Agentic AI vs. generative AI: The core differences 

Core differences between GenAI and agentic AI 

Generative AI excels at producing specific content based on direct, specific, step-by-step prompts. Agentic AI, on the other hand, excels with an initial set of instructions to autonomously manage multistep processes to achieve a larger objective with less human intervention. Agentic AI makes decisions and takes action to keep a process going, while GenAI reacts to input and creates output.

Simply stated, agentic AI specializes in workflow automation and independent problem-solving, while GenAI’s sweet spot is content creation.

What is generative AI?

Generative AI (GenAI) is a type of artificial intelligence designed primarily to create, when prompted, new content based on patterns learned from training data. This type of AI excels at producing text, images, code, and other media in response to specific, step-by-step human input. GenAI works by recognizing patterns and making statistical predictions to generate human-like outputs, making it particularly valuable for content creation, summarization, translation, and information synthesis tasks. GenAI typically functions in a request-response model, such as a chatbot.

For professionals, GenAI is a powerful tool for creating initial drafts and summarizing large volumes of information in clear language.

What is agentic AI?

Agentic AI is an advanced form of artificial intelligence that can plan and execute complex tasks across multiple systems to achieve specific goals. Unlike traditional AI, agentic AI makes decisions, uses various tools and APIs, and performs sequences of actions without continuous human guidance. This new AI technology excels in workflow automation, proactive monitoring, and multistep processes. It is applicable in professional fields like legal, tax, and compliance, where it can maintain ongoing operations while adapting to changing conditions.

Like other forms of AI, the framework of agentic AI uses machine learning and large-language models (LLMs). But unlike traditional AI, agentic AI systems don’t need to constantly follow preset inputs, giving them a great deal of adaptability. Agentic AI can improve its output and adjust to changing conditions and situations through reinforcement learning (RL). This type of machine learning works through trial and error, where an AI agent learns by taking actions in an environment and receiving rewards or penalties for its choices. Over time, the agent discovers which strategies maximize rewards, similar to how someone learns to ride a bike by experiencing what keeps them balanced versus what makes them fall.

Agentic AI also stops when it runs into situations requiring professional expertise and decision-making, whether in legal, tax, audit, accounting, or risk and fraud.

See how agentic AI works in different professions:

• Agentic AI in legal: What it is and why it may appear soon
• Agentic AI in tax: How it's redefining professions
• Agentic AI for risk and compliance: What the rise means

Differences in workflow benefits

Both types of AI have numerous real-world benefits relating to streamlining tasks, improving productivity, and saving time. Each also has its particular strengths.

GenAI workflow benefits

The Thomson Reuters Institute 2025 Generative AI in Professional Services Report identifies saving time, increasing productivity, assisting with routine work, improving work quality, and reducing costs as GenAI’s most compelling capabilities. More specifically, GenAI can:

• Produce high-quality initial drafts of documents, reports, and communications as quickly as they’re needed for scalability.
• Synthesize and summarize large volumes of information into concise, actionable insights.
• Transform complex technical information into plain language.
• Present multiple approaches to a problem by creating alternative versions of content.

Agentic AI workflow benefits

An e-book published by Thomson Reuters, Agentic AI 101: What your business needs to know, provides a guide for introducing this newer form of AI into professionals’ workflows. It notes that agentic AI’s overarching benefits are improving efficiency, elevating work quality, and accommodating more business. It delivers these benefits by:

• Automating multistep processes across different digital platforms
• Maintaining consistent application of complex rule sets and compliance requirements
• Coordinating multiple tasks, tools, and data sources
• Reducing time-to-completion for complex and routine tasks through autonomous decision-making while deferring to human expertise when encountering exceptional situations

Differences in use cases

Agentic AI and generative AI tools are not designed to replace human insight, experience, and judgment. What they can do is allow professionals to save time and focus on what they do best — and what only they can do. It can amplify their expertise, boosting the services they provide and what they achieve.

Here are examples of core use cases for each type of AI.

GenAI use cases

• Drafting. Create initial versions of legal contracts, regulatory filings, corporate communications, and other documents following specific goals, templates, and other user inputs.
• Research. Automate and analyze massive amounts of documents and historical data faster and more accurately than human beings. These capabilities also power tasks such as predictive modeling.
• Interpretation. Translate technical language into a style that is accessible to all audiences.
• Documentation. Assemble comprehensive compliance documentation based on policy requirements, implementation evidence, control descriptions, and testing results.
• Communication. Develop customized communications for various stakeholders — like investors, regulators, clients, and employees — while maintaining consistent core messaging.

Agentic AI use cases

• Investigation. Conduct comprehensive due diligence on potential customers or purchases by searching across multiple datasets, identifying relevant documents, extracting key information, and compiling findings into structured reports.
• Monitoring. Track regulatory changes in real time across jurisdictions, identify affected client portfolios or business operations, and generate action plans to address compliance gaps.
• Processing. Manage accounts payable workflows by validating invoices against purchase orders, routing approvals, and scheduling payments.
• Onboarding. Evaluate applications for government programs, financial accounts, and customer or vendor relationships. Agentic AI gathers applicant information from multiple sources, verifies documentation, and accesses sanctions lists and adverse media.
• Project management. Submit a GenAI-generated proposal to the company’s customer relationship management platform, then schedule a follow-up meeting with that potential customer and send additional information.

Preparing for the future of AI

The differences between generative AI and agentic AI represent fundamentally different approaches to how AI can serve organizations, and the most powerful applications use them in conjunction with the other tools and initiatives. While many businesses recognize that AI will be central to their future operations, there remains a significant gap between acknowledging its importance and implementing effective AI strategies with clear training programs and return on investment metrics. This implementation gap creates substantial risk in today's rapidly evolving technological landscape, particularly for organizations that understand AI's potential but struggle to operationalize it.

Thomson Reuters addresses this challenge with our agentic AI platform and purpose-built systems like CoCounsel that don't merely respond to prompts but operate within established professional workflows. Unlike generic AI solutions requiring extensive customization, these domain-specific agents are refined by legal, tax, audit, and accounting experts to reason in alignment with professional standards while ensuring human expertise remains firmly in the loop. This approach creates a partnership model where AI enhances rather than replaces professional judgment, allowing organizations to gain confidence with AI while maintaining control over critical processes.

Adopt the future of work with Thomson Reuters

CoCounsel is the professional-grade AI solution for professionals, trained by industry experts, backed by authoritative content, equipped with best-in-class security, and enhanced by our revolutionary GenAI and agentic AI. The future of work isn't just about having AI capabilities — it's about having the right AI capabilities to enhance human expertise, and we provide this blueprint for organizations ready to prepare for tomorrow's innovations today.