From Generation to Agency: Analyzing the Next Wave of AI and its Manifestation in Enterprise Workflow Automation

1.0 Executive Summary

The field of artificial intelligence is undergoing a significant paradigm shift, evolving from systems that primarily generate content to those that can execute autonomous actions. This report provides a comprehensive analysis of this transition, examining the established capabilities of Generative AI, the emerging power of Agentic AI, and the strategic implications for enterprise operations. The core finding is that while Generative AI has revolutionized content creation and information synthesis, Agentic AI represents the next logical step, transforming the potential of AI into tangible, goal-oriented action and redefining the landscape of business process automation.

Generative AI, powered by foundation models and large language models (LLMs), excels at creating novel text, images, code, and other media in response to specific user prompts. Its value lies in augmenting human creativity and productivity by automating discrete, content-centric tasks. The widespread adoption of these tools is commoditizing baseline content creation, elevating the strategic importance of prompt engineering and human oversight.

In contrast, Agentic AI introduces a new level of autonomy. These systems are defined by their proactivity, adaptability, and goal-orientation. Instead of reacting to prompts, AI agents are designed to pursue high-level objectives with minimal human intervention. They operate on a continuous loop of perception, reasoning, planning, execution, and learning, enabling them to manage complex, multi-step workflows across various digital platforms. The architecture of these systems often involves the orchestration of multiple specialized agents, which collaborate to achieve overarching business goals. This marks a move from AI as a tool to AI as a digital workforce.

A critical analysis reveals that Agentic AI is not a replacement for Generative AI but an evolution that builds upon it, using LLMs as a core reasoning engine. The distinction, however, is blurring as commercial products increasingly blend capabilities from both paradigms. A detailed case study of SeaMeet by Seasalt.ai, marketed as an “Agentic Meeting Copilot,” illustrates this trend. While its core functions are generative (transcription, summarization), its email-based workflow for creating follow-up documents represents a “proto-agentic” capability—a sophisticated, human-triggered automation that signals a broader market movement toward infusing products with agent-like features.

For business leaders, the strategic imperative is twofold: leverage Generative AI for immediate productivity gains while simultaneously initiating strategic experiments with Agentic AI to re-engineer core business processes for a new era of automation. Successful adoption hinges on establishing data readiness, robust security and governance frameworks, and a keen awareness of the ethical considerations inherent in deploying autonomous systems. The trajectory of this technology points toward a future where a collaborative ecosystem of human and AI agents drives enterprise efficiency, innovation, and value creation.

2.0 The Generative Revolution: AI as a Content Creator

The recent and rapid proliferation of artificial intelligence into mainstream business and consumer applications is overwhelmingly attributable to the rise of Generative AI. This subfield of AI has captured the public imagination and unlocked significant productivity gains by equipping machines with the ability to create original content. Understanding the mechanics, capabilities, and limitations of this paradigm is essential for contextualizing the subsequent leap toward autonomous agentic systems.

2.1 Defining the Paradigm: The Mechanics of Creation

Generative Artificial Intelligence is a class of AI that utilizes machine learning models to produce novel content, such as text, images, audio, video, or other forms of data, in response to user input.1 Unlike traditional AI systems designed for prediction or classification, or search engines that locate and curate existing information, generative models create entirely new outputs.2 They achieve this by being trained on massive datasets of human-created content. Through this training process, the models learn the underlying patterns, structures, and relationships within the data. Their core function is probabilistic; they predict the next most likely element in a sequence—be it a word in a sentence, a pixel in an image, or a note in a musical composition—to construct a coherent and contextually relevant result.2

The technological foundation for modern Generative AI rests on architectures known as foundation models (FMs) and, more specifically, large language models (LLMs).4 FMs are vast machine learning models pre-trained on a broad spectrum of generalized and unlabeled data, making them capable of performing a wide variety of tasks out-of-the-box.4 LLMs, such as OpenAI’s Generative Pre-trained Transformer (GPT) series, are a class of FMs specifically focused on language-based tasks like summarization, text generation, classification, and open-ended conversation.4

