Future of Work - Agentic AI and humans collaboration unlocking exceptional business value

Future of Work - Agentic AI and humans collaboration unlocking exceptional business value.

As AI matures, the availability of so-called “digital labor” is exploding, expanding the very definition of a qualified workforce. What was once the exclusive domain of human talent has now been joined by AI agents capable of handling many tasks once considered beyond the reach of automation—and as a result, according to Salesforce CEO Marc Benioff, the total addressable market for digital labor could soon reach the trillions of dollars.  

The digital workforce is happening.  Humans would be  working side by side with AI agents. We need to prepare our mindset and skill up for this eventuality.

·       As per Gartner, by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously.

·       Furthermore, up to 90% of businesses see agentic AI as a potential source of competitive advantage – gained through efficiency, enhanced decision making or scalability.

·       AI Agents Market worth $52.62 billion by 2030.

Agentic AI - AI agents  mark a new frontier in transforming the workplace. Capable of handling complex tasks with human oversight where needed, these systems can extract information and carry out complex processes. AI agents can access external data sources and maintain memory over time, enabling them to continually improve and take on more complex responsibilities. These "digital associates/workers" are being adopted across various industries to proactively achieve specific objectives.

AI agents possess diverse capabilities that extend beyond multimodal AI capabilities, including making decisions, solving problems, interacting with external environments, and performing actions.

As per Forrester, Agentic AI Is the next competitive Frontier. Process automation tools, predictive analytics, standalone large language models, and RAG systems have delivered significant efficiency gains, but these technologies still require human oversight and structured input. Agentic AI moves beyond these constraints, enabling self-directed decision-making and execution.

We are still in the early stages of agentic AI’s market impact; companies must test, learn, and iterate because these powerful systems can be misaligned, creating actions that are at best undesirable and at worst harmful to your customers and critical applications.

Core characteristics of AI Agents

Planning: Developing a strategic plan to achieve goals is a key aspect of intelligent behavior. AI agents with planning capabilities can identify the necessary steps, evaluate potential actions, and choose the best course of action based on available information and desired outcomes.

Autonomy: A key aspect of AI Agents is their ability to function with minimal or no human intervention after deployment.

Task-Specificity: AI Agents are purpose-built for narrow, well-defined tasks. They are optimized to execute repeatable operations within a fixed domain, such as email content-based filtering or database operations etc.

Action: The ability to take action or perform tasks based on decisions, plans, or external input is crucial for AI agents to interact with their environment and achieve goals. This can include digital actions like sending messages, updating data, or triggering other processes.

Reasoning: This core cognitive process involves using logic and available information to draw conclusions, make inferences, and solve problems.

Self-refining: The capacity for self-improvement and adaptation is a hallmark of advanced AI systems. AI agents with self-refining capabilities can learn from experience, adjust their behavior based on feedback, and continuously enhance their performance and capabilities over time.

Agentic AI in three key uses that are closer to my heart.

1.       AI assisted software development : Two emerging paradigms are changing how developers interact with code: Vibe Coding and Agentic Coding. While both rely on the power of large language models (LLMs), their design philosophies, interaction models, and operational scopes differ substantially.

a)       “Vibe coding” is introduced by renowned Computer scientist  Andrej Karpathy in February 2025 . He had tweeted: "There's a new kind of coding I call 'vibe coding', where you fully give in to the vibes, embrace exponentials, and forget that the code even exists."

At its core, vibe coding is about communicating with AI in natural language to build apps. Instead of writing code, you describe what you want your app to do, and AI tools handle the technical implementation. This frees you up to be more of a manager, focusing on the application's outcomes, functionality, and user experience.

You no longer need to:

·        Learn programming languages and syntax

·        Understand complex technical concepts and low level operations

Instead, you can focus on:

·        Your innovative idea

·        Dialogue , iterate and improve with vibe coding tool

·        Creating value for users

b)       Agentic Coding, on the other hand, prioritizes autonomy. Developers assign high-level goals to intelligent agents — such as “upgrade all web app dependencies” or “refactor the database layer” — and the AI takes over from there. These agents are capable of planning, executing, testing, and refining their own actions, making them well-suited to large-scale, production-grade environments.

Agentic coding marks a paradigm shift in AI-assisted software development. In agentic coding, significant cognitive and operational responsibilities are entrusted to autonomous or semi-autonomous software agents. These agents can plan, execute, and verify complex software tasks, converting natural language instructions into reliable, testable code with minimal human intervention. Achieving this demands the integration of goal planning, task decomposition, execution environments, safety measures, and continuous feedback systems.

Consider a Multi Agent system of coding agents, where one agent could plan based on high level goals. Another agent could do code development, another could do review of code, test designing, another agent could test and iterate. Giving a simplistic view but it illustrates the power of AI agents and autonomy.  Ofcourse, keeping human in the loop for their review/approvals.

2.       Customer Service using Agentic AI

 As per Gartner, Agentic AI has emerged as a game-changer for customer service, paving the way for autonomous and low-effort customer experiences. While previous AI models were limited to generating text or summarizing interactions, agentic AI introduces a new paradigm where AI systems possess the capability to act autonomously to complete tasks.

By 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention, leading to a 30% reduction in operational costs, according to Gartner.

Unlike traditional AI chatbots that answer questions one at a time, AI agents can retain memory, reason, and take independent action. They process customer or employee queries to understand intent and context, and they can ask clarifying questions if needed. Depending on their capabilities and access to external tools, AI agents can resolve customer service tickets, communicate with customers, analyze consumer data, escalate complex issues to human representatives, and deliver personalized service experiences. 

3.       Agentic RAG

Enterprise have been using RAG based solutions for last few years.  However, many RAG solutions involving complex retrievals never went pass POC or Pilot.  Traditional RAG systems are constrained by static workflows and lack the adaptability required for multi-step reasoning and complex task management.

Agentic Retrieval-Augmented Generation (Agentic RAG) transcends these limitations by embedding autonomous AI agents into the RAG pipeline. These agents leverage agentic design patterns - Planning, tool use, reflection and multi-agent collaboration to dynamically manage retrieval strategies, iteratively refine contextual understanding, and adapt workflows to improve outcome.

Way forward 

We are still in early stage of Agentic AI. However, as agents evolve from passive copilots to proactive actors—and scale across the enterprise—the complexity they introduce increases. 

As AI agents gain more autonomy, governance frameworks must evolve to address this increasing independence. While the economic opportunities presented by agentic AI systems are enormous, so too are the associated risks. Ensuring these systems operate securely, ethically, and safely presents a growing challenge as their autonomy rises.

Organizations should implement scalable governance models, robust cybersecurity measures, and comprehensive risk management strategies, along with maintaining human supervision where necessary. Successfully scaling agentic AI systems with these safeguards in place will allow organizations to unlock extraordinary value.