Tool use, orchestration frameworks, reliability research and the agent deployments that actually ship.
AI agents are systems that plan and act over multiple steps to complete a goal, calling tools and APIs and reacting to results along the way — the layer above a single model response. This section follows the orchestration frameworks (LangGraph, AutoGen, CrewAI), the protocols standardizing tool use (MCP, function calling), the reliability research and the real enterprise deployments that separate agent demos from agent products.
An LLM predicts the next token; an AI agent wraps that model in planning, memory, and tools so it can take actions on its own. Here is the real difference in 2026, with examples and a side-by-side comparison.
The company unifies ChatGPT, Codex and browser features into one desktop platform while shifting focus to agentic capabilities amid competition from Anthropic.
An AI agent is a system that uses a language model to plan and take multiple actions toward a goal — calling tools, reading results and adjusting — rather than producing a single response.
Reliability varies widely by task. This hub tracks the eval results and guardrail patterns teams use to put agents into production safely.
We cover the orchestration frameworks and protocols with real adoption, citing the primary source for each capability claim.