AI Agents vs. Agentic AI: What Legal Teams Need to Know

By Lars Mahler, LegalSifter

“Agentic AI” is everywhere—analyst maps, vendor decks, buzzy demos. For many legal use cases, however, autonomous AI isn’t necessarily beneficial; it can create compliance gaps, liability, and missed opportunities. This article explains the difference between AI agents (user‑directed helpers) and agentic AI (autonomous agents that control their own work), and why LegalSifter recommends orchestrated, multi‑agent workflows—not agentic autonomy.

Key Takeaways

AI agents execute; agentic AI decides. AI agents follow your playbook; agentic AI sets or interprets goals and sequences steps on its own.
Legal work needs guardrails. More autonomy ⇒ more variability, audit burden, and explainability risk.
LegalSifter is AI agent‑powered, not agentic AI. ReviewPro orchestrates many narrow agents under human and playbook control.
Trust beats hype. Watch for “agent‑washing”: vendors relabeling automation as autonomy. Regardless of branding, look for AI that meets 5 quality attributes: steerable, thorough, accurate, explainable, and fluent.

What Is an AI Agent?

An AI agent performs a narrow, well‑scoped task under human or workflow instruction—e.g., detect a concept, extract a date, flag a risk, or propose a fallback tied to a rule. It does not set goals or decide when it’s “done”; the workflow and playbook do that.

In practice, AI agents:

Do not define goals on their own: they do what users tell them to do.
Do not decide what to do next: they are controlled by a human or a workflow.
If they use tools, tools are pre-approved and invoked by the workflow (not by the AI deciding on the fly)
Execute within predefined constraints: predefined workflow constraints tell them when they are done.

In short: they’re smart assistants, not decision‑makers.

What Is Agentic AI?

Agentic AI operates with goal‑directed autonomy. Unlike traditional agents that follow predefined tasks, agentic systems make decisions for themselves. They can:

Set objectives: they set their own goals, based on their interpretation of the user’s request.
Choose which steps to take: they determine the sequence of steps needed to meet those goals.
Dynamically choose when to use tools, such as email, API, web search, or other tools.
Determine when the task is complete: they determine when they think the goals or objectives are met.

This typically involves:

Multi‑step control loops (plan → act → evaluate → refine)
Less explainable results (which can be a challenge in regulated or risk-sensitive domains)

Agentic AI is powerful—but it shifts control away from users and hands autonomy to the system, which raises risks such as compliance gaps, weaker transparency, and slower root‑cause analysis.

A Real-World Example: Same Request, 2 Approaches

To help understand the difference between these 2 approaches, imagine that a user directs the AI: “Bring this NDA into compliance with our standards.” The table below shows how the 2 different AI architectures might respond to this directive.

Orchestrated Multi-Agent AI (ReviewPro-style)	Agentic AI
Reads pre-written playbook Runs sifters to detect presence / absence of provisions Applies playbook rules Proposes edits tied to rules Produces traceable results (rule → evidence → edit)	Interprets “compliance” goal Chooses to edit indemnity, limitation of liability, and definitions Maybe rewrites cross-references Possibly contacts counterparty for clarification Decides it’s done when its own success metric is met.
Benefits: predictable scope, audit trail, reproducibility.	Risks: shifting objectives, opaque rationales, edits that diverge from policy.

The Control ↔ Autonomy Spectrum

Use this spectrum to think about risk, auditability, and fit for purpose:

Level	Who sets the goals?	Who sequences the steps?	Who decides when to stop?	Example legal use case
Tool	Human	Human	Human	Search, templates
Agent AI (Sifters)	Human (via playbook)	System workflow (predefined)	System workflow (predefined)	Concept detection, data extraction
Orchestrated multi‑agent AI (ReviewPro)	Human (via playbook)	System workflow (predefined)	System workflow (predefined)	Multi‑step review & proposed redlines
Agentic AI	AI	AI	AI	Autonomous AI negotiator that sets a ‘reduce liability’ goal, rewrites provisions, and emails a counter-proposal without approval

Position: Contract review calls for orchestrated multi‑agent AI workflows under explicit, auditable rules—not free‑roaming autonomy.

Why Agentic AI Is Risky in Contracts

When AI systems act too autonomously:

Compliance becomes harder
Contract standards drift
Transparency across teams breaks down
Root‑causing a bad edit or recommendation takes longer

Giving AI full autonomy introduces unnecessary risk, complexity, and inconsistency—especially in contract review.

