How to Build an AI Agent (and What Enterprises Should Know First)

You’re staring down a new kind of enterprise challenge: how to move your AI from simple prompts and chatbots to something far more capable. Not just answering questions, but reasoning about goals, taking action across your business systems, and helping automate real work at massive scale. 

An industry survey by PwC found that  79% of individuals report that AI agents have been adopted in their companies, while another report found 83% of organizations think investing in agents helps them stay competitive. Suddenly, it’s not about “using AI” — it’s about building an entire agentic AI system that can keep up with the speed of your company.

More than just another automation tool or interface, AI agents enable independent, dynamic systems that can understand intent, plan multi-step workflows, adapt to new situations, and still operate inside your enterprise guardrails.

This guide gives you the roadmap on getting started. You’ll get clear on what AI agents are, how they work, the components behind them, and how agentic frameworks compare to enterprise-ready platforms built for real deployment.

You’ll also walk away with practical guidance for building agents responsibly, so your teams can move from experimentation to production with clarity and confidence.

What is an AI agent?

An AI agent is an autonomous software system that can perceive its environment, reason about goals, and take action to achieve outcomes without constant human intervention. Unlike generative AI tools that produce text, summaries, or code, AI agents are self-directed and context-aware. They interpret intent, plan multi-step workflows, and interact with enterprise systems to move work forward.

AI agents operate as individual units — each designed for a specific purpose — while agentic AI systems coordinate multiple agents to handle broader, end-to-end processes across a business. 

This distinction matters: an individual agent might resolve a single IT request, while a full agentic system can orchestrate onboarding, procurement, or finance workflows across applications and teams.

How AI agents differ from earlier AI technologies

AI agents represent the next leap beyond chatbots and copilots because they are:

• Autonomous: They can take appropriate actions to meet a goal without ongoing supervision.
• Goal-oriented: They stay focused on objectives, adjusting their approach as inputs or conditions change.
• Context-aware: They use enterprise data and signals to choose the right next step rather than following fixed rules.
  

Enterprise examples

In an enterprise setting, an agent can:

• Update an employee’s payroll information
• Provision access to required apps on day one
• Route and pre-approve a procurement request
• Classify, triage, and resolve common IT issues

And when multiple agents work together, the impact compounds. An enterprise can move from answering a handful of common questions to coordinating full workflows: onboarding a new hire, setting up accounts, preparing equipment, sharing required training, and completing administrative tasks across systems.

Types of intelligent agents

AI agents aren’t one-size-fits-all solutions. Different types of AI agents are designed to perform specific functions, and the most effective solutions often involve multiple agents working together. 

Agents can be grouped into three tiers of increasing complexity:

1. Reactive agents: These are the most basic, rule-based agents. They are designed to react to specific inputs with a defined output, often used for responding to simple, predefined requests.
2. Goal-based agents: These agents possess the ability to plan and adapt their actions to achieve a specific goal. An application of this type would be complex process automation, as they can navigate a multi-step workflow.
3. Agentic systems: These are the most advanced. They are networks of individual agents that coordinate and cooperate to solve complex, multi-step problems across various enterprise systems.

As the level of sophistication increases, these agents are able to go beyond simple responses to become self-directed, context-aware systems capable of executing multi-step tasks.

At Moveworks we classify three core types of AI agents, each purpose-built for a specific role and designed to work together as a cohesive, intelligent system within the enterprise:

• Aggregation agents: These agents are designed to cut through the complexity of information overload by gathering, synthesizing, and presenting relevant data to the user or other agents.
• Action agents: These agents execute tasks on behalf of a user with precision. Their role is to translate a user's request into a reliable, automated action across integrated enterprise systems.
• Ambient agents: These agents operate quietly in the background, constantly monitoring the environment and spring into action as needed in response to signals from other systems or changes in the environment.

In this way, Aggregation, Action, and Ambient agents are the building blocks that the Moveworks Reasoning Engine uses to orchestrate solutions to complex enterprise problems, effectively creating the high-level Agentic Systems that go far beyond simple chatbots or reactive tools.

