What are AI agents? Definition and how they work

An AI agent is an autonomous software system that perceives its environment, makes decisions, and acts to achieve goals with minimal human input.

Picture this: one of your customers asks a question at midnight, and an AI system instantly pulls their order history, checks inventory, and solves their problem, all before you wake up. That’s an AI agent at work, handling complex tasks independently while you focus on other priorities.

For business professionals, AI agents mean handing off repetitive work to intelligent systems that learn, adapt, and deliver results while you focus on strategy and growth.

What are AI agents?

AI agents are autonomous software systems that observe their surroundings, make decisions, and take action to accomplish specific goals with minimal human input. Think of them as digital employees that work independently toward defined objectives

Traditional software follows rigid instructions: if X happens, do Y. AI agents are different. They assess situations, weigh options, and choose the best path forward as conditions change. Where conventional automation needs explicit programming for every scenario, AI agents figure out what to do on their own.

According to Grand View Research, the global AI agents market was valued at approximately $5.4 billion and is projected to reach $50.3 billion by 2030, an annual growth rate of 45.8%. This surge reflects how quickly organizations are adopting AI agents to transform operations and generate measurable ROI.

Key characteristics of AI agents

Four core traits set AI agents apart from traditional software and automation tools:

• Autonomy and independent decision-making: AI agents operate without constant supervision, choosing actions based on programmed goals and real-time context rather than waiting for human input
• Ability to perceive and interact with their environment: They gather information from sources such as databases, APIs, sensors, and user inputs, then respond by acting on their surroundings or connected systems
• Goal-oriented behavior and task completion: AI agents pursue defined objectives, breaking down complex assignments into manageable steps and persisting until they reach a result or determine it’s unattainable
• Learning and adaptation capabilities: Through experience and feedback, AI agents refine their approach, improving performance over time and adjusting to new situations

Together, these traits enable AI agents to handle sophisticated, dynamic tasks across business operations.

Where did AI agents come from?

The evolution of AI agents mirrors the broader development of artificial intelligence itself. Early research in the 1950s and 1960s explored how computers could perform tasks without explicit instructions. By the 1990s, researchers developed “intelligent agents” that could perceive their environment and act toward defined goals.

The next major leap came in the 2010s with advances in machine learning and natural language processing. Companies such as OpenAI and DeepMind demonstrated that neural networks could handle increasingly complex reasoning tasks, paving the way for systems that understand and execute instructions.

Today’s large language models have made AI agents practical. They can plan multi-step tasks, use tools, and interact naturally with people. What began as an academic idea is now software that supports everything from customer service to financial operations.

How do AI agents work?

With modern AI technology, agents now follow a repeatable process that allows them to operate independently and improve continuously. AI agents work through a continuous cycle of:

• Observe: Take in data from their environment through APIs, databases, sensors, or user input
• Think: Analyze that data against their goals to decide the best course of action
• Act: Take the right action—sending a message, updating a system, or triggering another workflow
• Learn: Evaluate the outcome and adjust future behavior based on feedback and results

Machine learning powers the “thinking” step of this loop. It enables agents to recognize patterns, make predictions, and improve their performance without explicit programming for every scenario. Over time, they learn what works and refine their approach through experience.

Natural language processing allows agents to interpret unstructured text and interact with people in everyday language. They can read emails, answer customer questions, extract key information from documents, and respond in ways that feel conversational.

Examples of AI agent platforms include Microsoft Copilot, Salesforce Agentforce, Google Vertex AI Agent Builder and Gemini models, and OpenAI’s agentic tools within the GPT platform.

Core components of AI agents

These core components form the foundation of every AI agent, working together within the observe–think–act–learn cycle:

• Sensors (inputs): Gather information from the environment through APIs, data feeds, user interactions, or monitoring systems. This data provides the context the agent needs to make decisions.
• Processing engine (reasoning): Analyze incoming data, evaluate options against the agent’s goals, and apply machine learning models to determine the best next step
• Actuators (outputs): Carry out the agent’s decisions by sending messages, updating databases, triggering workflows, or interacting with other digital or physical systems

This input–process–output loop runs continuously, enabling AI agents to respond dynamically as conditions change.

Example AI agent workflow

Here’s how that process looks in action.

Imagine a growth marketer asks an AI agent to conduct a competitive analysis. Instead of prompting for data one step at a time, the agent:

1. Searches for top competitors in the category
2. Gathers public data such as pricing, features, and reviews
3. Compares sentiment across social platforms
4. Compiles findings into a slide deck
5. Asks whether the user wants to generate talking points or emails based on the results

This sequence shows how AI agents can plan, execute, and refine multi-step tasks with minimal human input, delivering results faster and freeing people to focus on higher-level strategy.

Types of AI agents

AI agents can be grouped into several categories based on how they process information and make decisions. Understanding these types helps clarify what each can and can’t do.

