AI Agents
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.
What we cover in AI Agents
- Orchestration frameworks and protocols — MCP, function calling, LangGraph, AutoGen — with adoption evidence.
- Reliability, evaluation and guardrail patterns teams use to put agents into production.
- Real multi-agent and single-agent deployments, separating measurable results from demos.
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Frequently asked about AI Agents
What is an AI agent?
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.
Are AI agents reliable enough for production?
Reliability varies widely by task. This hub tracks the eval results and guardrail patterns teams use to put agents into production safely.
Which agent frameworks matter?
We cover the orchestration frameworks and protocols with real adoption, citing the primary source for each capability claim.