What are AI agents? Definition and how they’re used
What are AI agents?
AI agents are autonomous software systems powered by artificial intelligence that can independently plan and execute tasks on behalf of a user. Unlike standard AI tools that respond passively to single prompts, AI agents act proactively, breaking down goals, making decisions, using tools or APIs, and performing multi-step tasks with minimal human oversight.
AI agents represent a major leap beyond prompt-based interfaces. They combine reasoning, memory, planning, and action-taking—allowing them to accomplish complex objectives such as booking travel, analyzing competitors, building dashboards, or coordinating workflows across apps.
Where did AI agents come from?
The concept of AI agents originated in academic fields like artificial intelligence, robotics, and multi-agent systems, where researchers studied autonomous software capable of goal-directed behavior. While the ideas were explored for decades, practical adoption remained limited until 2023–2024, when generative AI tools like ChatGPT, Claude, and Gemini began enabling agents to reason, act, and iterate in natural language.
These technologies allowed LLMs to not just respond—but to choose actions, call APIs, retrieve documents, monitor progress, and revise strategies. Companies like OpenAI, Anthropic, Adept, and emerging startups accelerated the development of agentic frameworks built on these capabilities.
The term “AI agents” became mainstream as the focus shifted from passive chatbots to AI systems that could carry out multi-step workflows on their own.
How do AI agents work, and how are they used today?
An AI agent starts with a user-defined goal (for example, "summarize all recent customer feedback from email, support tickets, and reviews"). From there, it decides how to approach the task, breaks it down into subtasks, chooses tools or services to interact with, and completes the task iteratively—often reporting back with updates or asking clarifying questions.
Core components of modern AI agents include:
- Natural language understanding: Interprets user intent from plain language
- Planning modules: Breaks large goals into executable steps
- Tool use / API calling: Uses third-party apps or custom tools to get things done
- Memory and context tracking: Remembers prior actions and information
- Feedback loops: Evaluates results and adjusts course if needed
Many agents are deployed using orchestration frameworks or enterprise platforms that combine LLMs with external tools and databases.
Example use case
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:
- Searches for top competitors in the category
- Gathers public data (pricing, features, reviews)
- Compares sentiment across social platforms
- Compiles findings into a slide deck
- Asks the user if they'd like to generate talking points or emails based on the results
All of this is completed with minimal human intervention after the initial request.
Why do AI agents matter?
AI agents represent a major shift in how work gets done—especially for knowledge workers, operations teams, analysts, and marketers. By assigning repetitive or time-consuming workflows to agents, teams free up time for higher-level strategy and creativity.
The impact of AI agents includes:
- Automation of complex workflows, not just simple queries
- Faster decision-making, thanks to autonomous data gathering and synthesis
- Scalable task delegation, without linearly increasing headcount
- Real-time experimentation, where agents test approaches and iterate in the background
More than just tools, AI agents are capable of handling end-to-end tasks that previously required multiple apps and human coordination.
Examples:
- Marketing teams use agents to monitor brand mentions, create content variations, and A/B test messages
- Product teams deploy agents to gather user feedback, cluster it into themes, and generate draft specs
- Support teams use agents to triage tickets, draft replies, and escalate only when needed
As companies build internal agent platforms or adopt agent-ready tooling, the nature of work is transforming from task execution to task orchestration—with AI agents taking the lead on the former.
TL;DR
AI agents are autonomous systems that plan and complete tasks on your behalf. They use AI to understand your goals, decide on next steps, and execute multi-step actions across tools and data sources—with little hand-holding.
For growth-minded teams, AI agents are already unlocking new levels of productivity, insight, and operational speed. As the underlying tech improves, agents will increasingly shape how work gets done across industries.
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.
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