A webinar recap covering how in-house legal teams can choose the right AI agent to solve their biggest contract management challenges without getting overwhelmed
We recently presented a webinar in partnership with Today’s General Counsel, Understanding AI Agents: How In-House Legal Teams Pick The Winning Starting Point, presented by Pramata.
This webinar, hosted by our own James Cho, Contract Management Solutions Expert, and Alex Bubier, Senior Counsel at Smarsh, aims to educate legal professionals new to the world of AI on how to get started with AI agents from step 1.
This article recaps all of the must-know info you may have missed.
Table of contents:
What is an AI agent for legal teams?
The 3 types of AI agents every legal team should know
1. The Automator: Free your team from repetitive work
2. The Analyzer: Unlock strategic insights for executives
3. The Accelerator: Streamline your contract processes
How to choose your agentic AI starting point in 4 steps
1. Identify your biggest pain point
2. Start small and specific
3. Validate accuracy first
4. Get quick wins, then scale
Real talk: What it actually takes to get started
Common challenges with AI agents
1. Analysis paralysis
2. Building in isolation
3. Ignoring data quality
4. Trying to do it alone
The path forward: How AI agents work together
If you’d like to see the full recording and view the transcript, access it here.
Let’s dive in.
What’s holding you back from AI agents?
We asked webinar participants what their comfortability is with using AI agents.
- 50% are still learning about AI agents
- 35% have tried out-of-the-box tools like ChatGPT
- 10% have built custom agents
- 5% are seasoned veterans managing multiple agents
The message: Most legal teams are in the same boat. The time is here to get started with AI agents to transform the way you work.
What are AI agents for legal teams?
AI agents are purpose-built tools that help legal teams automate contract tasks, extract strategic insights, and streamline workflows. Unlike generic chatbots, effective legal AI agents are:
- Grounded in your actual contract repository (not just general knowledge)
- User-initiated (you control when and where they work)
- Consistent and trustworthy (producing repeatable, accurate results)
- Purpose-built for specific contract tasks
The key difference? Contract intelligence. Contract intelligence is the critical context of proprietary contract data and processes that are unique to your business.
It provides invaluable information to AI agents for more accurate, useful results that reflect your standards, your playbooks, your commercial relationship history and how you ultimately want to drive agentic processes. When agents have this crucial data on your contracts, they function as a high-value member of your team.
“What you start to realize is that these agents will carry out these tasks, even if it’s a simple task, in a very similar manner to how you would if you provide it with enough information,” says Alex Bubier, Senior Counsel and AI agent super user, Smarsh.
The 3 types of AI agents every legal team should know
There are endless numbers of use cases you can use AI agents for. But, we’ve distilled these use cases down into three types that our own clients get started with. The type of agent you choose should be selected with your ultimate end goal in mind.
1. The Automator: Free your team from repetitive work
Best for: High-volume, routine requests that drain legal resources
Common use cases:
- Instantly checking if an NDA or MSA already exists with a counterparty
- Triaging contract requests and routing to the right approver
- Answering repetitive contract questions (payment terms, renewal dates, contract status)
Real-world impact: Sales teams can check for existing agreements themselves instead of asking legal dozens of times per week. One less email per request adds up quickly.
Quick win example: Build an agent that checks your repository for existing NDAs. When a sales rep requests a new NDA, they get an instant answer—and if one exists, they get the executed copy immediately.
Choose this agent if: your team has too many repetitive tasks on their list.
2. The Analyzer: Unlock strategic insights for executives
Best for: Demonstrating legal’s strategic value to the C-suite
Common use cases:
- Extracting critical terms across your entire contract portfolio (price escalators, liability caps, auto-renewal clauses)
- M&A due diligence risk analysis under tight deadlines
- Generating customizable executive dashboards with visual analytics
- Identifying renewal deadlines and revenue opportunities
Real-world impact: Instead of manually reading hundreds of contracts for M&A due diligence, an analyzer agent can surface risks, create visual dashboards, and enable legal to focus on strategic decision-making rather than data extraction.
Strategic value: Transform legal into strategic business partners by providing data-driven insights that inform executive decisions.
Choose this agent if: your executive team has questions you can’t answer quickly.
3. The Accelerator: Streamline your contract processes
Best for: Speeding up contract negotiations and review cycles
Common use cases:
- Automated redlining against your playbook
- First-pass review of third-party agreements
- Diff comparison between redlines and your templates
- Self-service NDA workflows from request to execution
Real-world impact: “With Pramata’s AI agents it now takes me from hours to minutes to complete tasks,” says Alex.
Choose this agent if: you have multiple step processes to solve. This is a more complex type of agent (often called orchestrator) that works together with other agents. Try out the first two agents before diving in with an accelerator.
How to choose your agentic AI starting point in 4 steps
The bottom line is to start based on your current business needs and the biggest pain points happening right now. This will help you show quick wins and accelerate adoption.
1. Identify your biggest pain point
Don’t try to solve everything at once. Ask: What’s costing us the most time, money, or strategic opportunity right now? Once you’ve identified the biggest pain points, don’t forget to talk to those outside of the legal team that are directly involved, this will increase adoption and show that you’re an innovative leader solving real problems with AI!
For Alex, interest from his team members at Smarsh was instant once his first agent was running:
“Once some of our business teams and other teams started to hear about some of the things that our legal group was doing with respect to creating agents and extracting information and automating workflows, they asked, “Can we get in on this? Can some of our problems also be addressed in this manner?”
