AI agents are autonomous loops where a language model decides which tool or function to call next, runs it, observes the result, and either continues or returns. The "agent" is the loop itself; the LLM is just the decision step. The category exploded in 2024-2025 as model providers added reliable tool-calling APIs and the orchestration libraries (LangGraph, AutoGen, OpenAI Agents SDK, CrewAI) matured.
Building a real agent that does useful work for more than five turns is harder than the demos suggest. The hard parts are state management between steps, recovering from tool errors, deciding when to give up, keeping the model from hallucinating tool arguments, and limiting cost when the loop runs too long.
What you'll work with in these 22 courses
- Tool-calling APIs: OpenAI function calling, Anthropic tool use, Gemini function calling
- Orchestration frameworks: LangGraph, OpenAI Agents SDK, CrewAI, AutoGen
- Multi-agent patterns: planner / executor, debate, hierarchical teams
- Memory: short-term context windows, vector stores, structured scratchpads
- Evaluation: trace inspection, automated graders, human-in-the-loop review
- Cost and latency control: caching, model routing, early termination
