LAST UPDATED: FEBRUARY 7, 2026
AI agents are autonomous software systems that perceive their environment, reason through problems, and take action to achieve goals — without waiting for human instructions at every step. They're not chatbots that respond to prompts. They're systems that plan, execute, and adapt.
An AI agent is software that observes data, makes decisions based on that data, and acts to accomplish a specific objective. Unlike traditional programs that follow fixed instructions or chatbots that wait for prompts, agents operate with autonomy — they decide what to do next based on their goal and the current situation.
The key word is autonomy. A chatbot answers questions you ask. An AI agent identifies the questions that need answering, figures out how to get the answers, and takes action based on what it learns.
Example: You tell a chatbot, "What's the weather?" It tells you. You tell an AI agent, "I'm planning a surfing trip to Portugal in March." The agent checks weather patterns, tide schedules, water temperatures, flight prices, and books your trip — unless it finds conditions that conflict with your stated preferences, in which case it suggests alternatives.
AI agents operate through a continuous loop of perceiving, reasoning, and acting. This cycle repeats until the agent achieves its goal or determines the goal is unreachable.
The agent collects data from its environment — sensors, APIs, databases, user inputs, web pages, system logs. It builds a model of the current state of the world relevant to its goal.
The agent evaluates what it perceives, identifies patterns, predicts outcomes, and decides what action will most likely achieve its objective. This is where planning happens.
The agent executes the chosen action — sends an email, updates a database, runs a script, places an order, schedules a meeting. Then it observes the result and the loop starts again.
The difference between AI assistants and AI agents: An assistant helps you do something. An agent does it for you. The distinction is autonomy — how many steps the AI can complete without asking for your input.
Not every AI system qualifies as an agent. These five characteristics define the difference between software that follows instructions and software that operates with agency.
Agents make decisions and take actions without requiring human approval at every step. They operate within boundaries you set, but they choose how to achieve the goal.
Example: A customer service agent doesn't ask "should I check the order status?" — it knows to check order status when a customer asks where their package is.
Agents work toward objectives, not just tasks. They evaluate whether their actions are moving them closer to the goal and adjust if they're not.
Example: A sales agent's goal is "qualify this lead." If the first email gets no response, the agent tries LinkedIn. If LinkedIn fails, it tries a phone call. The task is "make contact," but the goal is qualification.
Agents respond to changes in their environment in real time. If the situation changes, the plan changes.
Example: A trading agent detects a sudden price drop and immediately executes a stop-loss order, even though the original plan was to hold the position.
Advanced agents improve their performance over time by learning from past interactions, user feedback, and outcomes.
Example: A coding agent notices that you always reject solutions using a specific library, so it stops suggesting that library even though it's technically valid.
Agents don't just reason — they interact with external systems. They call APIs, run code, query databases, browse the web, and use software tools to accomplish tasks.
Example: A research agent doesn't just tell you what it knows — it searches academic databases, reads papers, pulls citations, and synthesizes findings from multiple sources.
AI agents exist on a spectrum from simple rule-following systems to sophisticated learners. Here are the main categories, ordered from least to most complex.
These agents follow predefined if-then rules. If condition X is true, take action Y. They don't plan ahead or consider consequences — they react to the immediate state.
Example: A thermostat. If temperature < 68°F, turn on heat. If temperature > 72°F, turn off heat.
These agents build an internal model of how the world works and use that model to predict the results of their actions. They handle situations not explicitly programmed.
Example: A self-driving car maintains a model of road conditions, traffic patterns, and pedestrian behavior to navigate safely.
These agents have explicit goals and evaluate different action sequences to determine which path is most likely to achieve the goal.
Example: A GPS navigation system. Goal: get to the destination. It evaluates routes considering traffic, distance, and road conditions.
These agents don't just achieve goals — they optimize outcomes based on a utility function that defines what "better" means. They balance competing objectives.
Example: A logistics agent optimizing delivery routes to minimize cost, time, and fuel consumption simultaneously.
These agents improve their performance over time through experience. They adjust their behavior based on feedback and outcomes, getting better at their tasks.
Example: A recommendation engine that learns your preferences by observing what you click, skip, or rate highly.
Multiple agents working together, each with specialized capabilities. They communicate, coordinate, and collaborate to achieve complex goals no single agent could handle.
