AI agents that can plan, reason, and take actions autonomously.
AI agents are systems that use LLMs as their "brain" to perceive their environment, plan actions, execute them using tools, and iterate based on results. Unlike simple chatbots that respond to one message at a time, agents can pursue multi-step goals autonomously β browsing the web, writing code, managing files, and calling APIs.
The agent paradigm is where AI moves from tool to collaborator. Agents like Claude Code, Devin, and OpenAI's operator can complete complex tasks that would take humans hours. The key challenge is reliability β agents work best when given clear goals, appropriate tools, and guardrails to prevent harmful actions.
What Is an AI Agent
A system with an LLM core that perceives, plans, acts, and iterates. The perception-planning-action loop runs until the goal is achieved or a stopping condition is met.
Agent Architectures
ReAct (reason then act), Plan-and-Execute (create plan first, then execute steps), and Reflexion (self-critique and retry). Each architecture trades off between speed and reliability.
Multi-Agent Systems
Multiple agents collaborating on a task. CrewAI assigns roles (researcher, writer, reviewer), AutoGen enables agent conversations, MetaGPT simulates software teams. Powerful but complex.
Agent Memory
Short-term (current conversation), long-term (persisted knowledge), and episodic (memories of past tasks). Effective memory management is what makes agents improve over time.
Tool Use in Agents
Agents extend their capabilities through function calling β web search, code execution, file I/O, API calls. The quality and breadth of available tools directly determines what an agent can accomplish.
Coding Agents
Claude Code, Cursor, Devin, GitHub Copilot Workspace β agents that can read codebases, write code, run tests, and iterate on bugs autonomously. The fastest-growing agent category.
Browser & Computer Agents
Agents that can control a browser or desktop β clicking, typing, navigating. Anthropic's computer use, OpenAI operator, and browser-use frameworks enable real-world task automation.
Agent Evaluation
Measuring agent performance is hard β tasks are open-ended and multi-step. Benchmarks like SWE-bench (code), WebArena (browser), and GAIA (general) attempt to standardize evaluation.
Agent Safety
Agents can take irreversible actions (delete files, send emails, modify databases). Sandboxing, confirmation gates, and principle-of-least-privilege tool access are critical safety measures.
The Agentic Future
Agents are evolving from single-purpose to general-purpose. The trajectory: task-specific bots β coding agents β computer-using agents β fully autonomous assistants. We are early in this progression.
AI AgentAn LLM-powered system that autonomously perceives, plans, acts, and iterates to achieve goals.
ReActAgent architecture alternating between reasoning about what to do and taking actions with tools.
Multi-Agent SystemMultiple AI agents with different roles collaborating to solve complex tasks together.
Tool CallingThe mechanism by which agents invoke external functions, APIs, or services to extend their capabilities.