Why LLM agents keep failing (and it’s not the prompt)

Great list — and I agree with every point as practical advice. But I think it’s worth zooming out, because all five rules are essentially compensations for a missing layer.

1. “Stop Over-Prompting” — Exactly. But why do we over-prompt? Because we’re encoding logic, control flow, and context management inside the prompt itself. In ORCA, reasoning is decomposed into skills — small, declarative, reusable units. The prompt stays minimal because the structure carries the intent, not the text.

2. “Never Drop Below Q4” — True for unstructured generation. But when you externalize reasoning into a cognitive runtime, the model’s job shrinks: it executes one well-scoped step at a time, not an entire chain of thought. That changes the quantization equation — structured execution is more forgiving on model capacity.

3. “Provide Grounding via API” — 100%. In ORCA this is formalized through bindings — typed connectors between skills and real services (APIs, search, databases). Grounding isn’t an afterthought; it’s a first-class architectural element.

4. “Filter for Stability” — Agreed, but with structured skills you gain something bigger: model portability. Your agent logic lives in capabilities, not in a model-specific prompt. Swap the model without rebuilding the agent.

5. “Research the Model’s Ceiling” — This is where capability contracts come in. Each ORCA skill declares its inputs, outputs, and boundaries before execution. The ceiling is explicit and enforceable, not discovered through trial and error.

Your rules are solid engineering discipline. What I’m exploring with ORCA is whether we can encode that discipline into the runtime itself — so it’s not advice developers need to remember, but structure the system enforces.