentic system knows its limits. It escalates. It tells you what it tried and where it got stuck. A scripted system either fails silently or gives a confidently wrong answer.
2. Does it have your context?
The difference between useful AI and frustrating AI is context. Can the system access your contract archive? Does it know your negotiation playbook? Can it reference your past decisions?
If you have to manually provide context every time ("here is our template, here are our fallback positions, here is what we agreed to last time with this counterparty"), you are doing the AI's job for it. The whole point of an agentic platform is that context is built in.
Context shows up in layers. At the most basic level, the system should know your templates and clause libraries. Beyond that, it should understand your negotiation playbook: which terms you always push back on, which fallback positions are acceptable, and what your risk thresholds are. At the deepest level, it should know your history: what you agreed to with this counterparty last time, how similar deals were structured, and what precedents exist in your archive.
Ask vendors specifically: "If I send you a contract from a counterparty we have negotiated with before, will your system know our history with them?" The answer to that question tells you how deep the context actually goes.
See our three layers framework for why context is what separates each level of AI capability.
3. Is it a complete platform?
Or is it one piece you will need to stitch together with four others? A contract AI tool that does not connect to your e-signature workflow, your template library, or your obligation tracker creates more integration work than it saves.
This is a pattern we see repeatedly. A legal team adopts an AI review tool. It works well in isolation. But the reviewed contract still needs to be manually transferred to the CLM for storage, manually sent through DocuSign for signature, and manually tracked for obligations. The AI saved time on one step while adding friction to three others.
The tools that deliver the most value are the ones where the AI, the documents, the workflows, and the data all live in one place. Context flows naturally. You do not need to export from one system and import into another.
When evaluating, map the full lifecycle: creation, review, negotiation, signature, storage, obligation tracking, renewal management. How many of those steps does the platform handle natively? Every gap is an integration you will need to build and maintain.
There is a second-order effect here too. When your contract data lives in one platform, the AI gets smarter over time. It can learn from your negotiation patterns, your most-edited clauses, your approval timelines. When your data is scattered across five tools, that learning never happens.
4. Can it scale across your team?
Is this an individual productivity tool or company-wide infrastructure? A tool that makes one lawyer 30% faster is nice. A platform that lets your entire legal function operate differently is transformative.
The distinction matters more than most people realize. Individual productivity tools help the people who already know what they are doing work faster. That is valuable but limited. Company-wide infrastructure changes what is possible. It lets business teams handle routine contracts without waiting for legal. It lets junior team members operate at a higher level with AI-assisted guidance. It lets legal leadership see patterns across the entire contract portfolio.
Ask: Can business teams use this to self-serve on routine contracts? Can multiple lawyers collaborate within the same system? Does it support different permission levels, approval chains, and workflow configurations?
Also ask about onboarding. How long does it take a new user to become productive? If the answer is weeks of training, the platform will not scale beyond your power users. The best platforms are intuitive enough that a sales rep can use them for a standard customer agreement without reading a manual.
5. Can you trust it?
This is the question that matters most for legal work. And it is the one that separates platforms built for legal from general-purpose AI tools adapted for legal.
Does the AI show its reasoning? When it flags an issue in a contract, can you see why? When it makes a suggestion, can you trace it back to the playbook rule or the precedent that informed it?
Does it escalate when it is unsure, or does it confidently present uncertain conclusions? Is there an audit trail for every action it takes?
Trust has a very specific meaning in a legal context. It means you can explain to your board, to a regulator, or to a court why a decision was made and what information it was based on. If the AI is a black box that produces outputs without showing its work, you cannot build that explanation. And that means you cannot rely on it for anything consequential.
There is also the question of data security. Where does your contract data go? Is it used to train models? Who has access? For legal teams handling sensitive commercial agreements, these are not abstract concerns. They are dealbreakers.
What the Ideal Looks Like
When you stack these five questions together, a picture emerges of what the ideal evaluation target looks like. It is not a checklist of features. It is a set of capabilities that compound.
Truly agentic AI combined with deep organizational context and a full legal ecosystem:
- Reviews contracts against your negotiation history, not just market terms
- Contract archive, playbooks, e-sign, workflows, and AI agents all in one place
- Context is built in, not something you provide manually each time
- Scales across your team, not just one lawyer's productivity tool
- Shows its reasoning, flags uncertainty, and maintains a complete audit trail
The platform effect matters here. Each capability makes the others more valuable. Agentic reasoning is more useful when it has context. Context is deeper when the full lifecycle is in one platform. Scaling is easier when the platform is complete and intuitive. Trust is more achievable when everything is transparent and traceable.
The Bigger Unlock
Most conversations about AI and productivity focus on making individual contributors faster. That is the intuitive frame: give a lawyer AI tools, and they do the same work in less time.
That frame misses the bigger opportunity.
The real gain from agentic AI is not making lawyers twice as fast. It is enabling routine matters to be handled without being escalated to legal at all.
Business teams self-serve on standard contracts. The AI handles intake, applies the right template, checks against the playbook, routes for approval when needed, and sends for signature. Legal reviews exceptions, not everything.
That is the 10x gain. But it requires a platform your team trusts. Not a tool that occasionally gets things right, but a system that is reliable enough that non-lawyers can use it for routine work while legal maintains oversight.
Think about the math. If a legal team handles 500 contracts per quarter and 70% are routine (standard NDAs, order forms, renewals), that is 350 contracts that follow a predictable pattern. If business teams can handle those through a trusted platform, legal just freed up 70% of its contract bandwidth. Not by working faster, but by not having to touch routine work at all.
- Each lawyer handles more contracts per day
- Legal still reviews every agreement
- Bottleneck is reduced but not eliminated
- Linear scaling: more work requires more lawyers
- Routine contracts flow without legal involvement
- Legal focuses on exceptions and high-stakes deals
- Bottleneck is removed for standard work
- Platform scaling: more volume without proportional headcount
Software engineering figured this out first. The biggest productivity gain was not faster developers. It was automating routine work (testing, deployment, code review for standard patterns) so senior engineers only review the hard stuff. The CI/CD pipeline did not make developers faster at writing code. It eliminated entire categories of manual work so developers could focus on what actually required their expertise.
Legal is heading to the same place. The organizations that get there first will have a significant structural advantage. Not just lower costs, but faster deal cycles, fewer bottlenecks, and a legal team that operates as a strategic function rather than a processing center.
The prerequisite is a platform that passes all five questions. You cannot enable self-service on a tool that lacks context, requires manual handoffs between systems, or does not show its reasoning. Self-service requires trust, and trust requires transparency, completeness, and reliability.
See Bind in Action
This framework is based on a presentation by Aku Pollanen, CEO of Bind, at Global Legal Forum in February 2026. To see how these principles work in practice, here is a walkthrough of the Bind platform.
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