The AI workforce explained
"Agents are coming. In the next few years, they will utterly change how we live our lives, online and off."
Bill Gates, Founder of Microsoft
For years, it’s been said that AI would one day be sophisticated enough to assume the responsibilities of humans in the workplace. Due to the latest advancements in AI technology—the AI agent—the era of the AI workforce is finally here.
The AI workforce is a group of AI agents that work together to achieve a common goal as part of a multi-agent system. These intelligent agents work autonomously or collaboratively with humans to streamline workflows and automate complex tasks that have been out of reach until now.
Unlike generative AI that’s merely reactive, agentic AI is proactive and autonomous. It can reason, iteratively solve problems, and use real-time data and tools to tackle real business problems in novel ways.
Due to the increasing adoption and development of AI agents, the AI workforce is a fast-growing segment of the labor pool. In fact, Gartner predicts that AI agents will make 15% of all day-to-day routine work decisions by 2028.
As we transition to a future where every business has both a human and an AI workforce working in tandem, there’s a lot to understand.
In this article, we explore the opportunities, challenges, and capabilities of the AI workforce so you can thrive in the fast-approaching era of AI.
How to build an AI workforce
The AI workforce involves multiple AI agents working together to achieve a common goal, sometimes in collaboration with human employees.
At a high level, the AI workforce is made up of a four main elements:
AI agents: These are digital workers of the AI workforce. They have the ability to perceive their environment, iteratively solve problems, and learn from experience in pursuit of predefined goals. Agents are typically built on large language models (LLMs) like GPT4, which gives them the ability to understand and interact using natural language, while providing a core of general knowledge.
Their environment: This includes all the systems, data, context, and spaces that AI agents are programmed to perceive and interact with. For instance, a customer service AI agent could retrieve CRM data, perform web searches, or refer to company FAQs. Once integrated with existing systems, agents can use any available components in their environment to reach their goal.
Their tools: AI agents have the ability to use tools, which meaningfully expands the functionality and intelligence of the AI workforce. This could include the use of APIs to retrieve real-time weather data, software integrations, or even mutli-step agentic workflows. Tool use effectively enables the workforce to scale and adapt across diverse tasks, integrate with existing workflows and systems, and automate complex tasks using real-time data.
The multi-agent system: This is a type of AI architecture that enables a network of independent, interacting AI agents to collaborate, communicate, and achieve shared goals. Each agent in the system is autonomous, capable of decision-making, and often specialized for specific functions. Together, they can tackle complex, dynamic challenges beyond the capabilities of any single AI agent system.
Characteristics of the AI workforce
To understand the awesome potential of the AI workforce, it’s important to have a basic sense of what makes intelligent agents, well, work.
The following characteristics have enabled AI to transcend the pages of science fiction and become a near-term reality for enterprise operations:
Human-like decision-making
Agentic AI is the first AI system that can reason, use tools, plan action, and learn from experiences to self-improve over time. If these capabilities sound human, it’s because they are.
AI agents are designed to mimic human-like decision making and action, combining goal-oriented behavior with advanced machine learning. This capacity for autonomy, proactivity, and adaptability equips the AI workforce to handle dynamic and unstructured environments that earlier AI could not.
From static models to autonomous systems
Unlike GenAI models that are limited to creating content as prompted, agentic AI employs a modular structure of multiple autonomous LLM-powered agents working in concert. This orchestrated system represents a major leap forward in how AI operates, greatly empowering the AI workforce.The modular structure excels at tackling large-scale problems by coordinating specialized agents to break tasks into manageable components, offering unprecedented flexibility, adaptability, and scalability to address evolving needs in dynamic environments. Moreover, it simplifies development and maintenance, allowing for seamless upgrades to single agents without disrupting the entire system.
Domain-specific intelligence
The true power of the AI workforce, as with human teamwork, lies in harnessing the domain expertise of multiple agents working in coordination. In the latest vertical AI agent systems, each agent is tailored to industry-specific functions, leveraging its own domain-specific reasoning engine (LLM) that's fine-tuned to specific knowledge and AI agentic workflows.
