Since the rise of ChatGPT, generative AI (GenAI) has redefined the potential of artificial intelligence (AI), transforming how businesses synthesize information, generate content, and interact with AI systems through natural language. According to McKinsey’s estimations, GenAI has the potential to add $2.6 to $4.4 trillion in additional value on top of traditional AI technologies.
The next wave of innovation in generative AI extends beyond basic question-answering and content generation to autonomous GenAI agents. These sophisticated systems can manage and execute complex, multistep workflows, all amplified by generative AI capabilities.
In this article, we will discuss what generative AI agents are, how they work, and how they can transform the way your business operates.
Key Takeaways:
- Generative AI agents differ from other AI agents by combining autonomous, action-oriented capabilities with advanced content generation.
- GenAI agents complete tasks by planning, using short- and long-term memory, and interacting with other tools.
- Key benefits of generative AI agents include enhanced creativity, smarter decision-making, and increased productivity.
- To fully leverage the capabilities of GenAI agents, businesses should document key processes as structured workflows, ensure seamless integration with data and existing infrastructure, and implement human-in-the-loop oversight.
- Creatio empowers businesses with role-based AI agents tailored to specific job functions and industries, as well as the tools to build custom agents designed for unique workflows and requirements.
What are Generative AI Agents?
A generative AI agent is an advanced AI system that combines the capabilities of generative AI—such as text, image, or code generation—with autonomous, goal-oriented behavior known as agentic functionality. These agents go beyond simply generating content; they analyze inputs, reason through objectives, and independently execute actions across systems without requiring constant human intervention.
These agents are ideal for automating complex workflows and accelerating operational efficiency. By chaining decisions, adapting to real-time context, and learning continuously from data, GenAI agents can perform multi-step tasks across various industries, including finance, tech, manufacturing, public sector, and more.
For example, a generative AI agent can automatically create and send personalized marketing emails, generate sales quotes, design branded content, or prepare your team for an upcoming customer meeting, all without human supervision.
How Do Generative AI Agents Work?
At their core, generative AI agents are powered by advanced machine learning (ML) models trained on massive datasets to understand context, generate tailored content, and take action. These models are enhanced by other technologies, such as natural language processing (NLP) for understanding text, computer vision for interpreting visuals, and decision-making algorithms for planning and executing tasks.
This combination allows generative AI agents to operate independently, managing multi-step workflows and adapting to changes in real-time. To function autonomously, these agents rely on four key characteristics:
- Planning: GenAI agents can take a high-level goal—such as "generate a monthly sales report"—and break it down into actionable steps. They create a structured plan, decide how to approach the task, and execute it step by step, rather than following static rules.
- Memory: The “agentic” capabilities allow these digital assistants to remember past interactions, decisions, and outcomes. By using short-term and long-term memory, they stay consistent, avoid repeating steps, and improve over time based on feedback and context.
- Tool Use: GenAI agents interact directly with business tools like CRMs, industry-specific tools, or APIs. They retrieve data, update records, trigger actions, and integrate seamlessly with enterprise systems to complete tasks automatically.
- Generative AI Capabilities: What truly sets these agents apart is their ability to create emails, reports, summaries, presentations, and even code. With built-in NLP and computer vision, they can understand instructions in natural language, analyze different types of inputs, and generate tailored outputs on demand.
Together, these capabilities enable GenAI agents to think, create and act—just like a digital coworker.
A Real Example of GenAI Agent in Action
Imagine a Chief Sales Officer asks a GenAI agent to report on the top-performing sales reps from last month. On the surface, the interaction between the agent and the human will look as follows:
Example of a user interaction with a Generative AI agent
But behind the scenes, the agent is executing a multi-step reasoning and action loop:
- Understands the question using NLP.
- Plans the steps needed to get the data (e.g., access CRM, check sales by rep).
- Connects to the CRM to retrieve relevant sales data.
- Stores context (e.g., time period) in memory.
- Generates a clear summary with the top 3 performers based on revenue.
By chaining reasoning with actions and dynamically responding to insights, generative AI agents execute tasks at scale—keeping outputs accurate, relevant, and fully aligned with business goals.
Types of Generative AI Agents
Generative AI agents can be categorized in several ways. For instance, GenAI agents are often described by modality (text-only, visual, or multimodal) or by use case, which spans thousands of possible business applications. However, a more practical and intuitive framework comes from Patrick Marlow, Google’s GenAI Agents evangelist, who divides them into two core types:
- Conversational Agents – designed for human interaction, these generative AI agents interact with user queries in natural language, retrieve information, and assist with tasks or transactions. The common examples include AI-powered chatbots, virtual assistants, or helpdesk agents that handle Q&A, guide users, or automate front-end requests.
- Workflow Agents – operate independently in the system’s background, with little to no human involvement. They’re triggered by system events, like new data entries, time-based conditions, or status changes, and used to handle complex tasks automatically. Examples include AI agents that route leads, prepare reports, or initiate approvals across business systems.