How ReviewPro Works (AI Agent‑Powered, Not Agentic AI)

From a user’s seat, ReviewPro feels effortless:

You give it: a contract + your playbook.
It then: analyzes according to your rules, flags risks and ambiguities, interprets meaning and intent, proposes redlines tied to your standards, and returns a redlined draft for your approval.

Behind the scenes, this is an orchestrated multi‑AI agent workflow—not agentic AI autonomy.

ReviewPro isn’t passive; it orchestrates many narrow agents to deliver nuanced, multi‑dimensional analysis. LLMs help interpret text, but you define the boundaries, and you stay in control.

At LegalSifter, we’ve deployed AI agents in contract review for over a decade—we’ve just never called it “agentic,” because it isn’t. Our design principle is human‑in‑the‑loop productivity, not unchecked autonomy.

Just as important, ReviewPro never:

Defines its own objectives
Decides which contract to work on
Invents new standards or positions outside of your playbook

In short: ReviewPro isn’t agentic AI—but it’s powerful enough that it can feel like it is.

Quick Comparison: AI Agents vs. Agentic AI

Feature	AI Agents (LegalSifter)	Agentic AI
Who sets objectives?	Human/playbook	AI system
Who sequences steps?	Workflow/orchestrator	AI control loop
Who decides when to stop?	Defined by workflow	Chosen by AI
Transparency	High (rule‑tied outputs)	Variable
Legal risk	Contained, auditable	Broader, harder to audit
Example	Sifters; ReviewPro workflow	Not recommended for contracts

What to Look For: A 5‑Point Buyer Checklist

Some vendors “agent‑wash”—rebranding ordinary automation as “agentic AI.” Labels aside, here’s what actually matters.

Regardless of whether a vendor’s AI architecture is agentic or agent-based, look for these 5 quality attributes—and ask for concrete proof.

Quality attribute	What good looks like	Features to look for
Steerability	You can direct and constrain the AI to your standards.	Structured playbooks that capture positions and risk posture. Ability for you to edit playbooks directly.
Thoroughness	Every position in your playbook is checked—nothing is skipped.	Programmatic guarantee that all playbook positions were checked. System checklist or log showing each position that was checked.
Accuracy	The system edits when it should (and refrains from editing when it shouldn’t).	Controlled or constrained reasoning to reduce false positives and false negatives. Ability to trial on your contracts.
Explainability	You can see why each edit was proposed.	A decision trail that allows you to trace each edit back to the playbook position that created it.
Fluency	Edits are clean, professional, and consistent with the contract.	Surgical edits, reuse of defined terms, style/format preservation Multilingual edits where relevant.

FAQ

Q: Is LegalSifter’s AI considered agentic?
A: No—and that’s by design. We use tightly scoped AI agents that work under human instruction, and are orchestrated by multi‑agent workflows. This ensures that you define the boundaries, and you stay in control.

Q: Why choose AI agents over agentic AI for contracts?
A: AI agents provide speed and consistency with oversight—ideal for teams that require control, auditability, and transparency.

Wrap‑Up

At LegalSifter, we use AI to empower professionals, not replace them. Agentic AI is exciting, but we choose control, accountability, and real‑world productivity over autonomy for its own sake. That’s what separates meaningful contract AI from marketing buzz—and why our clients choose precision over hype.

Curious how orchestrated agents return auditable redlines—without agentic risk?
Request a ReviewPro demo.

About Lars Mahler

As Chief Science Officer and Co-Founder of LegalSifter, Lars has shaped the evolution of AI-powered contract review through his visionary leadership and deep expertise in natural language processing. The architect behind ReviewPro, LegalSifter’s proprietary AI redlining tool, Lars combines over a decade of contract-specific AI research with rigorous enterprise-grade engineering. His work introduced a new model for first-pass contract automation, anchored in structured playbooks, controlled LLM behavior, and auditable, automatic edits, that puts legal professionals in control without sacrificing speed or precision. Under his direction, ReviewPro has emerged as a market-leading solution, capable of delivering consistent, explainable, and negotiation-ready edits across a wide range of contract types. Lars’ contributions have not only advanced LegalSifter’s technology, but have also helped define the quality benchmarks and control frameworks now shaping the future of AI redlining.