By combining the expertise of multiple agents within an adaptive, real-time system, vertical agent solutions can perform intricate tasks to address unique challenges in dynamic environments—promising the possibility of end-to-end workflow automation.
Types of agents in the AI workforce
To recap, an effective AI workforce is an agentic system in which a diverse team of AI agents are equipped with relevant tools and collaborate toward a common goal. As a result, each business will have a unique collection of AI agents, custom-developed to meet their specific needs and workflows.
Three main types of AI agent systems are available, depending on the required structure, scope, complexity, and interaction dynamics of the AI agentic workflow:
1. Task-specific agents
An AI agent that focuses on one particular job within the larger system. Designed to handle a specific function or solve a narrowly defined problem within a particular domain, task-specific agents excel at executing well-defined tasks with precision and efficiency. Often, these tasks do not involve decision-making. Instead, the agent might delegate tasks to sub-agents, retrieve data, or verify outputs from other agents for accuracy.
Example: A task-specific agent that routes customer support queries to other agents based on priority.
2. Multi-agent systems (MAS)
A multi-agent system (MAS) is a collection of autonomous agents that collaborate to solve a set of interconnected problems or achieve a shared goal. Commonly used for multi-step workflows, multi-agent systems act as distributed modules that work together by communicating and coordinating tasks, offering scalability and adaptability in complex workflows. For instance, an MAS can involve a lead agent that delegates subtasks to other agents, then integrates their outputs and ensures contextually accurate and compliant outputs.
Example: Financial portfolio management. When queried about a customer’s portfolio performance, market risks, and investment opportunities, the orchestrator agent splits the query in subtasks, assigning sub-agents to access the portfolio database, risk assessment tools, and market APIs, then combines the results into a personalized investment report.
3. Human-augmented agents
This is an agent system designed to collaborate with humans by automating complex tasks while incorporating human oversight, feedback, or decision-making.
As AI technology continues to advance and become increasingly integrated with business processes, this human-AI collaboration will serve as the starting point for many businesses as they augment human productivity.
Depending on application needs, human-augmented agent systems can include:
Humans-in-the-loop (HITL) agents: These agents integrate human feedback to validate, refine, or override their outputs and decisions to ensure contextual accuracy or compliance. This combines the benefits of automation with human judgment and expertise, often used in high-stakes applications like finance or healthcare.
Supervisory agent: The agent monitors processes, flags anomalies, and recommends corrective action for human validation or intervention. Example: agent monitors network traffic for cybersecurity threats.
Collaborative agent: The agent interacts with humans in real-time to provide insights, suggestions, and assist in task execution within predefined boundaries.
Example: Microsoft Co-pilot for coding is a collaborative AI agent. It's embedded directly into the Microsoft suite to help users create PowerPoint or Docs more effeciently.
How to build an AI agent: 5 key components
It takes a considered and careful approach to build an AI agent. This involves combining various AI technologies such as machine learning and natural language processing and even building your own models, requiring an expert team of machine learning engineers, data scientists, and software engineers to develop, design, train, and manage the AI agent. By carefully designing and training the AI agent, developers can create a powerful tool that streamlines operations, automates tasks, and enhances decision-making.
There are five key components:
Large Language Model (LLM)
AI agents are built on a powerful foundational model, typically a large language model like GPT4 or Anthropic's Claude. Trained on vast amounts of data, the LLM is the “brain” of an AI agent. It enables them to understand, generate, and respond to users in meaningful and nuanced language, which simplifies how teams interact with agents.
In addition to providing the AI workforce with cognitive and language capabilities, the LLM also operates as its orchestrator, planning how to use individual agents within a larger AI workflow. Custom, domain-specific LLM models are developed and trained to serve as the core of domain-specific agents.
