Why agents go off track (and it's usually not the AI's fault)

Most agent failures are instruction failures: vague prompts, undefined edge cases, no fallback behavior. A well-written system prompt with examples, constraints, and escalation rules will prevent 80% of bad decisions before they happen.

The 4-part agent instruction framework

Every agent needs: 1) Goal (what output it must produce). 2) Constraints (what it must never do). 3) Examples (3-5 sample inputs and perfect outputs). 4) Escalation protocol (what to do when uncertain). Missing any of these is the most common cause of agent drift.

How do you build a guardrail system?

Guardrails operate at three levels: prompt level (instructions in the system prompt), tool level (the agent can only access specific tools/data), and workflow level (outputs route through a human review queue before execution). Use all three for customer-facing agents.

What's the Autonomous Mission Protocol approach to bad decisions?

AMP gives each agent a scope boundary. Anything outside that boundary triggers an automatic pause and human escalation, rather than the agent improvising. This is different from traditional automation where the system just fails — the agent knows when it doesn't know.

How do you monitor for drift over time?

Run a weekly 'agent audit': pick 10 random outputs and score them against your quality criteria. Track the score over time. If quality trends down, update the system prompt with more examples from the failure cases. Document all prompt updates with the reason.