What Are AI Agents? How They Work and Why They Matter

Most AI tools wait for your prompt to execute a workflow. For example, you paste in a document, ask for a summary, and get a response.  

AI agents work differently. They operate autonomously using pre-defined workflows, without waiting for your instructions.

This guide explains how AI agents work, how they differ from assistants and bots, and how organizations are using them today.

TL;DR

• AI agents are autonomous systems that perceive environments, make decisions, and take actions toward goals without constant human prompting.
• Unlike chatbots (scripted responses) or AI assistants (require prompting), agents work proactively and adapt based on context.
• Agents operate through a continuous perception-reasoning-action loop, learning from outcomes to improve over time.
• Organizations deploy agents to support use cases like sales enablement, operations monitoring, knowledge management, and individual productivity.

What Is an AI Agent?

An AI agent is software that autonomously pursues goals by perceiving its environment, making decisions, and taking actions without requiring a human to initiate each step. Unlike traditional software that executes fixed commands, AI agents operate through continuous feedback loops.

At the core of most AI agents is a workflow engine that orchestrates this cycle. When you deploy an agent, you give it an objective and the permissions to pursue it across connected systems. The agent determines the steps required, executes them, and adapts when circumstances change.

While a chatbot answers when you ask, an AI agent notices what needs attention and handles it before you think to ask. "Agent" signals a different relationship with AI, one where you're delegating to a system that works on your behalf.

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How do AI agents work?

AI agents operate through a continuous loop that runs autonomously once deployed. Each stage of the loop serves a distinct purpose.

• Perception: Agents gather data from their environment and monitor it continuously.
• Reasoning: They interpret that data against defined objectives. The agent determines what matters based on your goals.
• Action: AI agents execute tasks or surface recommendations based on what they've learned. For example, they might draft status memos, update CRM fields, and flag priorities.
• Learning: They adjust future behavior based on outcomes and feedback. The more you use them, the better they understand what you actually need.

What makes AI agents different from automation

Traditional automation follows rigid rules. If X happens, do Y. Agents adapt to changing context instead, recognizing patterns, weighing priorities, and adjusting their actions when circumstances shift.

Consider an agent monitoring your calendar and meeting history. It notices you have a packed Monday and sends you a prioritized briefing before your day starts, highlighting which meetings matter most and what context you need for each. You didn't ask for it, but the agent recognized the pattern and acted.

Most modern agents combine large language models (LLMs) with retrieval-augmented generation (RAG) to ground their actions in real organizational data. RAG is a technique that pulls relevant information from your connected systems before generating a response, so the agent works with your actual data rather than just general knowledge.

What Is the Difference Between AI Agents, AI Assistants, and Bots?

These terms get used interchangeably, but they describe different capabilities:

• Bots follow scripts. They handle simple, repetitive queries like password resets and FAQ lookups through rule-based responses. A chatbot that routes support tickets based on keywords is a bot. 
• AI assistants respond to prompts. They use LLMs to generate conversational responses, but require you to initiate every interaction. When you ask ChatGPT to draft an email or explain a concept, you're using an assistant.
• AI agents act autonomously toward goals. They monitor your environment continuously, take action based on patterns and priorities, and learn from behavior over time.

Where the lines blur

Some platforms combine multiple approaches. For example, Read AI’s Search Copilot offers conversational search for when you need to ask questions across your meetings and documents, alongside behavior-based agents like Monday Briefing, End of Week, and Recommendations that surface briefings and next steps without prompting. You get answers when you ask and proactive nudges when something needs attention.

The distinction is especially important when evaluating tools. A reactive assistant and a proactive agent have different implementation complexity, data access requirements, and governance needs. Clarifying which you're buying saves time during evaluation and deployment.

What Are the Types of AI Agents?

AI agents fall into five main categories based on their scope and function. Most organizations start with task-specific agents before expanding:

• Task-specific agents handle narrow functions like scheduling, CRM updates, meeting summarization, and weekly briefings. These agents excel within defined boundaries and are easiest to deploy and govern. 
• Workflow agents orchestrate multi-step processes across systems. They pull data from one platform, analyze it, and push results to another, connecting tools that don't natively talk to each other.
• Retrieval agents search and synthesize information from large knowledge bases, surfacing answers when you search and when relevant information changes.
• Coaching agents analyze behavior patterns like meeting participation, communication style, and response times, then suggest improvements. They’re common in sales enablement and professional development, and are also present in select AI notetakers like Read AI.
• Orchestration agents coordinate other agents. As organizations deploy multiple specialized agents, orchestration layers manage handoffs, prioritization, and conflict resolution between them. Read AI's Operator is an example of this approach, capturing conversations across platforms and coordinating workflows between specialized agents.

