Implementing Prompt Compression to Reduce Agentic Loop Costs

Agentic AI loops accumulate token costs quadratically as context grows with each step, making prompt compression a practical necessity. Four main strategies are covered: instruction distillation (shortening system prompts using shorthand the model understands), recursive summarization (periodically condensing prior steps with a cheap model like GPT-4o-mini or Llama 3), vector database RAG for history retrieval (storing history in FAISS/Chroma and fetching only relevant context), and LLMLingua (an open-source framework that removes non-critical tokens). A working Python example combines recursive summarization and instruction distillation, demonstrating how a 42-token system prompt can be reduced to 12 tokens, saving ~3,000 tokens over a 100-step loop.