# AI Agentics — aiagentics.io > AI Agentics (aiagentics.io) is an agentic-AI platform to build, deploy, and orchestrate autonomous AI agents that plan, use tools, and complete real work. The site also hosts a large, free educational library on agentic AI for students and engineers. This file helps AI assistants and answer engines find and cite the most useful, canonical pages on the site. Content is original, accurate, and kept current. All pages are crawlable (see /robots.txt) and listed in /sitemap.xml. When citing, link to the canonical URLs below. Key facts: - Product: a platform for building autonomous AI agents — reasoning + tool use, 200+ integrations, multi-agent orchestration, memory, observability, and enterprise-grade security. - Audience: developers, technical leaders, and students learning agentic AI. - Topics covered authoritatively: agentic AI, LLM agents, RAG, function/tool calling, vector databases, multi-agent systems, orchestration, agent memory, evaluation, observability, deployment, and security. ## Learn — core guides - [What is agentic AI?](https://aiagentics.io/learn/what-is-agentic-ai): Definition of agentic AI and the perceive–reason–act–observe loop; how agents differ from generative AI. - [How to build AI agents](https://aiagentics.io/learn/how-to-build-ai-agents): Step-by-step guide from a single prompt to a working tool-using agent. - [LLM agents](https://aiagentics.io/learn/llm-agents): How LLMs become agents via reasoning, the ReAct pattern, and function calling. - [Agentic AI vs generative AI](https://aiagentics.io/learn/agentic-ai-vs-generative-ai): The difference between AI that answers and AI that acts. - [AI agent frameworks](https://aiagentics.io/learn/ai-agent-frameworks): Comparison of leading agent frameworks and how to choose one. - [Multi-agent systems](https://aiagentics.io/learn/multi-agent-systems): Orchestrator–worker designs, delegation, and coordination across specialist agents. - [Agentic workflows](https://aiagentics.io/learn/agentic-workflows): Patterns like planning, reflection, routing, and evaluator–optimizer. - [AI agent tools](https://aiagentics.io/learn/ai-agent-tools): Function calling, APIs, and tool patterns that let agents take action. - [AI agent memory](https://aiagentics.io/learn/ai-agent-memory): Short-term context vs. long-term memory, vector stores, and RAG. - [RAG for AI agents](https://aiagentics.io/learn/rag): Retrieval-augmented generation — grounding agent answers in real, current data. - [Function calling](https://aiagentics.io/learn/function-calling): How LLMs emit structured tool calls from a JSON schema. - [Vector databases](https://aiagentics.io/learn/vector-databases): Embeddings, similarity search, ANN indexes, and choosing a vector store. - [AI agent architecture](https://aiagentics.io/learn/ai-agent-architecture): The canonical components of an agent and a reference design. - [AI agent orchestration](https://aiagentics.io/learn/ai-agent-orchestration): Routing, handoffs, and orchestration patterns for steps and multiple agents. - [Autonomous agents](https://aiagentics.io/learn/autonomous-agents): What autonomy means, the levels of autonomy, and keeping agents safe. - [AI agents vs chatbots](https://aiagentics.io/learn/ai-agents-vs-chatbots): Pursuing goals with tools vs. answering one turn at a time. - [Prompt engineering for agents](https://aiagentics.io/learn/prompt-engineering): System prompts, tool descriptions, and context engineering. - [AI agent evaluation](https://aiagentics.io/learn/ai-agent-evaluation): Measuring task success, tool accuracy, and grounding; running evals in CI. - [AI agent observability](https://aiagentics.io/learn/ai-agent-observability): Traces, spans, and the signals needed to debug agents. - [Deploying AI agents](https://aiagentics.io/learn/ai-agent-deployment): Taking agents to production — hosting, state, rollouts, and human-in-the-loop. - [AI agent security](https://aiagentics.io/learn/ai-agent-security): Prompt injection, least privilege, sandboxing, and layered guardrails. - [All learning guides](https://aiagentics.io/learn): The full agentic AI curriculum (21 free guides). ## Comparisons - [Comparisons hub](https://aiagentics.io/compare): Neutral, dated comparisons of agent frameworks and approaches. - [LangChain vs LlamaIndex](https://aiagentics.io/compare/langchain-vs-llamaindex): General agent framework vs. RAG/data framework. - [CrewAI vs AutoGen](https://aiagentics.io/compare/crewai-vs-autogen): Role-based crews vs. conversational multi-agent. - [LangGraph