The scale and complexity of these models represent a monumental leap from their predecessors. Early forms of generative AI, such as Markov chains developed over a century ago, could also perform next-word prediction but were limited by their inability to consider broader context beyond a few preceding words.5 In contrast, modern LLMs like ChatGPT are built with billions of parameters and trained on a significant portion of the publicly available internet, allowing them to grasp complex dependencies, nuances, and statistical patterns in language. This immense scale enables them to generate plausible, sophisticated, and human-like text, moving far beyond simple autocomplete functions.5 The fundamental interaction model remains reactive: the system awaits a specific prompt from a user and then generates content in response.6

The proliferation of these powerful and accessible tools is fundamentally altering the nature of content-related work. As Generative AI becomes capable of producing high-quality baseline content on demand, the economic value is shifting away from the manual act of creation itself. Instead, value is increasingly found in the strategic direction provided to the AI. The ability to craft a precise, context-rich prompt that elicits the desired output—a skill often termed “prompt engineering”—is becoming a critical competency. This dynamic reframes the human role from a pure creator to that of a creative director, strategist, and editor, who leverages AI as a powerful force multiplier. The technology is not merely replacing human effort but is creating a new collaborative layer where the quality of the human’s strategic input directly determines the quality of the AI’s output.

2.2 Core Capabilities and Applications: A Survey of the Generative Toolkit

The applications of Generative AI span a wide array of domains, reflecting the versatility of the underlying foundation models. These tools are being integrated into workflows across industries to boost productivity, support creative tasks, and automate communication.4 A survey of its core capabilities reveals a comprehensive toolkit for modern knowledge work.

A primary and widely adopted application is text generation. Generative models can produce a vast range of written content, from drafting professional emails, marketing copy, and technical reports to generating creative works like poems and stories.2 This capability extends to

content summarization and synthesis, where the AI can distill lengthy documents, research papers, or conversations into concise and digestible summaries, enabling users to quickly grasp key information from large volumes of unstructured data.2

Beyond text, Generative AI has made significant strides in multimedia creation. Tools such as DALL-E 3, Midjourney, and Stable Diffusion can generate high-quality, original images and artwork from simple text descriptions.8 This extends to video and audio, where emerging models can create animations or synthesize natural-sounding speech for applications like virtual assistants and audiobook narration.8

In the realm of software development, Generative AI is serving as a powerful assistant to programmers. Platforms like GitHub Copilot integrate directly into development environments to suggest code snippets, complete functions, translate between programming languages, and help debug existing code.2 This accelerates the development lifecycle and allows engineers to focus on higher-level architectural problems.10

Another sophisticated application is synthetic data generation. Generative models can create new, artificial data that mimics the statistical properties of a real-world dataset. This synthetic data is invaluable for training other machine learning models, especially in domains where real-world data is scarce, sensitive, or expensive to acquire, such as in healthcare or finance.4

The commercial landscape is populated with numerous well-known applications that have brought these capabilities to millions of users. These include conversational AIs like ChatGPT and Gemini, writing assistants like Grammarly, and integrated solutions like Microsoft Copilot and Adobe Firefly, which embed generative features into existing productivity suites.9 The overarching business value of these tools lies in their ability to enhance productivity and foster innovation by automating the creation of content and streamlining specific, input-driven tasks.4

3.0 The Agentic Leap: From Content Generation to Autonomous Action

While Generative AI represents a revolution in content creation, the next frontier in artificial intelligence is defined by a shift from creation to action. Agentic AI marks this evolution, introducing systems designed not just to respond, but to act independently to achieve complex goals. This leap toward autonomy promises to unlock a new echelon of productivity and transform the fundamental nature of business process automation.

