About this Resource
<p><span style="font-size: 16px;"><i>AI agents are evolving from simple chatbots into intelligent teammates that can help run entire businesses. Explore how companies can build internal agents, manage permissions, automate workflows, and create AI systems that continuously improve over time.</i></span></p><h2><br></h2><h2>The AI Agent Every Company is About to Build</h2><p><span style="font-size: 16px;">AI agents are moving beyond simple chatbots. Instead of waiting for someone to type a prompt, the next generation of AI agents could work continuously in the background, understand a company’s systems, and help employees complete real tasks.</span></p><h2><br></h2><h2>From Chatbots to Company Agents</h2><p><span style="font-size: 16px;">One of the biggest opportunities for AI agents is creating an internal “brain” for a company. Rather than simply answering questions, an agent can connect to company knowledge, customer information, data, and business processes. At Vercel, this idea has become "V", an internal agent available through Slack. Employees can use it to find company information, understand customers, create content, analyze data, and coordinate tasks. V is not simply one AI model. It can work with specialized sub-agents, allowing different tasks to be handled by the right capability. For example, a content agent can help with marketing while a data agent can work with information from the company’s data warehouse.</span></p><p><span style="font-size: 16px;"><br></span></p><h2>One “God Agent” or a Team of Agents?</h2><p><span style="font-size: 16px;">A major question for companies is whether they should build one powerful agent that knows everything or create multiple specialized agents. The approach discussed is closer to one central agent that acts as a router. Employees interact with a single interface, while that agent decides which specialized capability should handle the task. This creates a simpler experience for employees while allowing companies to maintain different permissions and access levels behind the scenes. The central agent might send a customer-support task to a support agent or a data question to a data-focused agent.</span></p><p><span style="font-size: 16px;"><br></span></p><h2>The Hard Part Isn't Creating the Agent</h2><p><span style="font-size: 16px;">Creating a basic AI agent is becoming relatively easy. The difficult part is connecting that agent safely to the systems a company already uses. An agent may need access to email, databases, customer records, payment systems, content platforms, or internal tools. Giving an AI unrestricted access to all of these systems creates obvious security risks. Companies therefore need to carefully manage permissions, data access, approvals, security rules, and audit trails. This could become one of the most important responsibilities for teams managing AI inside businesses.</span></p><p><span style="font-size: 16px;"><br></span></p><h2>Agents Need Skills, Tools and Rules</h2><p><span style="font-size: 16px;">A useful company agent needs more than a powerful AI model. It needs instructions that explain its role, tools that allow it to perform tasks, and rules that determine what it is allowed to do. For example, a company could give an agent a tool for working with its WordPress website. The agent could then draft content, while permissions determine whether an employee can publish that content directly or whether human approval is required first. Over time, companies can improve the agent by adding new skills and refining existing ones. If the agent repeatedly produces poor content, the company can update its content-writing instructions rather than correcting every individual piece of work. This creates a new kind of work, improving the intelligence that performs the work.</span></p><p><span style="font-size: 16px;"><br></span></p><h2>The Rise of Proactive Agents</h2><p><span style="font-size: 16px;">Today's AI tools generally wait for people to ask questions. The next step is for agents to become proactive. An agent could monitor events happening across a company and automatically respond when something needs attention. For example, a payment failure could trigger an agent, or an incoming email could start a specific workflow. At Vercel, agents can also provide information on a schedule. Regular reports can highlight important activity across different product areas, allowing the agent to do useful work in the background rather than only responding to prompts.</span></p><p><span style="font-size: 16px;"><br></span></p><h2>Agents Will Need to Learn From Their Mistakes</h2><p><span style="font-size: 16px;">For these systems to become genuinely useful, they also need to improve over time. Vercel uses feedback and evaluations to identify where its agents are making mistakes. Employees can provide feedback on responses, and that information can be used to improve the agent's skills and behavior. The idea is similar to testing software: companies can create tests to check whether an agent is giving accurate information, following the right rules, and behaving appropriately. Human oversight remains important, especially when agents have access to sensitive company systems.</span></p><p><span style="font-size: 16px;"><br></span></p><h2>How Companies Can Get Started</h2><p><span style="font-size: 16px;">The recommendation is surprisingly simple, start small. Instead of trying to build an agent that runs the entire company, identify one repetitive task that takes time but follows a predictable process.</span></p><p><br></p><p><span style="font-size: 16px;">Then:</span></p><p></p><ul><li><span style="font-size: 16px;">Create a basic agent with clear instructions.
</span></li><li><span style="font-size: 16px;">Connect it to the communication tool your team already uses.
</span></li><li><span style="font-size: 16px;">Give it access to the specific tools it needs.
</span></li><li><span style="font-size: 16px;">Set clear permissions and approval requirements.
</span></li><li><span style="font-size: 16px;">Measure how well it performs.
</span></li><li><span style="font-size: 16px;">Improve its skills based on real-world feedback.</span></li></ul><span style="font-size: 16px;"><br></span><p></p><p><span style="font-size: 16px;">A good starting point could be something like preparing reports, summarizing information, drafting content, or handling another repetitive workflow. The goal is to find a task where automation can produce a clear benefit.</span></p><p><br></p><h2>A New Kind of Company Infrastructure</h2><p><span style="font-size: 16px;">The larger idea is that AI agents could eventually become a standard part of how companies operate. Instead of simply buying access to an AI chatbot, businesses could build an intelligence layer of their own one that understands their data, processes, culture, tools, and goals. The vision is that creating this company agent could eventually become almost as fundamental as creating a website when starting a business. The agent could act as a constant partner that learns from the company and helps employees make decisions and complete work. The key shift is ownership. Companies may not want their intelligence tied to one particular AI provider. Instead, they could own their agent's instructions, skills, data connections, and workflows while choosing different AI models depending on the task. As AI models become faster and cheaper, this approach could become increasingly practical.</span></p><p><span style="font-size: 16px;"><br></span></p><h2>Final Thoughts</h2><p><span style="font-size: 16px;">The future of AI at work may not be about giving every employee another chatbot. It could be about building an AI teammate for the entire company. The most successful companies may be those that learn how to build, secure, manage, and continuously improve these agents. Rather than waiting for the perfect system, businesses can start by automating one useful, repetitive task and expand from there.</span></p>