These specialized agent types work to achieve the larger goal of Agentic Systems by coordinating to solve complex, multi-step problems.

What are the core components of AI agents?

AI agents consist of key components that work together to create intelligent, autonomous systems. Much like humans rely on senses to perceive, a brain to reason, and limbs to act, AI agents depend on a coordinated set of capabilities to function effectively in enterprise environments.

Sensors

Sensors act as an AI agent’s “eyes and ears,” gathering data from its environment. These can be physical devices like cameras or microphones, or digital inputs such as user requests, system events, APIs, databases, and application logs.

In enterprise settings, sensors often take the form of continuous data streams. For example, in HR workflows, an agent may monitor employee record updates, onboarding milestones, or policy changes. This steady flow of information allows the agent to stay aware of changing conditions and respond accurately in real time.

Intelligence (i.e. Reasoning, memory, and planning)

The agent’s intelligence processes inputs from its sensors and determines what to do next. This includes analyzing data, understanding context, recognizing patterns, and making decisions.

Modern AI agents frequently leverage large language models (LLMs) alongside other techniques—such as rules, retrieval systems, and structured workflows—to reason effectively. Beyond understanding language, agents use reasoning to manage memory, reference past interactions, plan multi-step actions, and adapt dynamically as conditions change.

For example, when resolving an IT support issue, an AI agent doesn’t rely on keywords alone. It can understand the full context of the request, reference previous tickets or known issues, retrieve relevant documentation, and determine the correct sequence of actions to resolve the problem efficiently.

Natural language processing enables agents to interact conversationally, making them far more intuitive than traditional automation tools while still delivering precise, reliable outcomes.

Actuators

Actuators serve as the agent’s “hands,” enabling it to take action and influence its environment. In software-based agents, actuators may include updating enterprise systems, provisioning access, triggering workflows, or sending communications.

These actions change the environment, which the agent then observes again through its sensors—creating a continuous feedback loop.

During employee onboarding, for example, an agent might:

• Update HR records
• Provision IT access
• Send welcome communications

All able to be executed in the correct order, without missing steps or manual intervention.

Plugins

AI agents extend their capabilities through plugins, which connect them to enterprise systems and data sources, enabling access to real-time information, real-world actions, and secure operation within organizational guardrails.

By combining reasoning with direct system access, agents can move beyond answering questions to executing complete workflows—while remaining grounded in accurate, enterprise-approved data.

Methods for building AI agents

Traditionally, organizations had to build intelligence, sensors, and actuator components from scratch. 

Today, AI agent platforms provide pre-built tools, security features, and enterprise-specific options, simplifying development and deployment. These platforms ensure that the agent’s "brain" and "body" are fully functional, interconnected, and ready to tackle real-world challenges. Most businesses end up choosing between building from scratch, enterprise-grade agentic platforms  or an AI agent builder. Let's examine how these approaches compare in enterprise implementations.

See AI agents in action. Watch our webinar on how AI drives enterprise-wide ROI and productivity.

Build from scratch: The most technical path

Developing a custom AI agent gives organizations maximum control but requires significant expertise, time, and resources. This approach typically involves designing and assembling core agent components—such as reasoning, memory, orchestration, and system integrations—either entirely in-house or by building on top of agentic frameworks.

Agentic frameworks are toolkits (such as LangGraph or CrewAI) that provide foundational building blocks for creating AI agents. They typically handle core mechanics like:

• Large language model (LLM) integration
• Reasoning loops
• Memory stores
• Task orchestration

Which allows developers to define how an agent perceives inputs, reasons over them, takes action, and learns from outcomes. These frameworks make it easier for teams to experiment, iterate, and prototype agent behaviors in a realistic development workflow.