3.1 The Dawn of Autonomous Systems: Defining Agency

Agentic AI is an advanced form of artificial intelligence centered on the development of autonomous systems capable of making decisions and performing tasks with minimal human intervention.13 The term “agentic” derives from the concept of agency—the capacity of a system to act independently and purposefully within its environment to achieve a predetermined set of goals.15 This stands in stark contrast to traditional AI, including Generative AI, which is fundamentally reactive. A generative model waits for a prompt and follows a predefined workflow to produce an output; an agentic system, once given a high-level objective, can proactively formulate and execute a plan to meet that objective.7

The core distinction lies in the transition from a request-response model to a goal-driven one. An agentic system is not simply following step-by-step instructions. Instead, it is capable of breaking down a complex goal into a sequence of smaller, manageable sub-tasks and pursuing them independently.17 This proactive nature allows it to anticipate needs, identify potential problems, and take initiative to address issues before they escalate, a capability far beyond the scope of reactive systems.15

3.2 Anatomy of an AI Agent: The Operational Loop

The functionality of Agentic AI is built upon a set of key characteristics that enable its autonomous behavior. These systems are designed to be:

• Proactive & Autonomous: They do not wait for explicit commands for each action. Instead, they operate with a degree of independence to perform tasks without constant human oversight, allowing them to manage long-term goals and multi-step problems.15
• Adaptable: A critical feature is the ability to learn from interactions and adapt to changing environments. They can adjust their strategies and actions in real-time based on new information or feedback, making them suitable for dynamic and unpredictable situations.12
• Goal-Oriented: AI agents are explicitly designed to achieve specific objectives. They reason about the steps required to reach a goal and formulate a strategy to do so.14
• Reasoning & Planning: At the heart of an agentic system is a reasoning engine, which is often a powerful large language model (LLM). The LLM serves as the agent’s “brain,” enabling it to analyze data, understand context, formulate potential solutions, and plan a course of action.14

The operation of an AI agent can be understood as a continuous, cyclical process. This loop allows the agent to interact intelligently with its environment and refine its performance over time 16:

1. Perception: The agent begins by collecting data from its environment. This can come from various sources, including APIs, databases, sensors, or direct user interactions.
2. Reasoning: The collected data is then processed to extract meaningful insights. Using capabilities like natural language processing, the agent interprets the information, detects patterns, and understands the broader context.
3. Decision-Making: Based on its reasoning and predefined goals, the agent evaluates multiple possible actions. It chooses the optimal course of action based on factors like efficiency, probability of success, and predicted outcomes.
4. Execution: The agent carries out the chosen action. This often involves interacting with external systems by calling APIs, manipulating data, or communicating with users.
5. Learning and Adaptation: After execution, the agent evaluates the outcome. This feedback is used to refine its internal models and improve future decision-making, often through techniques like reinforcement learning.

This operational framework transforms the raw potential of foundation models into practical, goal-oriented action. While an LLM provides the core intelligence for reasoning and understanding, it is the agentic framework—the components for planning, tool use, memory, and environmental interaction—that allows this intelligence to be applied to real-world tasks. This relationship is analogous to that of a computer’s operating system and its application software. The LLM is the powerful operating system providing fundamental capabilities, while the agentic system is the application layer that harnesses these capabilities to perform specific, valuable functions for the user, moving far beyond a simple chat interface.

3.3 The Power of Collaboration: Orchestration and Multi-Agent Systems

The complexity of real-world business problems often requires a diverse set of skills and domain knowledge. To address this, advanced agentic systems are frequently designed as multi-agent systems, where multiple, highly specialized AI agents collaborate to achieve a common goal.15 For instance, in a financial services setting, one agent might specialize in regulatory compliance, another in fraud detection, and a third in portfolio optimization. These agents coordinate their activities, share insights, and hand off tasks as needed to provide a comprehensive solution that would be beyond the capability of a single, generalized agent.15

