If you feel like every legal tech company is using the term “agentic AI,” you’re not alone. The label has exploded across conference panels, product launches, and vendor demos—yet most still aren’t sure what agentic AI for lawyers means or how it affects the work they do every day.
For many attorneys, the experience of testing AI tools has been similar: the tool produces a confident answer, but you’re left wondering what steps it took, where the information came from, and whether you can trust it. You still find yourself validating every definition, clause and cross-reference manually. And more often than not, the output doesn’t fit neatly into the way you actually draft in Word, update redlines, or manage documents in your DMS.
The time spent validating the output can offset the time you thought you saved.
This is why the concept of agentic AI has captured so much attention, and why it’s so important. Agentic isn’t about creating a more sophisticated chatbot. It isn’t about replacing lawyers. Instead, agentic AI is about something far more practical: whether an AI system can actually participate in a legal workflow—not just generate text that the lawyer still has to fix, verify and contextualize.
It’s about reclaiming your time for work that requires judgment, creativity and strategic thinking.
What Makes It “Agentic”?
The distinction matters because it determines whether a tool actually reduces your workload or just shifts it around. A legitimate agentic system requires three elements working together:
1. Reasoning Capability
The system interprets your intent, evaluates its own actions and adjusts based on context. This goes beyond keyword matching. It requires understanding what you’re trying to accomplish and adapting when circumstances change.
2. Tool Access Across Your Workflow
The AI connects to your actual work environment: your document management system, Word, email and practice-specific databases. Single-integration products that only touch one platform can’t replicate how you actually work.
3. Workflow Ownership
The system completes entire tasks, identifying what’s missing, locating relevant materials, adapting them to your context, and documenting each step — without requiring constant supervision.
For an example of how this works, consider contract drafting. A true agent identifies missing provisions, searches your DMS for relevant precedents, tailors language to match your current agreement’s structure, defines newly introduced terms, and inserts everything back into the document. Then it shows you exactly what it changed and where it found each piece.
Compare that to a chatbot that suggests boilerplate text you still need to copy, paste and manually integrate. Both might use AI. Only one is genuinely agentic.
A Lawyer’s Workflow Requires More Than One Integration
Legal work rarely happens in a single application. Drafting a purchase agreement means simultaneously referencing your DMS, working in Word, checking emails with opposing counsel, and consulting internal playbooks.
Authentic agentic systems should mirror that complexity, because that’s how you work. A tool that only operates inside your DMS or only suggests text in Word can’t own the workflow—it just adds another step to your process.
This also explains why these systems cost more than simpler AI tools. Multiple models and genuine cross-platform integration require substantial resources. That expense only makes sense when the alternative is hours of manual work jumping between systems.
AI Agents for Lawyers Must Show Build Trust Through Transparency
Here’s the reality: you remain professionally responsible for every output an AI system produces, even when you didn’t write it yourself. That creates natural, appropriate hesitation.
This is why explainability matters more than speed. A well-designed agent doesn’t just return an answer— it shows its reasoning and sources at every step. When an agent displays which clauses it retrieved, what documents it referenced, and what specific edits it made, you can review and validate each decision. The AI transforms from a black box into a visible, auditable collaborator.
This transparency also prepares you for client questions. As AI becomes standard in client-facing work, detailed audit trails will become as essential as the work product itself.
Your Proprietary Data as Competitive Advantage
Even sophisticated AI is only as good as the information it draws from. If your DMS is disorganized or contaminated with deal-specific provisions that shouldn’t become standard language, the agent will produce equally flawed results.
This makes your firm’s proprietary knowledge the new competitive advantage. Every clause, contract and playbook captures institutional expertise that no external model can replicate. Firms that structure and maintain this data — through metadata, version control, and ongoing curation — ensure their agents operate on accurate, firm-specific intelligence.
Practically, this means thinking prospectively about how to tag and organize new work so it becomes useful precedent. It means distinguishing between your firm’s preferred positions and compromises accepted due to specific deal circumstances.
Cutting Through Marketing to Assess Agentic AI Systems
When assessing tools marketed as “agentic,” demand specifics:
Workflow depth. What complete legal tasks can the system handle independently? “Speeds up drafting” is vague. “Identifies missing force majeure provisions, locates firm-approved alternatives, adapts language to match agreement structure, and inserts clauses with tracked changes” is specific.
Integration breadth. Which systems does it connect to—your DMS, Word, Outlook? Can it actually complete tasks across platforms, or does it require you to manually move information between systems?
Data handling. How does the system track provenance? Can it distinguish between work product you want to replicate and one-off provisions from unusual deal circumstances?
Transparency mechanisms. Can it show its reasoning step-by-step? Does it create audit trails you can review and share with clients?
Cost justification. Why does this particular problem warrant an expensive agentic approach versus simpler automation? The right vendor can articulate this clearly.
Use these questions to filter genuine capability from clever rebranding. The right vendor welcomes scrutiny.
Transparency keeps you firmly in control—validating outputs, refining the system through feedback, and maintaining professional responsibility.
Too Good to Be True? A Practical Evaluation Framework
When AI agents automate routine retrievals, comparisons and insertions — the repeatable tasks that consume your day but don’t require your expertise — they free you to focus on the ambiguous, high-stakes decisions that actually need a lawyer’s judgment.
The promise of gaining back hours for analysis, negotiation, client strategy and client development is seductive. Before committing to any “agentic” tool, however, ask:
- Can it complete entire workflows across multiple systems I already use?
- Does it show me its reasoning and create verifiable audit trails?
- Will it work with my firm’s proprietary data and precedents?
- Is the cost justified by the complexity of the problem it solves?
- Can I easily validate and correct its outputs?
- Does the vendor welcome detailed questions about security and methodology?
If the answer to any of these is unclear or negative, keep looking. The right solution makes these answers obvious—and transforms how you spend your day without requiring you to trust blindly or validate endlessly. The goal isn’t to find AI that works like magic. It’s to find AI that works the way you do.