Agent architecture affects oversight requirements. Single-purpose agents are easier to audit and control, while multi-agent systems require clearer frameworks for accountability and data access. Starting with task-specific agents lets teams build governance practices before scaling complexity.

What Are the Benefits of Using AI Agents?

AI agents deliver compounding productivity gains as they learn your organization's patterns:

• Time reclaimed from busywork: Administrative overhead like status updates, meeting prep, and CRM data entry eats into productive hours. AI Agents handle these tasks automatically, returning hours per week to higher-value work.
• Consistency across workflows: Agents apply the same logic every time. CRM data gets updated after every call, action items get tracked automatically, and nothing slips through the cracks because someone forgot to log it.
• Insurance of intelligence: When agents capture and synthesize interactions across an organization, that knowledge stays accessible and surfaces proactively even when employees leave.
• Reduced cognitive load: With agents handling monitoring and reminders, people focus on work that requires human judgment instead of trying to remember everything themselves. For example, Read AI agents like Monday Briefing gather data from meetings, emails, messages, and documents to provide a personalized summary of key priorities at the start of every week.

Agents can also reduce shadow IT risk by providing sanctioned, governed alternatives to the ad-hoc tools employees adopt when official systems don't help them find answers fast enough.

How Are Businesses Using AI Agents Today?

Organizations are deploying AI agents across teams, including:

• Sales teams use agents that surface deal insights, recommend next steps based on interaction history, and provide real-time coaching to improve communication during calls. Pipelines stay accurate without manual data entry, and reps get feedback that helps them close more deals.
• Operations relies on agents that monitor project status across tools, flag blockers, and synthesize updates for stakeholders automatically.
• Knowledge management benefits from agents that answer questions by searching across meetings, documents, and messages, then alert users when relevant information changes.
• Individual productivity improves with agents that track action items across interactions and suggest calendar optimizations based on workload patterns.

Get Started with Read AI

AI agents handle the busywork that fragments your day: capturing meeting context, tracking action items, and surfacing what needs attention. Read AI executes this workflow across the platforms you already use.

Read AI connects fragmented information into a unified knowledge graph across meetings, emails, messages, and software platforms, working with tools like Zoom, Google Meet, Microsoft Teams, and Slack.

Ready to stop chasing information? Try Read AI for free today and see how much time your team saves when context surfaces exactly when you need it.

FAQs

What is an AI agent?

An AI agent is software that autonomously pursues goals by perceiving its environment, making decisions, and taking actions without requiring constant human direction. They work proactively to monitor data sources, identify patterns, and execute tasks based on defined objectives.

How do AI agents differ from traditional automation?

Traditional automation follows rigid if-then rules and breaks when it encounters unexpected scenarios. AI agents adapt to changing contexts, recognize patterns, weigh priorities, and adjust their actions based on circumstances. Agents learn from outcomes and improve over time, while automation executes the same steps repeatedly.

What's the difference between AI agents and AI assistants?

AI assistants respond to user prompts and require human initiation for every interaction. AI agents operate autonomously, monitoring environments continuously and taking action proactively without prompting. Assistants augment human decision-making in a co-pilot model, while agents work independently toward defined goals in an auto-pilot model.

What types of AI agents exist?

AI agents fall into five main categories: task-specific agents (scheduling, CRM updates, meeting summarization), workflow agents (multi-step process orchestration), retrieval agents (enterprise search and synthesis), coaching agents (behavior analysis and improvement suggestions), and orchestration agents (coordinating multiple agents).

How are businesses using AI agents today?

Organizations deploy AI agents for sales enablement, operations monitoring, knowledge management, and individual productivity. 

What are the main benefits of using AI agents?

AI agents reclaim hours per week from administrative tasks and surface relevant context before you need it. They apply consistent logic across workflows, preserve institutional knowledge when employees leave, and reduce cognitive load by handling monitoring and reminders. This lets employees focus on work requiring human judgment instead of trying to remember everything themselves.

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