This collaborative model necessitates a critical function known as orchestration. Orchestration is the overarching management and coordination of the various AI agents and systems within an ecosystem.16 An orchestration platform is responsible for automating workflows, tracking progress toward goals, managing resource allocation, and handling failures. It ensures that the individual agents work together harmoniously and efficiently. This clarifies the distinction between an “AI agent,” which can be seen as an individual tool or specialist, and “Agentic AI,” which refers to the coordinated system that manages these agents to achieve broader, more complex objectives.14 Technology platforms like Amazon Bedrock and Google’s Vertex AI Agent Builder are being developed to provide the infrastructure for building and orchestrating these sophisticated multi-agent systems, signaling a significant architectural shift in AI development from monolithic models to collaborative digital workforces.14

4.0 A Comparative Framework: Generative vs. Agentic Systems

Understanding the distinctions and the relationship between Generative and Agentic AI is crucial for any organization seeking to develop a coherent AI strategy. While both leverage similar underlying technologies, their purpose, interaction models, and operational scope are fundamentally different. Agentic AI is not a competitor to Generative AI but rather a functional evolution that builds upon its capabilities to move from content creation to task execution.

4.1 Reactive Prompts vs. Proactive Goals: The Core Distinction

The most fundamental difference between the two paradigms lies in their operational posture: Generative AI is reactive, while Agentic AI is proactive.7 A generative system is designed to

create content in direct response to a specific user prompt. It is a passive tool that awaits instructions.17 In contrast, an agentic system is designed to

act in pursuit of a high-level goal. It is an active participant that takes initiative based on its objectives and its perception of the environment.15

This distinction can be clarified with a practical analogy. Generative AI is akin to a highly skilled specialist, such as a copywriter or a graphic designer. One provides this specialist with a detailed brief (“write a 500-word blog post on topic X in a professional tone”), and they execute that specific task. They will not, however, independently decide that a blog post is needed, research the topic without being asked, or schedule its publication.6 Agentic AI, on the other hand, is analogous to an autonomous project manager. One gives this manager a high-level objective (“increase engagement with our target audience this quarter”). The agentic manager would then independently devise a plan, which might include commissioning a series of blog posts (a task it would delegate to a generative model), scheduling social media updates, analyzing engagement data, and adjusting the strategy based on performance, all without requiring step-by-step instructions for each action.7

This difference in function directly impacts the nature of human interaction. With Generative AI, the user is “in the loop,” providing constant direction and making decisions at each stage of the process. For Agentic AI, the user is “on the loop,” setting the overall goals and providing oversight, but intervening primarily to handle exceptions or provide strategic guidance when the agent encounters a situation beyond its programming.6

4.2 A Symbiotic Relationship: Evolution, Not Revolution

It is critical to recognize that Agentic AI does not supplant Generative AI; rather, it extends its capabilities in a symbiotic relationship.16 Agentic systems rely on generative models, specifically LLMs, as their central processing unit or “brain”.14 The LLM provides the crucial cognitive functions of reasoning, language understanding, and planning that allow the agent to interpret goals, analyze situations, and formulate strategies.

A clear example illustrates this synergy. A sales representative could use a pure Generative AI tool by prompting it, “Write a polite follow-up email to Maria Wang about our proposal.” The AI would generate the text, but the representative would then need to manually copy it into an email client, find Maria’s contact information, send the email, and then update their customer relationship management (CRM) system. This is a series of discrete, human-driven tasks augmented by AI.7

An agentic system would handle the same objective differently. The representative would set a high-level rule or goal, such as, “For any lead marked ‘Follow-up required,’ send a follow-up email after two business days.” The agentic system would then autonomously execute a multi-step workflow. It would monitor the CRM for the trigger, wait the specified time, retrieve Maria’s details from the CRM, use a generative model to compose a personalized email, send the email via an API call, and finally, update the CRM to log the action. In this workflow, the Generative AI is a vital component—a tool that the agent uses to complete one step of its broader, autonomous plan.7

This relationship highlights that the line between these two concepts is becoming increasingly blurred in commercial applications. The theoretical distinction between reactive content generation and proactive goal achievement is clear, but in practice, products are emerging that occupy a middle ground. Advanced generative tools like ChatGPT are incorporating features such as “function calling,” which allows them to interact with external tools and perform simple, chained actions, thereby exhibiting nascent agentic behaviors.2 Conversely, agentic systems are fundamentally dependent on generative capabilities for their core intelligence.14 This convergence suggests that the market is evolving not as a binary choice between two distinct technologies, but as a spectrum of AI capabilities. This creates a challenge for business leaders, who must look beyond marketing labels to accurately assess the true level of autonomy and intelligence a particular product offers.

