Where you put the intelligence in a knowledge system — in the code or in the prompts — is a critical architectural fork with real consequences. Pal puts nearly everything in a 400-line prompt: routing, compilation, schema creation, even learning. Our graph puts structure in YAML/markdown with deterministic validation (validate-graph.js handles 7 structural checks in 0.3s). The trade-off is sharp:
Prompt-driven intelligence is more flexible — you can change behavior by editing text, no deploy needed. But it’s fragile: it breaks when models change behavior between versions, when instruction-following degrades under token pressure, or when instructions are simply misunderstood. Every prompt is a bet that the model will interpret it the same way next time.
Code-driven intelligence is rigid but reliable — validate-graph.js will catch a missing reciprocal link every time, regardless of which model is running. But it can’t handle nuance, context, or judgment calls.
The best systems will likely need both: deterministic structure for what can be verified mechanically, plus LLM-powered operations for what requires judgment. Our graph already does this — In agent-native architecture, features are prompts — not code describes the prompt-driven side (agent skills as prompts), while Safety enforcement belongs in tool design, not system prompts captures the principle that deterministic constraints belong in code. The A mediocre agent inside a strong harness outperforms a stronger agent inside a messy one insight explains why: the surrounding machinery (code-driven) matters more than the model capability (prompt-driven) for reliable systems. This is also why Production agents route routine cases through decision trees, reserving humans for complexity — the routing structure should be deterministic, with LLMs invoked only for genuinely ambiguous cases.