What Makes GenAI Agents Different from Other AI Agents?
The core distinction of generative AI agents lies in their unique ability to both autonomously reason and generate tailored content based on each specific context. By creating, adapting, and executing multi-step workflows, GenAI agents are ideal for complex, dynamic, and open-ended use cases.
Key differentiators that set generative AI agents apart include:
- Content generation, which makes them creative problem-solvers.
- Enhanced autonomy, which enables GenAI agents to operate independently in real-time with far less human oversight than other AI models.
- Superior understanding, leading to more human-like and context-aware interactions.
- Adaptability, which allows agents to continuously learn and refine their performance from new data and interactions, and align with changing conditions.
Key Features of Generative AI Agents
According to Deloitte’s State of Generative AI in the Enterprise survey, 25% of companies using generative AI plan to launch agentic AI pilots or proofs of concept in 2025. That figure is expected to double by 2027, indicating rapidly accelerating enterprise interest in agentic AI applications.
The surge in AI agent adoption is rooted in a powerful set of core capabilities, which are covered below.
1. Generative Capabilities & Creativity
One of the key characteristics of generative AI agents is the ability to create unique content, such as text, images, summaries, emails, code, or reports, which is fully tailored to specific tasks and business goals. These agents can adapt content to changing context in real-time and learn from vast sets of structured and unstructured data, which makes them versatile for various industries and use cases.
2. Autonomy and Context Awareness
GenAI agents operate independently, breaking down goals, making decisions, and continuously adjusting actions based on observed results. Like a skilled human assistant, they can troubleshoot issues, analyze outcomes, and adapt the next steps on the fly. Moreover, these digital assistants always remain context-aware, by taking into account factors like business objectives, prior steps, and live data to ensure accurate, goal-oriented responses.
3. Natural Language Understanding & Instruction
Generative AI agents are typically guided through simple, natural language prompts, which eliminates the need for complex coding and makes them accessible for non-technical teams. Their ability to support multi-turn, human-like conversations allows smooth integration into everyday workflows across departments.
4. Multi-Step Task Execution
Thanks to their agentic architecture, generative AI agents can complete complex processes end-to-end. They chain actions like retrieving data, making decisions, generating content, and executing next steps, creating a seamless reasoning-and-action loop that mirrors real human workflows.
5. Tool and System Integration
Generative AI agents can interact directly with CRMs, APIs, spreadsheets, databases, and other apps, enabling them to operate effectively across the business tech stack. This integration makes GenAI agents essential for executing complex workflows and delivering measurable business outcomes at scale.
Most Popular Use Cases of Generative AI Agents
The possibilities for generative AI agents are growing rapidly, and so is their impact across industries. As technology advances, businesses explore new ways to apply GenAI agents in sales, marketing, finance, customer service, and other high-impact areas.
Sales
Generative AI agents enhance sales productivity by automating a range of routine processes like creating personalized follow-up emails and meeting recaps, updating customer records, and even recommending next-best actions or upsell/cross-sell offers. Based on the deep analysis of the collected user data and interactions, a GenAI agent can also qualify and route leads in real-time. This not only improves the performance of reps but also significantly increases the sales revenue.
Examples of GenAI agents for sales are:
- Account Research Agent – Gathers key data on prospects from internal systems and public sources
- Quote Agent – Drafts personalized proposals and pricing documents based on inputs
- Meeting Prep Agent – Summarizes account history, goals, and next steps for upcoming calls
- Forecast Agent – Reviews pipeline trends and generates forecast summaries for managers
Example of Creatio’s Sales AI Agent
Marketing
In marketing, generative AI agents enhance campaign performance by streamlining execution and improving personalization at scale. These autonomous systems can create and localize content assets (i.e. emails, social posts, and landing pages), which are tailored to specific audiences and campaign goals. By analyzing real-time engagement signals and past performance, they enable marketers to refine targeting, accelerate content generation and get real-time insights to maximize marketing ROI.
Examples of generative AI agents for marketing include:
- Marketing Content Agent – Creates email copy, landing pages, and ad creatives personalized by segment or buyer intent
- Email Generation Agent – Produces hyper-personalized email content based on lead behavior and lifecycle stage
- Lead Scoring Agent – Evaluates the leads based on their responses and behavior patterns to define their readiness to convert
Example of Creatio’s Marketing AI Agent
Customer Service
Generative AI agents are transforming customer service by automating high-volume interactions, providing contextual responses, and accelerating resolution times. Particularly, agents can generate human-like responses for support tickets and inquiries, and summarize long threads and surface key issues for escalation. They can also analyze tickets based on intent and priority, and suggest internal knowledge base updates for the common issues, helping teams solve problems faster and increase customer satisfaction.
After integrating GenAI in customer service, Lenovo has achieved a 17% boost in customer satisfaction within a year.
