#claude-ia

Why agent optimization is different from prompt engineering

Prompt engineering focuses on getting a model to produce the right answer. Agent optimization focuses on getting an agent to produce the right answer consistently, thousands of times, at a predictable cost. These are different problems that require different approaches. The dominant cost driver in an agentic workflow isn't the length of the system prompt. It's the number of LLM calls the agent makes to complete its work. Cost scales with llm_calls × input_tokens_per_call, and input tokens per ca

Step 4: Revise and verify

Two options exist for producing revised instructions: write the changes directly or use an AI assistant to analyze and rewrite them. Write the changes directly. If the trace analysis points to a small number of clear issues (a missing stopping criterion, a redundant query instruction, a field name typo), edit the system prompt directly. Small, targeted changes are easy to review and understand. They're also easier to isolate as the cause if something regresses. Use an AI assistant to analyze a

Étape 5 : Brancher Claude Code dessus