Simple reflex agents

Simple reflex agents respond to immediate conditions using predefined rules. They don’t consider history or future consequences, only the current situation. For example, if the temperature drops below a set point, turn on the heat; if a form field is empty, show an error message.

You’ll find these agents in thermostats, spam filters, and basic chatbots that match keywords to scripted responses. They’re fast and reliable for straightforward tasks where the right action depends solely on what’s happening in the moment.

Goal-based agents

Goal-based agents think ahead. They evaluate possible actions and choose the path most likely to achieve their objective. Instead of reacting only to present conditions, they plan several steps ahead and work backward from a desired outcome.

These agents power route-planning apps that find the fastest path home, scheduling assistants that coordinate multiple calendars, and inventory systems that reorder supplies before they run out. They’re useful when success requires planning or weighing trade-offs between competing priorities.

Learning agents

Learning agents improve performance through experience and feedback. They track which decisions lead to positive outcomes, adjust their behavior, and get better at their jobs over time without manual reprogramming.

Recommendation engines that learn user preferences, fraud detection systems that adapt to new scam tactics, and customer service bots that refine responses based on satisfaction ratings all fit this category. These agents become more valuable the longer they operate, building expertise that reflects your specific business context.

Multi-agent systems

Multi-agent systems deploy multiple agents that coordinate and share information to tackle complex problems. Each agent handles a specialized subtask, communicating through shared protocols or APIs to achieve outcomes no single agent could accomplish alone.

Supply chain management platforms use multiple agents to monitor inventory, forecast demand, coordinate logistics, and optimize pricing simultaneously. Trading systems deploy agents that analyze markets, execute transactions, and manage risk in parallel. This collaborative approach distributes workload, increases resilience, and enables sophisticated problem-solving at scale.

AI agents vs. traditional automation

While both AI agents and traditional automation reduce manual work, they take fundamentally different approaches to getting tasks done. Here's how they compare and when to use each:

Advantages of AI agents

AI agents excel in unpredictable environments where conditions change frequently. Traditional automation often breaks when faced with unexpected inputs or scenarios outside its programming, but agents can adapt and find workable solutions.

They also handle complexity that would overwhelm rule-based systems. Parsing unstructured data, such as emails, contract repositories, or customer feedback, requires contextual reasoning that traditional automation can’t provide. AI agents interpret meaning, detect nuance, and respond appropriately to real-world information.

Perhaps most valuable is their ability to improve over time. Traditional automation performs the same way on day one and day one thousand, while AI agents refine their approach through experience and deliver greater value the longer they operate.

When traditional automation still makes sense

Traditional automation remains best for predictable, repetitive processes with clear rules. If a task follows the same steps every time, like generating invoices from completed orders or backing up files nightly, simple automation is faster, cheaper, and easier to maintain.

Building and training AI agents requires more data and infrastructure investment. For routine processes with stable requirements, traditional automation offers faster ROI at a lower cost.

Regulated industries also favor traditional automation’s transparency and predictability. When compliance requires proving exactly how decisions are made, rule-based systems provide clear audit trails. AI agents’ decision-making can be more opaque, which poses challenges in those environments.

Real-world applications of AI agents

AI agents are already delivering measurable results across industries. According to McKinsey, companies investing in AI have achieved a revenue increase of 3% to 15% and a sales ROI boost of 10% to 20%.

AI agents in customer service

Virtual assistants now handle millions of customer interactions daily, resolving common issues without human intervention. They check order status, process returns, update account information, and troubleshoot basic problems while escalating complex cases to human support.

Research from Zendesk shows that AI agents can resolve up to 80% of routine customer requests independently, dramatically reducing response times and freeing human agents to focus on interactions requiring empathy or judgment.

Examples include Sephora’s chatbot, which helps customers find products by skin type and preference; H&M’s virtual assistant, which guides shoppers through outfit selection; and Bank of America’s Erica, which has handled over a billion customer requests.

AI agents in business operations

AI agents automate workflows that previously required manual coordination across departments. They route documents for approval, schedule meetings, generate reports, and notify teams when action is needed.

In procurement, agents monitor inventory levels, predict stockouts, and automatically place orders with preferred suppliers. In manufacturing, they schedule maintenance, optimize production runs, and adjust resources based on real-time demand and equipment data.

Agents also improve efficiency in the tech sector. NVIDIA’s ChipNeMo, a team of AI agents, helped 5,000 engineers in design, verification, and documentation save 4,000 engineering days in one year.

AI agents in finance and accounting

Expense management agents scan receipts, categorize transactions, flag policy violations, and route reimbursement requests for approval. They save finance teams hours of manual work while catching errors and duplicate submissions.

For instance, Ramp’s own AI agents catch 15 times more out-of-policy spend than non-AI systems and enforce policy with 99% accuracy. This reduces compliance risk and ensures consistent policy application across departments.

