Yes — while still an emerging field, agentic agents are already being used and experimented with across several real-world applications. These agents exhibit memory, autonomy, adaptability, and persistent identities. Many are research prototypes, while others are production-ready or actively evolving open-source tools.

🏛️ Academic & Research Examples

• Stanford’s Generative Agents (2023):
  A groundbreaking simulation of AI-powered NPCs living in a sandbox town. Each agent had memory, goals, and relationships — producing emergent behaviors like planning parties, forming friendships, and sharing gossip.
  • Technologies: GPT-3.5 + Vector Memory (FAISS)
  • Impact: Inspired a wave of agent-based social and game research
• Voyager (OpenAI Minecraft Agent):
  A self-improving autonomous agent that learns new skills in Minecraft using GPT-4. It stores lessons, reflects on failures, and unlocks complex capabilities over time.
  • Planner + Skill Repository + Execution Agent
  • First agent to show continual skill learning in an open world
• CAMEL Agents (Role-Playing Agents):
  Framework where two agents adopt roles (e.g., “researcher” and “engineer”) and collaborate to solve complex tasks in a constrained environment. Enables self-driven progress via dialogue loops.

🧪 Experimental & Open-Source Projects

• AutoGPT:
  An early attempt at building goal-seeking agents. You give it a goal, and it recursively creates tasks, executes them, and updates its plan.
  • Pros: Open-source, plugin system, customizable
  • Cons: Prone to errors, goal drift, lacks guardrails
• BabyAGI:
  Lightweight agent framework that uses a task queue, memory (via vector DB), and LLM to execute self-improving behaviors. A simplified variant of autonomous planning loops.
• SuperAGI:
  A more modular AutoGPT-style agent framework. Offers dashboards, memory management, and multi-agent execution capabilities.

💼 Industry Examples

• Devin (by Cognition Labs):
  A software development agent that autonomously writes, debugs, tests, and deploys code. It uses a persistent shell environment, memory, file system, and repo awareness to operate like a junior developer.
  • Real-time demos showed end-to-end issue tracking and PR creation
• Personal AI Assistants (WIP):
  Companies like OpenAI, Rabbit, and Replika are working on assistants that remember past interactions, plan proactively, and manage personalized tasks.
• Simulation NPCs:
  Games and virtual worlds increasingly use memory-driven agentic NPCs (e.g., “Simulacra AI,” “Inworld”) to deliver evolving behavior and human-like responses.

🎮 Use Case Highlight: AI NPCs in Games

AI characters that remember the player, form opinions, and plan their own day are now being prototyped using LLMs with memory and tool integrations. Examples include:

• Stanford Generative Agents (research prototype)
• Inworld AI (NPC platform with emotion and memory layers)
• Convai.ai (voice-based character agents)

🚧 Limitations in Real Deployments

• Cost: Real-time agents require hosting LLMs, persistent storage, and tool execution layers.
• Stability: Agents may hallucinate goals or misinterpret memory if not grounded properly.
• Guardrails: Production agentic agents need robust security, permissioning, and feedback systems.

🚀 Summary

Real-world agentic agents are already reshaping how we think about autonomy, simulation, and task execution. From simulated societies to personal taskbots and coding companions, these agents bring memory, intent, and adaptability into the AI landscape — and the ecosystem is evolving rapidly.