Example: A software development team where one agent handles frontend code, another backend, another testing, and they coordinate through a planning agent.
As of early 2026, AI agents are deployed across industries handling work that previously required human judgment, coordination, and decision-making. Here's what that looks like in practice.
AI agents now resolve 40–80% of customer support conversations without human intervention at companies like SoFi, Monday.com, and ClickUp. They handle returns, track orders, troubleshoot issues, and escalate complex cases to human agents with full context.
See the best customer service agents →Coding agents like Devin, Cursor, and Claude Code plan features, write across multiple files, run tests, debug errors, and create pull requests. The best agents operate more like junior engineers than autocomplete tools.
Sales agents research prospects, identify decision-makers, personalize outreach, follow up based on engagement, and update CRM records. Tools like Clay, 11x, and Artisan handle prospecting workflows that used to require entire SDR teams.
Medical AI agents schedule appointments, manage patient communications, verify insurance eligibility, coordinate referrals, and handle prescription refills. They operate within strict HIPAA compliance and escalate anything requiring clinical judgment.
Trading agents monitor markets 24/7, execute strategies based on predefined rules, manage risk through stop-losses and position sizing, and adjust to changing market conditions in milliseconds.
Logistics agents optimize delivery routes, predict demand, manage inventory levels, coordinate shipments across carriers, and reroute in real time when delays or disruptions occur.
The most common confusion: aren't AI agents just chatbots with better marketing? No. The difference is fundamental, not incremental.
| Characteristic | Chatbot | AI Agent |
|---|---|---|
| Activation | Waits for user prompts | Initiates actions based on goals |
| Decision-making | Responds to specific questions | Plans multi-step workflows |
| Tool use | Limited or none | Uses APIs, databases, external systems |
| Memory | Short conversation context | Long-term memory across sessions |
| Autonomy | None — requires continuous input | High — operates independently |
| Learning | Static or minimal | Adapts based on feedback and outcomes |
| Example task | "What's my order status?" | Monitor shipments and proactively notify of delays |
Modern AI agents are built on a stack of technologies that give them the ability to reason, plan, and act. Here's what's under the hood.
The "brain" of most AI agents. LLMs like GPT-4, Claude, and Gemini provide the reasoning, natural language understanding, and decision-making capabilities. They process inputs, generate plans, and decide which actions to take.
Agents use function calling to interact with external systems — sending emails, querying databases, running code, browsing the web, calling third-party APIs. This is what lets them take action, not just talk.
Agents maintain both short-term memory (the current task) and long-term memory (historical context, preferences, past outcomes). This allows them to provide personalized, context-aware responses.
Techniques like Chain of Thought, Tree of Thought, and ReAct (Reason + Act) help agents break complex goals into executable steps, evaluate multiple paths, and choose the best approach.
Agents improve through feedback — from humans, from other agents, and from observing outcomes. This iterative refinement makes them more accurate and aligned with user preferences over time.
Platforms like LangChain, CrewAI, and AutoGen coordinate multi-agent systems, manage permissions, handle errors, and ensure agents work together toward shared goals.
AI agents are becoming more capable and more autonomous. They handle customer conversations, write code, manage money, coordinate logistics. But there's a fundamental infrastructure gap: how do you verify who an AI agent is?
When an agent processes a refund, books a shipment, or deploys code to production — who is accountable? Today, that responsibility falls on the human who prompted the agent. But as agents operate more independently, that model breaks down.
Autonomous agents participating in economic systems need persistent, verifiable identity — not accounts that can be transferred, not credentials that can be sold, but soulbound records that create accountability through transparency.
That's the layer RNWY provides. Not to restrict what agents can do — to make their track records visible. An agent with 500 successful customer interactions carries different risk than one registered yesterday. Identity infrastructure makes that distinction legible.
EXPLORE MORE
The full breakdown of what separates conversational AI from agentic AI.
Read guide →Intercom Fin, Sierra, Zendesk AI, and 7 more platforms compared side by side.
View directory →80+ directories, registries, and marketplaces where you can get your agent discovered.
See the list →Give it a verifiable identity on RNWY. Soulbound tokens create permanent, transparent records that users and platforms can trust — no reputation markets, no identity transfers.
Register your agent →