Author(s): Enzo Lombardi

Originally published on Towards AI.

Prompt architecture

The Part 1 system prompt was a single string literal. About sixty words. It told Eugene to answer questions about a Rust project and call read_file when it needed to see what was in a file. That prompt did its job because there was exactly one tool, exactly one user, and no expectation about how the answer should look.

After moving beyond a single-tool prompt, the article argues that agent prompts should be structured and layered so you can manage tool-selection, ordering, output format, tests, and caching without relying on fragile string literals. It presents a four-layer system prompt design—Identity (who the agent is/voice), Instructions (testable behavior rules), Output constraints (format requirements), and Examples (few-shot demonstrations)—followed by dynamic Context supplied per run. It explains how headers and clear section boundaries improve model understanding and tool usage, why identity controls voice rather than “truth,” and how output constraints decouple answer formatting from behavioral rules. The post then covers operational concerns: using a cache boundary to split stable static prompt sections from per-request context to reduce cost/latency, memoizing expensive dynamic sections in-process, and applying prompt fingerprinting plus regression tests to detect unintended prompt drift. It concludes with how this structured prompt changes Eugene v0.2 in practice and points to upcoming work (a Skill trait, registry-based dispatch, and JSON schema generation) to scale tool composition cleanly.

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Published via Towards AI