What are AI agents?

AI agents are autonomous systems that can plan, reason, and execute multi-step tasks with minimal human intervention. Unlike a standard chatbot that responds to one message at a time, an agent can take a goal, break it into steps, use tools, and keep working until the job is done.

A chatbot is reactive. You send a message, it sends a reply, and the conversation moves one turn at a time. The human drives every step.

An agent is proactive. You give it an objective like "find the cheapest flights to Tokyo next month and summarize the options," and it figures out what to do: search multiple sites, compare prices, handle errors, and compile a final report. The agent drives the process. You review the result.

The core difference is autonomy. Chatbots respond. Agents act.

Most agents follow a loop:

1. ▸[Perceive]: Take in information from the environment, which could be user instructions, tool outputs, or new data
2. ▸[Reason]: Decide what to do next based on the current state, the goal, and what has happened so far
3. ▸[Act]: Execute a step, such as calling a tool, writing code, or sending a request
4. ▸[Observe]: Review the result of that action and update internal state

This loop repeats until the agent decides the task is complete or it cannot make further progress. The reasoning step is what separates agents from simple automation scripts. The model is genuinely deciding what to do, not following a hardcoded sequence.

Tools are what give agents their power. Without tools, a language model can only generate text. With tools, it can search the web, query databases, run code, send emails, create files, and interact with APIs.

[Function calling] is the mechanism that makes this work. The model receives a list of available tools with their descriptions and parameters. When it decides a tool is needed, it outputs a structured function call. The system executes that function and returns the result to the model, which then continues reasoning.

Every major provider supports this pattern. OpenAI calls it function calling and has built it deeply into their Agents SDK. Anthropic provides tool use as a core feature of the Claude API. Google's Gemini supports function declarations. Even open-source models like Llama and Mistral support tool use through standardized formats.

Effective agents need memory. There are several types:

• ▸[Short-term memory]: The conversation context and recent tool results. This lives in the model's context window.
• ▸[Working memory]: Summaries or notes the agent creates for itself during a task. Think of a scratchpad.
• ▸[Long-term memory]: Information persisted across sessions, often stored in a vector database or key-value store. This lets agents remember user preferences, past interactions, and learned facts.

State management matters because context windows are finite. A coding agent working through a large codebase cannot hold every file in context simultaneously. It needs strategies for what to remember, what to look up, and what to summarize.

Several frameworks have emerged to simplify agent development:

[OpenAI Agents SDK] provides a Python-first framework with built-in support for tool use, handoffs between agents, and guardrails. It is tightly integrated with OpenAI's models but designed with open interfaces.

[LangChain and LangGraph] offer a flexible toolkit for building agents with any model provider. LangGraph specifically focuses on stateful, multi-step agent workflows with explicit graph-based control flow.

[AutoGen] from Microsoft supports multi-agent conversations where multiple AI agents collaborate with each other and with humans to solve problems.

[CrewAI] takes a role-based approach where you define agents with specific roles, goals, and backstories, then let them collaborate on tasks.

[Anthropic's tool use] is not a framework per se, but Claude's strong instruction-following and tool use capabilities make it straightforward to build agents directly on the API without heavy framework overhead.

A [single agent] handles everything itself. It reasons, calls tools, and produces the final output. This is simpler to build and debug, and it works well for many tasks.

[Multi-agent systems] use multiple specialized agents that collaborate. One agent might handle research, another handles writing, and a third handles fact-checking. They pass information between each other and may have a supervisor agent coordinating the work.

Multi-agent architectures shine when tasks are complex enough to benefit from specialization, but they add complexity in communication, error handling, and coordination. Start with a single agent and move to multi-agent only when you have a clear reason.

[Coding agents] like those in Cursor, GitHub Copilot, and Claude Code can read codebases, plan changes across multiple files, run tests, and iterate until the code works. They combine code generation with file system access, terminal commands, and search tools.

[Research agents] can take a complex question, break it into sub-queries, search multiple sources, cross-reference findings, and produce a synthesized report. They often use web search, document retrieval, and citation tools.

[Customer service agents] can handle support tickets by looking up order information, checking knowledge bases, applying policies, and escalating to humans when necessary. They integrate with CRM systems, order databases, and communication tools.

Giving AI systems the ability to act autonomously introduces real risks:

• ▸[Unintended actions]: An agent might delete files, send emails, or make purchases that the user did not intend. Confirmation steps and sandboxing are essential.
• ▸[Runaway loops]: Without proper stopping conditions, agents can get stuck in loops, wasting time and money on API calls.
• ▸[Security]: Agents with tool access can be exploited through prompt injection. If an agent reads untrusted content that contains instructions, it might follow those instructions instead of the user's.
• ▸[Reliability]: Agents can make mistakes at any step, and those mistakes compound. Error handling and human oversight matter.

Best practices include limiting tool permissions, adding human approval for high-stakes actions, setting budget and iteration limits, and logging every step for review.

Agents represent a fundamental shift in how we use AI. We are moving from AI as a tool you operate to AI as a collaborator that operates tools on your behalf. As models get better at reasoning and tool use, agents will become more capable and more common. The companies and developers who learn to build reliable, safe agents now will have a significant advantage as this technology matures.