4.3 Table 1: Generative AI vs. Agentic AI - A Feature-by-Feature Comparison

The following table provides a concise, feature-level comparison to summarize the key distinctions between the Generative and Agentic AI paradigms.

Aspect	Generative AI (The Content Creator)	Agentic AI (The Autonomous Actor)	Supporting Snippets
Primary Function	To create novel content (text, images, code) based on learned patterns.	To act and achieve high-level goals by executing multi-step tasks.	2
Interaction Model	Reactive: Responds to specific, direct user prompts.	Proactive: Takes initiative based on goals and environmental data.	7
Autonomy Level	Low (Human-in-the-Loop): Requires step-by-step human guidance for each output.	High (Human-on-the-Loop): Operates independently with human oversight for exceptions.	6
Input Method	Specific Prompts: “Write an email about X.”	High-Level Goals: “Manage follow-ups for all new sales leads.”	7
Scope of Work	Narrow, Defined Tasks: Content generation, summarization, translation.	Broad, Complex Workflows: Process automation, problem-solving, system management.	19
Core Mechanism	Pattern Recognition & Prediction: Predicts the next item in a sequence.	Perception-Reasoning-Action Loop: Senses, plans, decides, executes, and learns.	2
Tool Integration	Limited: Can be integrated as a feature within a larger application.	Extensive: Natively designed to call external tools, APIs, and other systems to act.	14
Business Analogy	A highly skilled specialist or assistant (e.g., a copywriter, a coder, a researcher).	An autonomous project manager or a digital employee.	6

5.0 The Agentic Enterprise: Transforming Industries with Autonomous Workflows

The theoretical promise of Agentic AI is rapidly translating into practical applications that are poised to redefine operational efficiency and strategic capability across a multitude of industries. By automating not just simple tasks but complex, end-to-end workflows, agentic systems are enabling a new paradigm of enterprise productivity. This represents a significant evolution from earlier automation technologies, augmenting human potential rather than simply replacing manual labor.

5.1 A Cross-Sector Analysis of Impact

The versatility of Agentic AI, stemming from its ability to reason, plan, and interact with digital systems, allows for its application in virtually any domain that relies on complex information processing and decision-making.

• Customer Service: Agentic AI is transforming customer support from reactive, script-based chatbots to proactive service agents. These systems can autonomously manage customer inquiries, access knowledge bases to resolve complex issues, process refunds or returns, and deliver personalized support across multiple channels, only escalating to human agents for the most nuanced or empathetic interactions.14
• Finance and Banking: In the data-intensive financial sector, AI agents are being deployed to automate fraud detection by monitoring transactions in real-time and taking action to block suspicious activity. They can also perform continuous risk assessments, manage investment portfolios based on market conditions and predefined strategies, and ensure regulatory compliance by monitoring for changes in law and updating internal policies accordingly.14
• Healthcare: The potential impact in healthcare is profound. Agentic systems can assist clinicians by analyzing medical records, research papers, and clinical trial data to help with diagnosis and treatment planning. They can also be used for proactive patient monitoring, using data from wearable devices to detect early signs of health issues and alert care teams, or even to automatically schedule follow-up appointments.14
• Supply Chain and Logistics: Agentic AI can create highly resilient and efficient supply chains. By analyzing data from sales, inventory, weather, and shipping, these systems can predict demand, optimize logistics, and proactively reroute shipments to avoid delays, all while minimizing costs and human oversight.14
• Software Development and IT Operations: Beyond simple code generation, AI agents can automate the entire software development lifecycle, including debugging, testing, and deployment. In IT service management, agents can move beyond being simple helpdesk bots to autonomously resolving complex IT tickets, troubleshooting network issues, and managing software provisioning.14
• Human Resources: HR departments can leverage agentic workflows to automate time-consuming processes such as resume screening, where agents can analyze and score candidates against job criteria. They can also handle interview scheduling by coordinating calendars between candidates and hiring managers, and manage employee onboarding and payroll processes.25