Examples of GenAI agents for service are:
- Customer Support Agent – Automates handling the tier-1 and tier-2 cases across different communication channels
- Knowledge Base Agent – Retrieves, summarizes, and presents the most relevant articles or documentation from the internal bases
Example of Creatio’s AI Agent for Service
Document & Knowledge Work
Generative AI agents empower teams to streamline the creation, organization, and retrieval of internal and external documentation. This allows for reducing manual effort and improving accessibility of core knowledge assets across the organization.
Examples of Generative AI Agents may include:
- Document Drafting Agent – Generates proposals, contracts, and reports based on user inputs and templates
- Knowledge Article Generation Agent – Creates and maintains FAQs, SOPs, and internal knowledge base content
Specific Ops & Workflow Automation
GenAI agents can be configured to support the unique demands of industry-specific operations. For instance, they can perform specific tasks like claims processing, real-time calculations and data summarizations, and automated lead data capture. By connecting tools, teams, and data, these customizable AI-powered agents enable businesses to accelerate execution, reduce manual effort, and drive consistent outcomes.
According to McKinsey’s future predictions on generative AI, agents could cut time spent on creating credit-risk memos by 20% to 60%.
Some of the common operations where GenAI agents are applied include:
- Customer quote generation
- Campaign flow design
- Communication templates generation
- Lead funnel overview
- Case sentiment analysis and response generation
- Activity summaries
- … and many more
Key Benefits of Generative AI Agents for Business
Once deployed, generative AI agents can significantly elevate how businesses operate—by automating, optimizing, and scaling key processes. The core benefits include:
- Enhanced creativity. GenAI agents generate content, ideas, and solutions using advanced machine learning models—supporting teams in marketing, design, product, and other creative tasks.
- Smarter decision-making. By analyzing vast volumes of structured and unstructured data, generative AI agents surface insights, assess risks, and support faster, more informed decisions.
- Real-time adaptability. GenAI agents respond dynamically to changing inputs, allowing businesses to stay agile and adjust to customer behavior, market shifts, or system changes in the moment.
- Boosted efficiency and productivity. By offloading repetitive tasks and automating workflows, agents free up human teams to focus on high-value work.
- Faster process scaling and innovation. With AI agents, businesses can replicate and adapt successful workflows across teams and regions quickly, accelerating time-to-market and experimentation.
- Lower operational costs. Automation through AI agents allows businesses to cut down manual tasks, minimize costly errors, and significantly reduce expenses across departments.
Preparing for the “Agentic Future”: How Business Can Embrace GenAI Agents
As organizations move from experimentation to scaled deployment of generative AI agents, those unprepared risk falling behind. To help businesses fully capitalize on the potential of agentic AI, McKinsey outlines three readiness factors to focus on:
1. Codify Core Knowledge
To enable agents to handle complex tasks efficiently and at scale, organizations must define and document key processes as structured workflows. Capturing subject matter expertise in natural language also helps train agents more effectively, streamlining operations.
2. Modernize Data and IT Systems
To operate efficiently across workflows, AI agents need seamless access to data and infrastructure. Companies should ensure their IT systems support agent interactions, enable continuous feedback, and accommodate new technologies without disrupting existing systems.
3. Implement Human-in-the-Loop Oversight
As genAI agents gain more autonomy, human validation remains essential. Businesses must define when and how employees should review outputs for accuracy to ensure that AI agents act ethically, responsibly, and in alignment with business goals.
Enhance Your Business Performance with Creatio AI Agents
Generative AI agents are already transforming how businesses operate across industries. They automate routine workflows, boost creativity, surface actionable insights, and drive intelligent execution—at scale. Whether you're deploying ready-to-go agents or building custom solutions tailored to your unique operations, Creatio empowers your business to thrive with AI.
Creatio offers both out-of-the-box AI agents and flexibility to create custom AI agents on top of its AI-native platform —trusted by global enterprises to power intelligent automation at scale. Its no-code tools and composable architecture allow domain experts to automate industry workflows and CRM without writing a line of code. With generative, predictive, and agentic AI embedded at its core, Creatio enables organizations to design fully autonomous agentic workflows or support human teams through intelligent collaboration between AI agents and human. The combination of hybrid human-digital talent properly applied in your business allows you to expand and augment your business outcomes.
Built with enterprise-grade security and compliance in mind, Creatio ensures data privacy and governance at scale—supporting organizations operating in regulated industries.
Pre-built AI agents for sales, marketing, and service are available across Creatio’s CRM solutions. These agents can research account data, generate quotes, prepare meeting briefs, draft personalized emails, create campaign content, produce knowledge base articles, and assist with other specific processes and workflows. With the No-Code Agent Builder, business users and domain experts can create their own AI agents by visually composing agent skills, workflows, and knowledge—no coding required.
Creatio AI Agents
Enhance productivity and business outcomes with Creatio’s AI agents