Custom builds need extensive resourcing and maintenance 

However, while agentic frameworks accelerate early development, they stop short of delivering production-ready agents. Teams are still responsible for manually integrating agents with enterprise systems, managing permissions, handling failures, enforcing governance, and ensuring reliability at scale. As a result, deploying framework-based agents in real enterprise environments often requires significant custom engineering beyond the framework itself.

Organizations pursuing custom or framework-based agent development need specialized teams with expertise in machine learning, natural language processing, distributed systems, and enterprise integration. The process typically includes:

• Designing custom logic and workflows tailored to specific use cases
• Iterating on reasoning and planning behavior through testing and prompt refinement
• Building and maintaining data pipelines for real-time inputs
• Creating and maintaining secure integrations with enterprise systems
• Debugging and optimizing agent behavior to ensure consistency and reliability

While frameworks can make experimentation more accessible, maintaining a custom-built agent over time remains resource-intensive. Teams must manage model updates, memory systems, orchestration logic, system integrations, and operational concerns such as monitoring, security, and compliance. Without native governance and scalability, framework-based agents can become brittle as complexity grows.

For these reasons, agentic frameworks are often best suited for prototyping, research, or narrowly scoped use cases rather than full-scale enterprise deployment. Most enterprises ultimately require agentic platforms purpose-built for production—platforms that combine intelligent reasoning with enterprise-grade security, governance, observability, and scalability.

That’s why many organizations choose enterprise-grade agentic platforms or AI agent builders instead of building from scratch or relying solely on frameworks. Let’s examine how these approaches compare in enterprise implementations.

Use an AI agent-building platform

Enterprise-ready AI agent platforms help teams launch and scale AI agents faster, while giving organizations the governance, security, and reliability they expect. No-code and low-code tools — like those in Moveworks Agent Studio — streamline development by providing pre-built reasoning, integration, and governance layers. The result is a simpler build process without giving up control or functionality.

Enterprise-grade AI agent platforms, from Dialogflow and Microsoft Bot Framework to IBM watsonx Assistant and Moveworks, help teams stand up agents quickly and iterate with confidence. 

Agentic platforms like Moveworks take that a step further with installable, purpose-built agents available through a growing marketplace, letting organizations activate proven use cases instead of building everything from scratch.

Fast time to value, enterprise-ready scale

With an AI agent builder, teams avoid managing complex codebases or underlying infrastructure. Instead, they use intuitive interfaces to configure behavior, set decision logic, and tune responses — often deploying new agents or updates in hours, not weeks. This speed unlocks rapid prototyping, continuous iteration, and faster time to value across workflows like customer support, IT service delivery, and HR operations.

These platforms also simplify integration through secure, pre-built connectors to enterprise systems like ServiceNow, Workday, Jira, and SAP. Built-in compliance controls, testing, monitoring, and performance analytics ensure agents operate reliably and within organizational guardrails. 

As a result, enterprise teams can focus less on implementation complexity and more on outcomes, like resolving support requests faster, reducing manual effort, and delivering consistent, high-quality experiences at scale.

Build custom AI agents with Moveworks

Custom agents can help your organization automate work at scale, but success depends on using a platform designed for enterprise reliability, governance, and speed.

Moveworks Agent Studio provides a unified, low-code environment to create and deploy AI agents powered by our agentic Reasoning Engine. Teams can:

• Automate routine processes across IT, HR, finance, and other functions using pre-built plugins and proven agent templates.
• Connect to existing systems through secure, pre-built integrations that help reduce inefficiencies across your workflows.
• Support complex, multi-step automations without managing underlying infrastructure, enabling faster iteration and safer deployment across teams.
• Improve workflows over time with built-in analytics and monitoring designed to surface opportunities for optimization.

Agent Studio brings enterprise governance, connectors, and development tooling into a single platform. That means you can activate new automations quickly while keeping every agent aligned to your systems, permissions, and security standards.

Moveworks helps your teams refocus on high-impact work by reducing manual effort, consolidating development in one place, and enabling scalable agentic automation across the enterprise.

Ready to get started? Request a demo to see how Moveworks can accelerate your AI journey.