The true, long-term impact of these applications will likely extend beyond the automation of existing tasks. The introduction of autonomous agents allows for a fundamental rethinking of how business processes are designed. Workflows that were previously constrained by the speed and capacity of human decision-making can be re-engineered to be more dynamic, responsive, and data-driven. For example, instead of an agent simply flagging a potential supply chain disruption for a human to review, a fully realized agentic system could autonomously model the financial impact of the disruption, evaluate alternative shipping routes, negotiate new terms with carriers via API, update the enterprise resource planning (ERP) system with the new plan, and notify all relevant stakeholders in real-time. This is not just task automation; it is a complete transformation of the business process itself, enabling a level of operational agility and resilience that was previously unattainable.

5.2 Redefining Productivity: Beyond Robotic Process Automation (RPA)

The rise of agentic automation marks a significant advancement over traditional automation technologies like Robotic Process Automation (RPA). RPA has been highly effective at automating structured, repetitive, and rules-based tasks, such as data entry or invoice processing. However, it often struggles when faced with unstructured data or changes in the underlying applications or processes.7 Screen-scraping bots, for example, can easily break if the user interface of a target website is updated.7

Agentic AI overcomes these limitations. By leveraging the reasoning capabilities of LLMs, AI agents can understand context, handle ambiguity, and adapt to changes in their environment. This allows them to automate a far broader class of work, including complex, end-to-end processes that involve unstructured data and dynamic decision-making.17 This represents a shift from automating simple “robotic” tasks to automating “cognitive” workflows.

Crucially, this new wave of automation is best understood as a form of human augmentation.17 The goal is not to replace human workers but to enhance their capabilities and productivity. By delegating complex, time-consuming, and repetitive tasks to AI agents, human employees are freed to focus on activities that require uniquely human skills: strategic thinking, creative problem-solving, complex negotiation, and building interpersonal relationships.17 This collaboration between humans and AI agents promises to expand an organization’s capacity to tackle complex challenges, drive innovation, and deliver higher-quality outcomes. The significance of this trend is underscored by market forecasts, with Gartner predicting that by 2028, one-third of all enterprise software will include agentic AI capabilities, and at least 15% of day-to-day business decisions will be made autonomously by AI agents.19

6.0 Case Study: Deconstructing the “Agentic Meeting Copilot” - SeaMeet by Seasalt.ai

To ground the theoretical discussion of Agentic AI in a real-world commercial example, this section provides a detailed analysis of SeaMeet, a product from Seasalt.ai. By examining its features, marketing, and strategic positioning, it is possible to deconstruct how the concept of “agency” is being interpreted and implemented in the current market, revealing a nuanced reality that sits between pure generative capabilities and full autonomy.

6.1 Product Overview and Core Generative Capabilities

SeaMeet is marketed as an AI meeting assistant or “copilot” designed to enhance productivity for individuals and teams.31 Its foundational features are centered on processing the content of meetings. The system integrates with popular video conferencing platforms like Google Meet and Microsoft Teams, and can also process uploaded audio files from in-person meetings.31

The core functionalities of the product are:

• Real-Time Transcription: SeaMeet provides highly accurate, real-time transcription of meeting conversations, complete with speaker identification and timestamps.31
• Intelligent Summaries: After a meeting, the system automatically generates intelligent summaries that capture the key topics and discussions.31
• Action Item Detection: The AI analyzes the transcript to automatically identify and list actionable tasks or “to-do” items that were discussed.31

