The conversation about AI in law firms is evolving rapidly. Twelve months ago, most discussions centred on chatbots and document review tools. Today, the leading edge has moved to something more ambitious: agentic AI systems that can reason, plan, and execute multi-step workflows with minimal human intervention.
For firms evaluating their technology strategy, the distinction between traditional automation and agentic AI is not academic. It has direct implications for where to invest, how to sequence adoption, and what risks to manage.
Traditional Automation: The Foundation
Traditional automation in law firms follows simple logic: if X happens, then do Y. It is rule-based, predictable, and well-understood. Most firms already use it to some degree, even if they do not call it "automation."
Common examples
Document assembly: Select a template, answer a series of questions, and the system produces a draft document. The logic is predetermined. Every input maps to a specific output.
Workflow triggers: When a matter reaches a certain stage, the system automatically generates a task list, sends a notification, or updates a status field.
Email rules and filters: Incoming enquiries are routed to the right practice area based on keywords or form selections.
Billing reminders: The system automatically generates and sends reminders when invoices reach 30, 60, or 90 days outstanding.
Template-based reporting: Data is pulled from the practice management system and populated into a pre-designed report template.
Strengths
Predictability: Rule-based systems do exactly what they are told. The output is deterministic. The same input always produces the same result.
Auditability: Every action traces back to a specific rule. This matters enormously in a regulated profession.
Low risk: Because behaviour is fully defined in advance, the risk of unexpected outputs is minimal.
Established tooling: Most practice management systems already support basic automation. New technology purchases may not be required.
Limitations
Rigidity: Rule-based systems cannot adapt to scenarios they were not programmed for. A document assembly system configured for standard commercial leases will not handle a lease with unusual provisions unless someone builds new rules.
Maintenance burden: As rules accumulate, the system becomes complex and fragile. Changing one rule can break others. Someone needs to maintain the logic.
Narrow scope: Each automation handles a single task or a simple sequence. Complex, multi-step workflows requiring judgment at each step are beyond reach.
Agentic AI: The Next Layer
Agentic AI represents a fundamentally different approach. Instead of following predetermined rules, an agentic system receives a goal, plans a series of steps to achieve it, executes those steps, evaluates the results, and adjusts its approach based on what it finds.
What "agentic" means in practice
Consider a contract review workflow. In traditional automation, a firm might set up rules to flag specific clause types or check for the presence of required provisions. The system checks each rule against the document and reports results.
An agentic approach is different. The system receives the goal ("review this contract against our standard terms and identify all material deviations"), reads the contract, identifies relevant provisions, compares them against the firm's standards, assesses the significance of each deviation, drafts a summary of findings, and produces a recommended action for each issue. It does this without being told the specific steps to follow.
The key distinction
Traditional automation follows instructions. Agentic AI pursues objectives. The automation knows what to do. The agent figures out what to do.
Traditional automation asks "what are the rules?" Agentic AI asks "what is the goal?"
Real-world examples emerging in legal
End-to-end matter intake: An agent receives a client enquiry, classifies it by practice area, runs conflict checks, drafts an initial engagement letter based on matter parameters, routes it for partner approval, and follows up with the client. The agent orchestrates this rather than relying on separate, disconnected automations.
Research and memo drafting: An agent receives a legal question, plans a research strategy, searches relevant databases, evaluates the authorities it finds, drafts a research memo, and cites its sources. It adapts its approach based on what it discovers during the research.
Regulatory monitoring: An agent monitors regulatory updates, assesses their relevance to the firm's client base, identifies affected clients and matters, drafts alerts, and routes them for review. It does this continuously, without being prompted for each update.
Why the Distinction Matters
For law firms, the practical difference between traditional automation and agentic AI comes down to three dimensions: scope, judgment, and adaptability.
Scope
Traditional automation handles individual tasks. Agentic AI can manage entire workflows spanning multiple systems, multiple steps, and multiple decision points. The economic implications are significant. Instead of automating the 15-minute task within a 4-hour workflow, a firm can potentially automate, or at least orchestrate, the 4-hour workflow itself.
Judgment
This is where both the promise and the risk concentrate. Agentic AI systems make judgment calls: which research path to pursue, which contract deviations are material, which client enquiries are urgent. When those judgments are sound, the efficiency gains are transformative. When they are wrong, the consequences in a legal context can be serious.
Adaptability
Traditional automations break when they encounter scenarios outside their rules. Agentic AI systems can adapt, encountering an unusual contract structure and adjusting their review approach accordingly. This adaptability is powerful. It also means behaviour is less predictable, which creates new oversight challenges.
The Risk Dimension
Agentic AI introduces risk categories that traditional automation does not:
Unpredictable behaviour: Because agents determine their own approach, they may take actions that were not anticipated. In a legal context, an unexpected action, such as including confidential information in a research query sent to an external API, could constitute a serious breach.
Error propagation: In a multi-step workflow, an error in step two compounds through steps three, four, and five. A rule-based system makes isolated errors. An agent can make cascading ones.
Accountability gaps: When an agent makes a decision, who is responsible? The lawyer who deployed it? The firm? The vendor? Professional liability frameworks have not fully caught up with agentic AI.
Oversight complexity: Reviewing the output of a rule-based system is straightforward: check each rule's result. Reviewing the output of an agentic system requires understanding not just what it did, but why it chose to do it that way.
Managing the risk
The answer is not to avoid agentic AI. It is to implement it with appropriate controls:
Human-in-the-loop: For high-stakes decisions, require human approval before the agent proceeds. The agent handles preparation and analysis. The human makes the call.
Defined boundaries: Constrain what actions the agent is permitted to take. An agent that can draft and suggest should not be able to send or publish without approval.
Audit trails: Ensure the system logs every action, every decision point, and every data source consulted. If something goes wrong, the firm needs to trace what happened.
Graduated deployment: Start with low-risk, high-volume workflows where errors are easily caught and consequences are limited. Build confidence and control capability before moving to higher-stakes applications.
What This Means for Strategy
The practical recommendation for most mid-market law firms is to sequence their approach:
Phase 1: Maximise traditional automation
Before investing in agentic AI, ensure full value from rule-based automation. Most firms use less than 30% of the automation capability in their existing practice management systems. Document assembly, workflow triggers, billing automation: these are proven, low-risk, and often require no new technology purchases.
Phase 2: Introduce AI-assisted tools
Add AI capabilities that enhance individual tasks: contract review, research assistance, time capture. These tools use AI for analysis and suggestion but keep humans firmly in the decision-making loop.
Phase 3: Explore agentic capabilities
Once the firm has built AI literacy, established oversight protocols, and demonstrated comfort with AI-assisted workflows, begin exploring agentic capabilities for well-defined, bounded workflows. Client intake orchestration and regulatory monitoring are common early candidates.
Phase 4: Expand agentic scope
As confidence and control maturity grow, extend agentic AI to more complex workflows. This phase requires strong governance, clear accountability frameworks, and robust audit capabilities.
The Strategic Imperative
The distinction between traditional automation and agentic AI is not just technical. It is strategic. Firms that understand the distinction can sequence investments intelligently, manage risk appropriately, and build toward genuine competitive advantage.
Firms that treat "AI" as a monolithic category risk either over-investing in capability they are not ready for, or under-investing in capability that competitors are already deploying.
The conversation should be strategic, not vendor-driven. Understand what each layer of technology offers, what it requires, and what it risks. Then build a plan that matches the firm's ambition to its actual readiness.