2. Start small and specific
Once you’ve identified your pain point, then it’s time to solve the problem. We recommend starting with one small, highly specific task that can help free your time.
Here’s how Alex Bubier from Smarsh launched his first agent:
“I started very small. I took a simple problem that the legal team was having, a simple request that’s repeatable. I started with termination for convenience,” shared Alex. “Does this contract have termination for convenience? I built an agent to extract just that information. And it was a success!”
The key: Choosing one simple, repeatable problem. Not an end-to-end solution on day one.
3. Validate accuracy first
Before rolling out to your team, validate that the agent produces consistent, accurate results within your tolerance level. Test first, and then show results on how it works. This is the path to department-wide adoption.
Alex took a similar approach when launching his first agent at Smarsh:
“I was able to use my first agent and extend it to my internal legal team, and they too were able to quickly and easily use that agent because it was a single click, ” he shared. “They didn’t have to understand the background and the tech behind it, but they knew how to use that single-purpose agent, and they recognized that the information being returned was relatively valid.”
4. Get quick wins, then scale
Your process for scaling should look something like this:
Show results to stakeholders → Get buy-in → Build confidence → Scale to more complex agents
Throughout this whole journey, our motto here is: start smart but scale fast. One that buy-in is in place, the possibilities are endless.
Real talk: What it actually takes to get started
There is going to be a learning curve anytime you get started with something new. Here’s the reality of setting up your first AI agent, straight from people who have been where you are today:
The good news
- You probably already have the tools you need (playbooks, templates, preferred positions)
- You don’t need to be technical—modern platforms, like Pramata, make agent building accessible
- Start small—your first agent can be operational in days, not months
The reality check
Even experts like Alex have had failed first attempts. “How did I get started with AI agents? Slow, I think, is probably the best response,” shared Alex. “For context, because of the company that I work at, privacy and data protection are very important. That led me to really try to experiment with local options…And the outcome was I absolutely failed. I could not even get the program started. There were a lot of different dependencies, and it was frustrating.”
How Alex overcame barriers to launching his first AI agent:
Alex partnered with Pramata, a platform built for legal teams, which removed the technical barriers slowing his success.
Alex reflected, “My first experience was poor, but as I moved forward and really tried to continue to learn and educate, partners like Pramata have really made it much more simple to use these tools.”
“With Pramata, I was able to build an agent very quickly and easily.” – Alex Bubier,
Getting started demands the right foundation
To empower AI agents to deliver accurate results, they need to be built on the right foundation. Providing commercial relationship context specific to your business—the clean, intelligent data locked in your contracts—is non-negotiable.
Once that is in place, you must provide a clear use case. And remember, this is more important than creating a technically complex agent. After you’re up and running with a tested agent, show don’t tell stakeholders about its success.
Common mistakes with AI agents
Don’t let these common mistakes stop you from building your first agent. Remember, starting slow and small is your best course of action.
1. Analysis paralysis
Don’t wait for the “perfect” comprehensive agent to solve a muti-step process. Start with one common, high-volume pain point.
2. Building in isolation
Don’t miss out on input from your network. Show early versions to your team and business partners. Their feedback is invaluable.
Alex shared how his teams worked together: “Most legal teams play strategically in between a lot of different groups, so you have some insights into different problems your business teams have. We really started to scale our agents when we started partnering with some of the other business teams to really accelerate or address their problems.”
3. Ignoring data quality
If your repository has duplicates, partially signed agreements, and poor organization, clean that first. Otherwise, you run the risk of errors and lost trust. Clean data begets more possibilities and use cases as well:
“Agents all build on each other. In effect, I had all the information I needed to build some of these agents because I think a lot of teams have playbooks, or attorneys, even if they don’t have playbooks, have preferred approaches to the way they want to address certain issues.”
4. Trying to do It alone
Partner with vendors, like Pramata, who understand AI + contracts and can guide your journey.
The Path Forward: How AI Agents Work Together
Once you’re comfortable with your first agent, there’s a world of possibilities for what you can accomplish. With multiple agents working together, you can accomplish more complex and time consuming tasks.
Take for example an agent-powered self-service NDA:
Self-service NDA example:
- Sales rep requests NDA → Automator checks if one exists
- If counterparty provides their NDA → Analyzer does first-pass compliance review
- If issues found → Accelerator routes to legal with full context and suggested timeline
- Legal reviews → Accelerator suggests redlines based on your playbook
The result: Faster deal cycles, fewer emails to legal, better risk management.
Key Takeaways
- Start smart, scale fast: Begin with one simple, high-volume use case
- You already have the ingredients: Your playbooks and clause banks are training data
- User experience matters: Single-click agents beat chat interfaces for consistency
- Accuracy builds trust: Validate results before extending to business stakeholders
- Strategic value unlocks budget: Analyzer agents help position legal as a strategic business partner
- You don’t have to do it alone: Partner with platforms built specifically for contract intelligence
Ready to Build Your First AI Agent?
The best time to start with agentic AI may have been six months ago, but the second-best time is now.
The legal teams that embrace AI agents today will be the strategic business partners of tomorrow.
Watch the full webinar recording here for more information and a real-demo of Pramata AI agents in action.
Ready to dive in? Schedule a demo.