These primary features are clear applications of Generative AI. The system uses sophisticated speech-to-text models to create a transcript (generating text from audio) and then employs LLMs to synthesize this transcript into a new, condensed form (the summary) and to classify certain statements as action items. User reviews consistently highlight the value of these generative capabilities, noting that they eliminate the need for manual note-taking and provide an easy way to track responsibilities, thereby solving a significant pain point for many professionals.34

6.2 Analyzing the “Agentic” Claim: The Email-Based Workflow

SeaMeet distinguishes itself in the market by branding itself as an “Agentic Meeting Copilot”.32 The justification for this claim appears to rest on a specific, innovative feature: an email-based workflow designed to automate post-meeting tasks. After a meeting concludes, SeaMeet sends the generated summary to the user via email. The user can then reply directly to this email with natural language commands such as “draft a follow-up email to the client,” “create a Statement of Work (SOW) based on our discussion,” or “generate a report for stakeholders”.32 The system then processes this request and returns the professionally formatted document, ready to be sent.

A critical evaluation of this workflow against the rigorous definition of Agentic AI established earlier in this report reveals a nuanced picture. While this feature represents a powerful and sophisticated form of workflow automation, it does not demonstrate true, goal-driven autonomy. The system’s actions are entirely reactive; it waits for a specific, human-initiated command via email before it acts. This is a chained task—combining the initial summary with a new user prompt to generate a subsequent document—but it is not proactive.

A truly agentic system, by contrast, might be given a high-level goal like “manage the onboarding for this new client project.” Based on the content of the initial meeting, it could independently recognize the need for an SOW, draft it without being explicitly told to, and perhaps even route it for internal approval. SeaMeet’s workflow, while highly efficient, still relies on a human to be “in the loop,” triggering each major post-meeting action. The lack of publicly available documentation detailing features for autonomous task delegation or completion further supports the assessment that the system operates as an advanced, command-driven assistant rather than a fully autonomous agent.33

6.3 Strategic Context: SeaMeet within the Broader Seasalt.ai Ecosystem

To fully understand SeaMeet’s positioning, it must be viewed within the larger strategic framework of its parent company, Seasalt.ai. Founded in 2020, Seasalt.ai positions itself as an all-in-one contact center and AI automation platform primarily targeting small and medium-sized businesses (SMBs).22 The company’s broader product suite is designed to automate a wide range of business communications and operational workflows. This includes AI-powered voicebots and chatbots for 24/7 customer support, automated appointment scheduling, lead qualification and routing, and management of omni-channel communications (e.g., WhatsApp, SMS, voice) from a unified inbox.22

Within this ecosystem, SeaMeet serves as a key component focused on internal and client-facing meeting intelligence. It complements the company’s external communication automation tools by capturing and structuring the valuable data generated in meetings. This aligns with the company’s overarching mission to provide accessible, end-to-end AI automation solutions for the SMB market, a segment that may lack the resources to build or integrate more complex enterprise-grade systems.38

6.4 Market Positioning: The “Proto-Agentic” Differentiator

The decision to market SeaMeet as “agentic” is a deliberate and strategically astute move. In a crowded market of meeting transcription and summarization tools, the “agentic” label serves as a powerful differentiator. It signals to potential customers that the product offers capabilities beyond standard Generative AI, tapping into the growing industry excitement around autonomous systems.

A more precise classification for this category of tool might be “proto-agentic” or “agent-assisted.” These systems represent an intermediate step on the spectrum between purely generative tools and fully autonomous agents. They excel at automating complex, multi-step tasks but still require a human trigger to initiate the workflow. This positioning allows Seasalt.ai to claim a technologically advanced position without needing to have developed a system with full proactive, goal-seeking capabilities.

This case study reveals a broader trend in the commercialization of AI. As new, powerful concepts like “agency” gain traction, marketing language often precedes the technology’s full maturation. Companies are strategically adopting this terminology to shape the perception of their products and capture market attention. For business leaders and technology evaluators, this underscores the critical importance of developing a nuanced understanding of these concepts. It is no longer sufficient to accept marketing claims at face value; instead, a deeper analysis of a product’s true operational model—its level of proactivity, adaptability, and goal-orientation—is required to make informed purchasing and strategic decisions.