System prompt
The system prompt is the set of overarching instructions that define how an AI agent should behave. This blueprint for action provides the reasoning and decision-making patterns for agents, as well as their personality. A basic example of a system prompt for an AI customer service agent is:
"You are a friendly and informative customer service agent for a tech company. Respond to user inquiries with clear explanations, provide relevant product details, and always strive to be helpful."
System prompts must be carefully engineered to facilitate an appropriate response from the LLM, especially in the context of multi-agent systems with dynamic environments. Different prompts allow for different types of reasoning, such as chain of thought, tree of thought, or ReAct.
Memory
AI agents have a memory module that enables them to act intelligently and adapt over time. Memory allows agents to store and retrieve past experiences, maintain context between user interactions, and create more personalized experiences. For example, an ecommerce AI agent that assists with shopping could, based on both current context and customer history, proactively trigger cart abandonment coupons in checkout or cross-sells to drive conversion.
Memory also enables agents to learn and adapt to evolving circumstances. Through the process of moving data from memory to its knowledge base, the agent can reflect on its actions and outcomes, and adjust its decision-making accordingly.
Reflection and feedback
AI agents have the ability to self-reflect on interactions to enhance their performance and adaptability. By evaluating their own outputs and decision-making processes, they can adapt their strategies through a process of continuous learning that enables them to better handle dynamic environments over time.
Similarly, agents can receive feedback from users that can be incorporated into their core model using methods like reinforcement learning from human feedback (RLHF).
Tools and integrations
AI agents need access to tools and data sources to function effectively. This is because AI agents base their decision-making on what they perceive in their environment, and when they find their available resources lacking, they access new data or functionality required to execute a task.
Tool use effectively expands the functionality and adaptability of the AI workforce, enabling it to solve complex problems that exceed the information in its knowledge base.
Tools for AI agents fall into three buckets:
Informational tools: Includes internal resources like FAQs and product documentation, as well as knowledge bases, web searches, or APIs for specific data sources like news or stock prices.
Functional tools: APIs or software integrations that run a particular action, such as “send an email” or “schedule meeting.”
Case-specific custom workflows: Typically made up of multiple domain-specific LLMs each with an expertly crafted system-prompt, workflow tools give agents access to specialized functions.
Multi-agent systems and the AI workforce
As with human teamwork, the expertise of different disciplines is often required to effectively complete a complex task. For example, to launch a marketing campaign would require a content writer to create ad copy, a designer to design graphics, and a campaign manager to track performance and optimize to ensure a good outcome. If you only had a content writer, the design quality and data feedback would be lacking, and the campaign would flounder for its limitations.
Likewise, multi-agents systems are essential to the AI workforce. They enable multiple agents to effectively collaborate on complex tasks, share data, and tackle challenges that exceed the capabilities of an individual agent. New agents can be added or removed from a multi-agent system as needed, allowing for easy scaling of operations.
These systems can be developed using various design patterns that provide the optimal orchestration for a specific application. For example, in the graphic below, each black dot represents an AI agent as part of a multi-agent system:
Key stages to developing the AI workforce
The AI workforce landscape continues to evolve rapidly, with 75% of organizations saying have already deployed or plan to deploy co-pilot agents in the next year. To remain competitive means moving from where you are now to having your first agent, to an entire AI workforce.
There’s more to it than building models, and here’s a roadmap to guide your development and implementation strategy:
- Define problems and goals: Clearly define the problem you want AI to solve, outlining its desired capabilities and the environment it will operate in.
- Prepare your data: The lifeblood of AI agents is data. Gather relevant data from various sources, then clean, label, and structure it to facilitate high-quality training for the agent.
- Design your agent: Select a suitable architecture for the agent’s applcaitions, and machine learning model that are suitable for your application.
- Train your agent: Using the preprocessed training data, train the AI agent on exmplaes in the data so it can perform tasks on its own.
- Test the agent: Evaluate the agent’s performance in various simulated environments to identify potential issues and refine its decision making process.
- Integrate with systems: Connect the developed agent to existing infrastructure and APIs to enable seamless interaction with other systems and data sources.
