Developers · Definition
What Are Agentic Apps? A Developer’s Guide
Agentic apps let AI act autonomously with tools, planning loops and memory — perceiving, deciding and executing multi-step tasks rather than single prompt-response turns.
Agentic loop
Components
Core components of agentic apps
Five building blocks — memory is the one most teams underestimate.
- LLM — reasoning, generation and tool-calling (GPT-4o, Claude, open weights)
- Tools / APIs — search, code execution, CRM, memory operations
- Planning loop — ReAct, planner-executor or graph-based orchestration (LangGraph, Letta)
- Memory — cross-session persistence via a memory layer; without it agents reset every session. Mem0 LOCOMO J 66.9 with median search 0.148 s (Chhikara et al., 2025) shows what production memory achieves
- Observability — traces, LOCOMO/LongMemEval in CI, memory audit logs
Comparison
Agentic apps vs traditional AI features
Reactive chatbots answer one turn; agentic apps plan, act and persist across sessions.
| Dimension | Traditional AI feature | Agentic app |
|---|---|---|
| Interaction | Single prompt → response | Multi-step plan → tool calls → observe → repeat |
| Autonomy | User drives every step | Agent decides next action within guardrails |
| Memory | Session context or none | Memory layer with extract/retrieve/update (Engram, Mem0, Zep) |
| Tools | None or read-only search | Write APIs, code execution, memory tools |
| Examples | Chat widget, doc summarizer | Coding agent, support bot with history, personal assistant |
FAQ
Frequently asked questions
Examples of agentic apps?
Coding agents (Cursor, Devin-style), personal assistants with cross-session memory, support bots that act on tickets, research agents with tool loops. See AI-native app examples.
Do agentic apps need memory?
For production, yes. Without a memory layer agents forget between sessions. See memory layer in the AI-native stack.
Agentic app vs chatbot?
Chatbots are reactive (one turn). Agentic apps plan, call tools and persist state across steps and sessions.
Frameworks for agentic apps?
LangGraph (graphs + LangMem), Letta (MemGPT paging), custom ReAct loops. Memory: Engram (Weaviate), Mem0, Zep, LangMem.
First steps to build an agentic app?
Scope → model → tools → memory layer → eval. See build an AI agent.
Production pitfalls for agentic apps?
No memory layer, no user_id scoping, no LOCOMO eval in CI, unbounded tool loops. Gate deploys on recall@k regression.