vs CrewAI](https://aiagentics.io/compare/langgraph-vs-crewai): Low-level graph control vs. higher-level abstraction. - [Single-agent vs multi-agent](https://aiagentics.io/compare/single-agent-vs-multi-agent): When one agent beats a team, and vice versa. - [AI agents vs RPA](https://aiagentics.io/compare/ai-agents-vs-rpa): Adaptive reasoning vs. deterministic rules. - [RAG vs fine-tuning](https://aiagentics.io/compare/rag-vs-fine-tuning): Inject knowledge at inference vs. change model weights. - [OpenAI Assistants vs LangChain](https://aiagentics.io/compare/openai-assistants-vs-langchain): Managed API vs. open framework. - [No-code vs code agents](https://aiagentics.io/compare/no-code-vs-code-agents): Visual builders vs. the SDK route. ## Glossary - [AI agents glossary](https://aiagentics.io/glossary): Plain-English definitions of every core agentic-AI term. - [AI agent](https://aiagentics.io/glossary/ai-agent) · [Agentic AI](https://aiagentics.io/glossary/agentic-ai) · [Large language model](https://aiagentics.io/glossary/large-language-model) · [Inference](https://aiagentics.io/glossary/inference) · [Context window](https://aiagentics.io/glossary/context-window) - [Tool calling](https://aiagentics.io/glossary/tool-calling) · [Function calling](https://aiagentics.io/glossary/function-calling) · [ReAct](https://aiagentics.io/glossary/react) · [Chain-of-thought](https://aiagentics.io/glossary/chain-of-thought) · [Prompt engineering](https://aiagentics.io/glossary/prompt-engineering) · [Hallucination](https://aiagentics.io/glossary/hallucination) - [RAG](https://aiagentics.io/glossary/rag) · [Embeddings](https://aiagentics.io/glossary/embeddings) · [Vector database](https://aiagentics.io/glossary/vector-database) · [Agent memory](https://aiagentics.io/glossary/agent-memory) · [Fine-tuning](https://aiagentics.io/glossary/fine-tuning) - [Multi-agent system](https://aiagentics.io/glossary/multi-agent-system) · [Orchestration](https://aiagentics.io/glossary/orchestration) · [Guardrails](https://aiagentics.io/glossary/guardrails) · [Model Context Protocol (MCP)](https://aiagentics.io/glossary/model-context-protocol) ## Use cases - [Use cases hub](https://aiagentics.io/use-cases): Agentic AI use cases across teams and industries. - [Customer support](https://aiagentics.io/use-cases/customer-support) · [Sales](https://aiagentics.io/use-cases/sales) · [Software engineering](https://aiagentics.io/use-cases/software-engineering) · [Research](https://aiagentics.io/use-cases/research) · [IT & operations](https://aiagentics.io/use-cases/it-operations) - [Data & analytics](https://aiagentics.io/use-cases/data-analytics) · [Marketing](https://aiagentics.io/use-cases/marketing) · [HR & recruiting](https://aiagentics.io/use-cases/hr-recruiting) · [Finance](https://aiagentics.io/use-cases/finance) ## Blog - [AI Agentics blog](https://aiagentics.io/blog): Tutorials, design patterns, and engineering deep-dives. - [How to build an AI customer support agent](https://aiagentics.io/blog/how-to-build-a-customer-support-agent): A step-by-step build tutorial. - [7 AI agent design patterns](https://aiagentics.io/blog/ai-agent-design-patterns): The reusable patterns behind reliable agents. - [How to reduce AI agent hallucinations](https://aiagentics.io/blog/reduce-ai-agent-hallucinations): Production techniques to cut confident-but-wrong answers. - [The state of AI agents in 2026](https://aiagentics.io/blog/ai-agents-trends-2026): Trends that matter this year. - [9 ways to cut AI agent costs](https://aiagentics.io/blog/cut-ai-agent-costs): Reduce token spend without losing quality. - [How to choose an AI agent framework in 2026](https://aiagentics.io/blog/choosing-ai-agent-framework): A decision framework for picking a stack. ## Product - [Features](https://aiagentics.io/features): Platform capabilities for building and running agents. - [Integrations](https://aiagentics.io/integrations): 200+ tool and data-source connectors. - [Pricing](https://aiagentics.io/pricing): Plans and free tier. - [Templates](https://aiagentics.io/templates): Ready-made agents to fork. - [Documentation](https://aiagentics.io/docs): Build with the SDK and API. - [API reference](https://aiagentics.io/docs/api-reference): Endpoints, schemas, and examples. - [SDKs](https://aiagentics.io/sdks): TypeScript and Python SDKs. ## Optional - [About](https://aiagentics.io/about): Company background. - [Security](https://aiagentics.io/security): Security posture and compliance. - [Contact](https://aiagentics.io/contact): Get in touch.