Stop Debating Agentic AI’s Definition. Start Demanding Results

Walk into any legal tech conference today, and you’ll hear the words “agentic AI” everywhere. Listen more closely, and you’ll hear legal tech leaders debate what counts as “real” agentic AI.

Some argue that true agentic AI must operate completely independently—say, a virtual assistant that can book your hotel, negotiate rates, and handle payments without any guidance. On the other side of the debate, you have sophisticated AI workflows designed to streamline complex workflows that follow predetermined steps and require human oversight—which some would deem real agents.

It’s a heated debate, and it completely misses the point.

Lawyers are asking simpler questions:

• Does this help me do my job better?
• Can I trust the results?
• Will it actually save time and reduce risk?

Here’s what we’ve learned from deploying AI across multiple live legal projects: Today’s barrier is trust, not capability. AI can already review contracts with remarkable accuracy, reason across documents, and compress months of work into hours.

But none of that matters if lawyers can’t verify the output quickly and confidently. In legal, adoption follows verifiability and reliability.

The current debate about what constitutes real agentic AI is the wrong one. Whether a system satisfies a purist’s definition is far less important than whether it consistently produces outcomes one can stand behind, including faster reviews, stronger compliance, and fewer risks slipping through the cracks.

Lawyers may live by definitions in their documents, but with technology, they care about outcomes they can trust. Gatekeeping the “agent” label, and framing the debate accordingly, risks alienating those who would benefit most from the capability.

Autonomy Versus Reliability

Full autonomy sounds impressive—everyone wants an agent that books travel or redlines a contract without oversight. But autonomy in practice remains a trade-off against reliability. In high-stakes legal work, a wrong call costs more than any convenience gained from total automation.

The smarter approach combines AI capability with flexible human oversight, layering in agentic design and autonomy where appropriate. These systems can plan, execute, and self-check, but they know when to hand off decisions to humans at the right moments. The key is calibrating that handoff correctly.

In legal work, reliability comes first, with autonomy dialed up only when you have solid evidence that it works.

Trust by Design

Trust comes with good design. The most reliable legal AI tools share specific features that let lawyers verify outputs efficiently—every conclusion links back to source documents with clickable citations, for example. Well-designed systems show reasoning, step-by-step, so you can follow the logic. And good AI models will admit if they’re uncertain instead of bluffing.

When lawyers can trace AI decisions back to sources and verify reasoning in minutes, trust compounds with every use. Success in legal work comes from consistent performance and reliability, not labels. The metrics that matter are simple: faster cycle times, higher first-pass accuracy, and quicker validation of outputs.

Tech, Services Converge

The convergence of legal tech and services is already underway. Rather than shipping tools that lawyers must configure and monitor, AI is embedded into delivery so business intent goes in and outcomes come out.

Well-designed systems handle heavy lifting: parsing documents, extracting key information, reasoning across contracts, redlining, summarizing, and flagging risks at scale. Human experts remain accountable, validating where context and judgment matter most. Autonomy is calibrated based on risk, with more granted for low-risk use cases and less for complex matters.

What Legal Needs

AI tools must be designed for how legal work gets done. They need to plan complex tasks, reason across multiple documents, and ground every assertion in evidence—all within the guardrails that high-stakes legal work demands.

The best AI systems adapt and improve based on real-world use and continuous feedback. The result is AI that acts responsibly and predictably.

The effects are tangible. High-volume tasks move end-to-end with minimal oversight. Complex agreements arrive with rationale-backed redlines and clear risk summaries that free up senior lawyers for higher-value work. Turnaround times drop, escalations decline, and savings show up on real matters—not just in pilots.

Let’s stop debating whether today’s systems are true agents. To paraphrase Shakespeare, an AI by any other name still must deliver results. For legal teams, the question isn’t what we call it; it’s whether it works. If not, the label won’t matter.

This article does not necessarily reflect the opinion of Bloomberg Industry Group, Inc., the publisher of Bloomberg Law, Bloomberg Tax, and Bloomberg Government, or its owners.

Author Information

Hitesh Talwar is head of research and development at Factor.

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