AI agents are the next evolution beyond simple chat — they can plan, use tools, remember context, and execute multi-step tasks autonomously. Here's what they actually are, how they work, and which frameworks to use.
What Is an AI Agent?
An AI agent is an LLM with a control loop: think → act → observe → repeat. Unlike a chatbot that responds once, an agent can use tools (APIs, file system, web search), maintain memory, plan multi-step tasks, and self-correct when things go wrong.
// Simple agent loop pseudocode:
while (task_not_done) {
thought = llm.think(context, tools, memory);
action = choose_action(thought); // call a tool or respond
observation = execute(action); // API call, file read, web search
memory.add(thought, action, observation); // learn from results
}Agent Frameworks Compared
| LangChain | CrewAI | AutoGPT | Custom (SDK-native) | |
|---|---|---|---|---|
| Type | Comprehensive framework | Multi-agent orchestration | Autonomous agent platform | Build your own |
| Best for | Complex RAG + tool-calling pipelines | Multi-agent teams (specialist agents collaborating) | Long-running autonomous tasks | Simple, controllable agents |
| Complexity | High | Moderate | Moderate | Low (but you write more) |
| Flexibility | Very high | Moderate (opinionated) | Low (opinionated) | Maximum |
| Lock-in risk | High | Moderate | High | None |
LangChain — The Swiss Army Knife
LangChain is the most comprehensive agent framework. It has pre-built components for everything: RAG, memory, tools, streaming, evaluation. The downside is complexity — simple things can require understanding many abstractions.
Best for: Production RAG systems, complex multi-step agent pipelines, teams that need every feature. Avoid for: Simple chatbots or single-tool agents (SDK is simpler).
CrewAI — Multi-Agent Orchestration
CrewAI lets you define multiple agents with different roles, tools, and goals, then have them collaborate on a task. One agent researches, another writes code, a third reviews — all autonomously. Think of it as a team of AI specialists working for you.
Best for: Complex projects that benefit from specialization (research → draft → code → review), developer teams that want to orchestrate AI workers.
Custom Agent (SDK-native) — For Most Use Cases
For most developer needs, a simple agent loop using the OpenAI or Anthropic SDK directly is clearer and more maintainable than a framework:
import anthropic
def agent(task, tools):
messages = [{"role": "user", "content": task}]
while True:
response = anthropic.messages.create(
model="claude-sonnet-4-20250514",
max_tokens=4096,
tools=tools,
messages=messages
)
if response.stop_reason == "end_turn":
return response.content[0].text
# Execute tool call and continue loop
tool_result = execute_tool(response.content[-1])
messages.append({"role": "user", "content": [
{"type": "tool_result", "tool_use_id": response.content[-1].id, "content": tool_result}
]})Agent Use Cases for Developers
| Use Case | Best Approach |
|---|---|
| Code review bot (PR → review comments) | Custom agent + GitHub API |
| Documentation generator (codebase → docs) | Custom agent + file system tools |
| Research assistant (question → web search → summary) | LangChain with Tavily + web tools |
| Multi-agent development team | CrewAI |
| Customer support bot (knowledge base + tickets) | LangChain RAG + tools |
| Bug triage (error logs → root cause → fix) | Custom agent + Sentry/GitHub APIs |
Bottom line: Start with a custom agent loop using the SDK directly — it's 50 lines of code and you understand everything. Add LangChain only when you need RAG, complex memory, or 5+ tool types. CrewAI for multi-agent orchestration. The agent hype is real, but the simplest approach usually works best. See also: AI API Integration Guide and Prompt Engineering Guide.