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Automatic Error Recovery in AI Agent Networks

In a single-agent system, failure is simple: the agent errors, you retry. In multi-agent systems, failure is a graph problem.

  • Multi-agent systems face complex network failures, not single-agent issues
  • AgentForge's three-layer recovery strategy prevents systemic collapse
  • Layer three enables re-planning when critical agents fail

From Neural Networks to LLMs: The Mental Model I Was Missing

Before jumping into APIs, RAG, agents, and AI applications, I wanted to understand what actually happens inside an LLM. I kept coming across terms like neural networks, deep learning, Transformers…

  • Neural networks learn patterns from data without explicit programming
  • Transformers address RNN/LSTM limitations with attention mechanisms
  • Encoder-only models (BERT) and decoder-only models (GPT) serve different tasks

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