Fraud detection agents also analyze transaction patterns, identify suspicious activity, and block potential fraud before payments are processed. They learn typical behavior for each account and alert security teams when anomalies occur, adapting faster than rule-based systems.

Emerging applications

Healthcare providers are deploying agents that monitor patient data, predict complications, and recommend personalized treatment adjustments. Legal teams use agents to review contracts and suggest edits based on company standards.

Looking ahead, entire business processes may soon be orchestrated by autonomous agents, such as managing supply chains end to end, conducting competitive research, and even negotiating contracts with other AI systems. The technology continues to evolve rapidly, expanding from narrow use cases to broad enterprise applications.

Benefits and challenges of AI agents

AI agents can transform how teams work, driving measurable efficiency gains and cost savings across industries. According to the 2025 Stack Overflow Developer Survey, about 70% of developers using AI agents say they spend less time on repetitive tasks, and 69% report higher productivity. Still, implementing agents comes with real challenges that require thoughtful planning.

Key benefits for businesses

AI agents deliver tangible improvements across business operations, though the scale of impact varies by industry and use case:

• Efficiency and productivity gains: AI agents complete repetitive tasks faster than humans, freeing your team to focus on creative work, relationship building, and complex problem-solving that requires human judgment and expertise
• Cost reduction: By automating routine activities, agents lower labor costs for high-volume tasks while reducing errors that lead to expensive fixes, rework, or escalations
• Scalability: Agents handle larger workloads without proportional cost increases, processing thousands of requests simultaneously while maintaining quality and response times
• 24/7 availability: Agents work around the clock, providing instant responses to customers in any time zone and managing after-hours tasks that would otherwise wait until morning

These benefits compound over time as agents learn and expand their capabilities within your organization.

Common challenges and limitations

Implementing AI agents requires careful planning and realistic expectations:

• Implementation complexity: Deploying agents involves integrating them with existing systems, training models on relevant data, testing thoroughly, and managing organizational change
• Data quality requirements: AI agents need large volumes of clean, accurate, representative data to perform well. Businesses must invest in data collection, validation, and maintenance before seeing consistent results.
• Ethical and accuracy concerns: Agents can perpetuate bias, produce hallucinated or incorrect outputs, and make opaque decisions that conflict with company values if not carefully designed and monitored
• Need for human oversight: Even the most advanced agents require supervision to catch errors, handle exceptions, and intervene when automated decisions could negatively affect customers or the business

Success with AI agents depends on balancing capability with control—building the right data infrastructure, maintaining oversight, and treating automation as a partnership between humans and intelligent systems.

Getting started with AI agents

Implementing AI agents successfully starts with assessing readiness and approaching deployment methodically. Here’s how to prepare your organization for adoption.

Assessing your readiness

Before investing in AI agents, evaluate whether your organization has the foundation to support them. You can use this AI agent readiness checklist:

• Repetitive, high-volume tasks identified
• Accessible, high-quality data available for training
• IT infrastructure capable of supporting integrations and processing
• Internal expertise to implement, monitor, and maintain AI systems
• Executive sponsorship and cultural readiness for AI adoption

Beyond these prerequisites, secure leadership buy-in to ensure adequate budget and cross-functional support. You’ll also need API access to relevant systems, sufficient computing resources, and staff capable of managing the rollout.

Your data infrastructure is the most critical factor. Agents rely on clean historical data for training, real-time feeds for operations, and feedback loops for continuous improvement. If your data is fragmented or inconsistent, resolve those issues before deployment.

Implementation best practices

Following proven practices reduces risk and accelerates your path to results with AI agents:

• Start with one pilot project instead of an enterprise-wide rollout. Choose a process that’s painful but low-risk, with clear success metrics.
• Pick high-impact use cases where agents have a clear advantage—high transaction volumes, unstructured data, or 24/7 needs
• Avoid edge cases or processes that require complex human judgment early on
• Measure key outcomes from day one: time saved, error rates, cost per transaction, satisfaction, and adoption
• Set realistic expectations. Agents learn gradually, so early performance may lag before improving over time
• Review and iterate. Monitor metrics regularly and refine your approach based on results

A thoughtful, step-by-step rollout helps teams build confidence and momentum, setting the stage for broader AI adoption across the organization.

AI agent for finance automation

Ramp recently introduced its first AI agent to handle the routine, repetitive tasks that consume finance teams’ time each month. Take a $5 latte: uploading the receipt, reviewing the charge, and coding the expense in NetSuite can add up to 14 minutes and more than $20 in labor for a single transaction. Multiply that by thousands of expenses and the cost is significant.

By automating these small but frequent tasks, the AI agent frees teams to focus on higher-value work and decision-making.

Explore how Ramp’s AI agents fits into your finance processes and where it could remove the most friction. Learn more about Ramp Agents.