AI Agent

One of the most important AI Harness

• planning
  AI Scheming

• memory

• tool interface

Specifically

• Tool learning
• System Calls
• Socket
• Headless Browser access to pretend human
• CLI access
• Curl
• GET - Search
• POST - Communication

• Permanent Storage like vector database
• Context Selection based on vector similarity
• Long-term memory by weight update
• Vanishing to prevent manage capacity based on LLM decision or dynamic sparsity

• Self-replication, Self-restart
• Self-initialization prompt, Self-editing

• Asynchronous communication port
• Interrupt

• …

Text to Task

Generative Model

• AI Agent

• AI Assistant

• AI Automation

• Photoshop like AI software

Google whitepaper

Agents

Authors: Julia Wiesinger, Patrick Marlow and Vladimir Vuskovic

https://www.kaggle.com/whitepaper-agents

Workflow orchestration (

Prompting Optimization)

Agent Recipes

Explore common agent recipes with ready to copy code to improve your LLM applications.

https://www.agentrecipes.com/

In the Matter of OpenAI vs LangGraph

The silent war in Agent Engineering gets loud.

https://www.latent.space/p/oai-v-langgraph

Agent Software

• Parity: Agents should be able to perform any action through tools that is possible through the UI.

• Granularity: Tools should be atomic primitives without judgment. The agent makes the decisions.

• Composability: New features are composed through prompts alone without code changes.

• Emergent Capability: Agents should be able to solve unplanned requests by composing available tools.

• Improvement over TimePerformance: improves without deployment through context accumulation and prompt refinement alone.

Agent-native Architectures: How to Build Apps After Code Ends

A new software paradigm for the AI age

https://every.to/guides/agent-native