Trying to fix an unstable AI agent with a 200-line system prompt is like debugging a broken interface by writing longer comments
Most teams still treat prompt engineering as the main lever. In practice, the majority of hallucinations, infinite loops, and failed tool calls do not come from insufficient instructions. They come from missing contracts
When an agent gets an unpredictable payload, a bloated schema, or a polluted context, failure rates compound fast. We end up writing paragraphs of natural language to paper over what should have been enforced at the interface
Three rules that actually keep production agents stable: 1/ Strict contracts instead of verbose descriptions. Tool specs (MCP or custom) should be minimal and strongly typed. The fewer optional fields the model has to invent, the more deterministic the run becomes. 2/ Context isolation. Dumping full conversation history or raw database rows into one window is an anti-pattern. The agent should see only the exact state required for the current step. Everything else is noise. 3/ Structured error recovery. A model should never have to guess why a tool call failed. Clear, actionable error states bring it back on track far better than blind retries.
Designing interfaces for LLMs follows the same principles as designing distributed systems. If the boundaries are broken, no amount of clever prompting will save them.