In our last webinar, we discussed tailoring AI to your legal workflows with a focus on agentic AI. You can watch the full discussion here. 

Legal teams are under pressure to work at speed to draft airtight contracts, stay ahead of regulatory changes, and mitigate legal risks. Manual processes slow them down. The AI tools you know might help, but they often require significant human oversight. They suggest text, provide research, and assist in drafting, but they don’t execute legal work.

Agentic AI changes that. Instead of offering recommendations, it follows structured workflows, retrieves relevant legal data, applies compliance rules, and produces legally sound outputs with minimal human input. This is the next step in AI for legal professionals: AI that doesn’t just assist but acts.

AI that acts

For many, AI still conjures the image of a chatbot. It’s an interface that responds to prompts with pre-generated text. This is no longer the case. The latest evolution of AI is agents that don’t just generate words but understand context, retrieve information, apply reasoning, and take action.

This shift from assistive AI to agentic AI is a turning point. IInstead of simply answering a single question, agentic AI follows structured workflows, applying logic and reasoning to execute complex, multi-step processes with minimal human input. Previous AI tools needed extensive verification and revision, but agentic AI delivers results that are ready for use.

What sets agentic AI apart is that each agent is optimized for a very specific task.The agent is already equipped with useful context and specialist instructions for that task, resulting in a much higher quality output. In legal work, this means automating workflows without sacrificing accuracy. AI is becoming an independent executor that streamlines legal operations while maintaining legal integrity.

Agentic AI’s applications extend far beyond law. Across industries, businesses are deploying autonomous AI agents to streamline operations, reduce errors, and improve decision-making. In finance, AI agents manage portfolio rebalancing, detect fraud, and execute trades in real time. In healthcare, they assist in clinical decision-making, drug discovery, and personalised treatment recommendations. Manufacturing firms use agentic AI for predictive maintenance, supply chain optimisation, and quality control.

How agentic AI works in legal practice

Consider drafting a force majeure clause for a financial lease. A traditional AI model might generate a generic clause based on publicly available templates, leaving the lawyer to adapt it to their needs. Agentic AI, by contrast, follows a multi-step process:

Understanding intent – Does the user need a summary, legal precedent, or a newly drafted clause? The AI determines this before proceeding.

Identifying the legal domain – Recognising that the request relates to financial contracts, it pulls from relevant banking and financial regulations.

Retrieving authoritative sources – The AI searches firm-approved databases, case law, statutory codes, and internal policies.

Applying legal reasoning – Using structured decision-making, the AI selects the most relevant legal frameworks to guide the drafting process.

Generating a precise output – The AI delivers a force majeure clause aligned with the relevant legal jurisdiction and firm-specific guidelines.

The limitations of generic AI in legal work

Agentic AI is a necessary evolution because traditional AI tools are fundamentally unsuited to legal work. General-purpose models like ChatGPT and Microsoft Copilot generate text based on linguistic patterns rather than legal logic, often producing results that lack compliance safeguards. They also fail to adapt to firm-specific policies, jurisdictional differences, and industry nuances, making them unsuitable for the complexities of legal practice. Additionally, many AI tools process data externally, posing security and compliance risks for sensitive legal and client information. 

Agentic AI addresses these shortcomings by operating within secure environments, following structured workflows, and ensuring that all outputs are legally sound.

Agentic AI in action 

The most transformative aspect of agentic AI is its ability to execute legal tasks with precision and consistency. Unlike traditional AI, which passively generates responses, agentic AI actively engages with legal processes, ensuring compliance, adaptability, and security.

One useful application is contract drafting and review. AI agents generate contract clauses, NDAs, and agreements that align with firm standards and regulations. A team handling cross-border mergers can use agentic AI to ensure contracts reflect jurisdiction-specific requirements, reducing manual cross-referencing and inconsistencies.

Regulatory monitoring is another way it comes in handy. Legal teams need to track evolving laws and policies, a time-consuming task. Agentic AI automates this by scanning regulatory databases, extracting relevant updates, and flagging necessary changes. A financial services firm navigating new AML rules can use AI to detect compliance shifts and update internal policies accordingly.

Contract risk assessment benefits from AI’s ability to identify missing terms, ambiguous language, and compliance gaps. A technology company negotiating a licensing agreement can use AI to flag vague indemnification clauses or incomplete dispute resolution mechanisms, enabling stronger negotiations.

Comparison tasks are another area where agentic AI excels. Insurance companies, for example, can use it to compare policies across providers, identifying missing coverage or inconsistencies. A business reviewing liability insurance can instantly see where exclusions differ, avoiding gaps in coverage.

Litigation and compliance support also improve with AI retrieving and summarising case law, statutes, and industry regulations. A corporate legal team preparing for a data privacy dispute can use AI to surface key rulings, reducing research time and strengthening legal arguments.

The future of agentic AI: beyond single-task automation

Agentic AI is still evolving, with its capabilities expanding beyond isolated tasks to more complex legal workflows. With end-to-end workflow automation, AI agents will not only draft contracts but also review, approve, and flag inconsistencies before execution. Instead of lawyers manually cross-checking terms and clauses, AI will ensure agreements meet firm standards, reducing the risk of errors. A corporate legal team finalising a supplier agreement, for example, could use it to detect and update contradictory clauses, missing liability protections, or outdated regulatory references before signing.

Integration with enterprise systems will also improve. Agents will connect directly with document management platforms, legal databases, and compliance tools, reducing the need for manual uploads, searches, and approvals. A team managing thousands of client contracts could have AI automatically extract key terms, compare them against updated policies, and flag any deviations for review, all within existing systems.

Proactive legal risk mitigation is also getting better. Instead of reacting to regulatory changes after they take effect, AI will monitor legislative updates in real time, providing alerts and recommended actions. A financial institution dealing with evolving AML laws could use AI to highlight and amend policy gaps, ensuring compliance before enforcement actions occur.

A new era for legal work

Agentic AI is a massive shift in AI’s usefulness for legal works. Legal teams can use it to streamline compliance, accelerate decision-making, and reduce manual effort. LEGALFLY offers all of this and is equipping teams with AI that executes legal tasks with precision. As the industry moves towards greater automation, the teams using agentic AI will have an operational and competitive advantage.

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