- Deploy and monitor: Once integrated, you’ll be able to see how the agent interacts with users and performs in the real-world. Regularly monitor the agent's performance, collect feedback, and update the model to adapt and improve over time.
How to structure and organize AI in the workforce
The integration of AI into the workforce isn’t just a technological upgrade—it’s a transformational shift in how businesses operate. Rather than trying to “bolt on” AI to existing structures and workflows, AI should be incorporated as a core capability into the workforce.
This requires an integrated approach in which AI isn’t siloed off. Rather, each team integrates AI into its workflows and objectives by use case, and per a larger strategic vision. Each AI project is linked to a business objective, and AI professionals should be hired and placed across the organizational chart — rather than centralized in one unit.
For example, a common pitfall to avoid is creating a central group of AI professionals and leaders that are loaned out to different departments on request (as with IT). Instead, AI talent should be accountable for business needs, and therefore should exist across the business as a basic unit of operations.
However, AI professionals and non-technical teams should have access to a centralized one-stop-shop for AI tools and resources. This central AI technical resource provides technical, legal, security and other support as needed to all employees to further efforts in AI adoption.
Integrating AI into the human workforce
Even the most refined and technically sound AI agent solution will fall short of its full potential without a dedicated team of people that understand how to use them. As a result, there's a growing emphasis on AI education and workforce development among business leaders.
Research from IBM finds that up to 120 million workers will need to be retrained or reskilled to effectively work with AI. Given the growing need for technical knowledge, many businesses are investing in upskilling and reskilling their workforce.
Surveying employees is an effective way to assess the AI literacy of employees, as well as identify potential AI talent. There are many overlapping skills between AI and analytics, IT, and other STEM fields, making workers with these faculties prime candidates for skill development.
Hiring specialists is another popular option. A recent report finds that AI is the fastest-growing job sector, with two-thirds of employers are planning to hire talent with specific AI skills, such as prompt engineering.
Critical skills for the AI-enhanced workplace include:
Technical competencies
AI fundamentals and capabilities
Data literacy and analysis
Tool proficiency
AI system monitoring and maintenance
However, even with these skills, the real challenge in collaborating with AI is adapting, learning, and implementing the next wave of AI. This requires a balance of technical know-how and human-centric skills, such as:
Adaptability and continuous learning
Critical thinking and problem-solving
Ethical decision-making
Employees with AI skills “in the loop” are essential to realizing the long-term competitive advantage promised by the AI workforce. To achieve this, employees must understand these new AI capabilities, but also grasp how best to use them going forward.
Responsible development of the AI workforce
For all its potential, the AI workforce isn’t without its risks and ethical concerns. To ensure compliance, security, and sustainability, businesses should pay heed to the ethical considerations of and establish governance for their AI workforce.
Ethical considerations include:
Transparency in decision-making: The logic of AI can be hard to track and understand, raising questions about accountability and explainability.
Biased outputs: Agents can reproduce biases in training data to the detriment of users and the organization’s reputation.
User privacy and data security: Agents may handle sensitive data, requiring robust security measures to safeguard against data breaches.
Balancing automation with human oversight: Establish control mechanisms that validate outputs for accuracy and compliance, planning for HITL and guardrails around sensitive or complex interactions.
A governance framework should include:
Defined roles, responsibilities, and engagement protocols for all stakeholders
Compliance measures for regulatory requirements
Ethical guidelines
Regular audits and assessments to ensure trust and safety
Ready to create an AI workforce?
AI agents are set to reshape how work gets done across the business landscape. By automating a range of complex multi-step tasks, the AI workforce is set to enhance productivity, decision making, and innovation in meaningful ways.
Despite the challenges of integrating new systems, change management, and compliance, many business leaders are already investing heavily in AI workforce development. For example, Accenture has pledged to double its AI workforce to 80,000 professionals.
To learn about how Sendbird can help you develop and deploy AI agents for your unique application, contact our team of AI experts.
To dive deeper, you can explore these resources related to the AI workforce:
