Legal AI agents: don’t get these 5 things wrong in 2026
2026 is shaping up to be the year in-house legal teams move from experimental pilots to mainstream adoption of legal AI agents.
Across Europe and beyond, in-house legal teams are being asked the same questions by their leadership teams:
Can AI do more than just draft and summarize?
Can it actually act without a ton of oversight?
And if so… can we trust it?
The promise of autonomous legal AI is exciting and compelling, but the path forward requires careful thought and navigation to avoid costly missteps in the long run.
I sat down to discuss all this with Mariana Hagström, CEO and founder at Avokaado, a contract intelligence platform solving the trust problem for in-house legal teams across Europe. Based on what I’m seeing across the ITGC community and in-house legal market, here are five things you can’t afford to get wrong when it comes to legal AI agents in 2026.
1. Assuming “agentic” just means faster automation
One of the biggest myths is that legal AI agents are simply a faster or smarter version of traditional document automation platforms (think CLM tools circa 2021 that automated NDA creation, for instance).
But they’re not. They differ from traditional legal software because they can reason, make decisions and act autonomously within defined parameters. For example, a true legal AI agent can do all this without constant human intervention:
Analyse a contract
Identify risks and suggest amendments
Draft responses
In legal AI, an “agent” isn’t just faster automation or chat in disguise. The shift isn’t speed - it’s agency: the ability to reason, choose actions, and move a workflow forward independently.
“Agentic” doesn’t mean fully autonomous by default. In regulated environments, the value is bounded autonomy: agents that act within clear limits, escalate when needed, and can show what was done and why.
Agent = a system that can take actions.
Agentic = the ability to plan and act across steps, not just respond.
That changes the risk profile entirely, which brings me to point 2.
2. Implementing agents without proper governance frameworks
Regulation (particularly in Europe) still hasn’t fully landed, but the direction of travel is clear. Legal AI sits at the intersection of:
AI governance
Data protection
Professional and ethical responsibility
Organisational accountability
Without establishing proper boundaries, approval flows and oversight mechanisms, you get inconsistent outputs, compliance risks and erosion of stakeholder trust (when let’s face it, a huge benefit of leveraging legal AI agents is showing the business you’re running a future-fit function that operates like all other business functions).
Don’t wait for perfect clarity from regulators - reasonable, documented judgment is the best place to start, so start planning these three things asap:
How agent behaviour aligns with internal policies
How outputs and actions can be justified to boards, auditors and regulators
How human oversight is maintained in practice, not just on paper
3. Treating trust as a comms issue, not a design principle
What do I mean here? “Trust” in legal AI is often discussed as a change management problem, mostly about educating lawyers so they get comfortable with AI.
But that misses the point - in-house lawyers don’t distrust AI because they don’t understand it, they distrust it because the cost of being wrong is too high.
Trust in legal AI agents comes from:
Predictable behaviour
Explainable outcomes
Clear accountability lines
Systems that reflect legal reasoning, not just efficiency gains
“Lawyers need agency, not another chatbot. But agency without governance is just risk. The model doesn’t matter as much as the system around it: boundaries, escalation, and auditability. That’s what makes legal innovation trustworthy.”
- Mariana Hagström, CEO and founder at Avokaado
4. Overlooking data quality and integration requirements
AI agents are only as good as the data they can access and process.
A common mistake is assuming agents can work effectively with fragmented, poorly organised, or inaccessible legal data.
So before deployment, assess your data infrastructure, establish data quality standards, and ensure agents can seamlessly integrate with your existing legal systems and workflows.
If you are lucky enough to have a data analytics team, this is a perfect opportunity to collaborate with them cross-functionally (and a great project for a new or more junior member of the team to start working on and own!).
5. Forgetting that you’re in charge
This one is the biggest blocker to you trusting agentic AI.
Yes AI agents can act, but they can never own the consequences of their actions. No matter how advanced they become, never forget that:
Accountability sits with the organisation
Professional and ethical responsibility sits with lawyers
Strategic judgment is and will always remain human
The real goal is not to remove lawyers from the loop, but to remove them from repetitive work so they can spend more time where humans add the most value: strategy, risk trade-offs, and high-impact decisions. Here’s an example we put together of a common pain point (vendor onboarding and DPA compliance) to show you what we mean:
The challenge:
Onboarding a new SaaS vendor usually triggers a fragmented process. Procurement uploads the agreement. Legal reviews it. Security asks for a DPA. Jurisdiction is checked. Edits happen in Word. Approvals live in email. The final contract is stored somewhere, and renewals are missed.
The risk isn’t drafting the contract. It’s the manual process and missed compliance steps.
The solution: the agentic approach
With agentic automation, the workflow becomes structured:
Vendor contract is uploaded (or ingested from Drive/SharePoint/Box)
The agent identifies contract type and extracts key terms (parties, term, liability, data processing, jurisdiction)
If the contract includes personal data processing, the agent checks whether a DPA is present
If DPA is missing → the agent blocks signature and escalates to legal/compliance
If the vendor is in a high-risk jurisdiction → escalates for enhanced review
If all requirements match policy → the agent approves, routes for signature, stores to the correct folder, and schedules renewal monitoring
A Scorecard is generated showing what was checked, which rules applied, what changed, and why
The result: not just faster review, but compliance by default.
This is the problem Avokaado is built to solve.
Avokaado doesn’t treat legal AI like a black box. Agents work inside your rules and playbooks, with full visibility into what they did and why.
No blind trust, just confidence that the system followed your playbooks.
Join the Avokaado waitlist → avokaado.io
With thanks to our guest author and sponsor, Avokaado, for teaming up with us on this blog to help you overcome agentic AI trust issues!