7.0 Strategic Imperatives and Future Outlook

The evolution from Generative to Agentic AI is not merely an incremental technological advancement; it represents a fundamental shift in how businesses can leverage artificial intelligence to create value. This transition necessitates a proactive and well-considered strategic response from enterprise leaders. Navigating this new frontier requires a clear understanding of the immediate opportunities, a plan for strategic adoption, and a vigilant approach to the associated risks and ethical considerations.

7.1 Recommendations for Adoption: Navigating the New AI Frontier

For organizations looking to harness the power of this evolving AI landscape, a pragmatic, two-pronged approach is recommended.

First, businesses should embrace and scale the use of Generative AI now for immediate and demonstrable productivity gains. The technology is mature and accessible enough to be deployed across various functions to automate content creation, accelerate research and data synthesis, and assist in software development.4 This not only delivers short-term ROI but also helps build a foundational “AI-ready” culture within the organization, familiarizing employees with human-AI collaboration.

Second, leaders must begin strategically experimenting with Agentic AI. This should start with identifying well-defined, high-impact business processes that are suitable for automation by an autonomous or semi-autonomous agent. Early use cases in areas like IT service management, customer support ticket resolution, or supply chain monitoring can serve as valuable pilot programs to build internal expertise and demonstrate the potential of agentic workflows.17

Successful implementation of either paradigm, but especially Agentic AI, is contingent on several critical prerequisites:

• Data Readiness: AI agents are only as effective as the data they can access and process. Organizations must invest in creating a clean, well-governed, and accessible enterprise data infrastructure. This “AI-ready” data foundation is essential for enabling agents to make accurate, context-aware decisions.30
• Security and Governance: The power of Agentic AI is directly tied to its deep integration with enterprise systems and its access to sensitive data. This creates profound security and privacy risks.20 Robust security protocols, access controls, and transparent audit trails are not optional but are fundamental requirements. A “human-on-the-loop” governance model, with clear guardrails and oversight, is essential to mitigate risks and ensure that agents operate reliably and safely.15
• Ethical Considerations: AI agents operate based on algorithms and data; they do not possess an innate understanding of human values, ethics, or morals. This creates a significant risk of unintended consequences, particularly in high-stakes domains like healthcare, finance, or law enforcement.20 Organizations must proactively design and test their agentic systems for fairness, bias, and alignment with human values to ensure they act in a manner that is not only effective but also responsible.

7.2 The Trajectory of AI Agency: The Road Ahead

The development of Agentic AI is still in its early stages, but its trajectory points toward a future of increasingly sophisticated and integrated autonomous systems. The current focus on single or small groups of agents tackling specific workflows will likely evolve into large-scale, decentralized multi-agent systems. In this future vision, heterogeneous agents with diverse specializations—some owned by the enterprise, some by partners, some by individuals—will collaborate within a common digital environment to solve highly complex, dynamic problems.40

This technological progression will have a profound impact on the nature of work and the structure of organizations. As the World Economic Forum has highlighted, roles that emphasize complex decision-making, problem-solving, and strategic oversight are becoming increasingly critical in the global economy.41 The rise of agentic systems will accelerate this trend, automating much of the cognitive “plumbing” of the modern enterprise and elevating the human role to one of strategy, creativity, and governance.

Ultimately, the transition from generative to agentic systems marks a pivotal moment in the evolution of the human-computer partnership. It is moving AI from being a tool that we instruct to a collaborator that we empower. For the enterprises that successfully navigate this shift, the reward will be a new level of operational agility, resilience, and innovation, paving the way for a future defined by the seamless integration of human and machine intelligence.

Works cited

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2. What is Generative AI? - University Center for Teaching and Learning, accessed September 6, 2025, https://teaching.pitt.edu/resources/what-is-generative-ai/
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