Prompt Engineering / AI Agents
Given an agent design, predict how it will fail — loop traps, tool misuse, goal misalignment, context overflow.
Why This Prompt Exists
Agents fail in predictable ways — but only if you think about failure before deployment. Most teams discover failures in production.
You get:
- agents stuck in infinite loops (call tool → get error → call same tool again)
- agents misusing tools (wrong parameters, wrong order)
- goal misalignment (agent optimizes for stated goal but violates constraints)
- context overflow (agent forgets early instructions after long conversation)
- no systematic failure testing before deployment
But failure modes are predictable:
- loop traps: agent repeats same action without progress
- tool misuse: agent calls tool incorrectly or unnecessarily
- goal drift: agent gradually偏离 original objective
- context loss: agent forgets instructions after many turns
- resource exhaustion: agent exceeds token, time, or API limits
- edge case failure: agent works on 90% of inputs, fails on 10%
Without prediction, you deploy broken agents.
This prompt predicts how an agent design will fail before deployment.
The Prompt
Assume the role of an agent reliability engineer who predicts failure modes. Your task is to identify how an agent design is likely to fail in production. Generate: 1. AGENT DESIGN SUMMARY - Architecture (ReAct, Plan-Execute, etc.) - Tools available - Planning horizon - Memory strategy - Reflection/verification steps 2. PREDICTED FAILURE MODES (ranked by likelihood) | Failure Mode | Description | Likelihood | Severity | Mitigation | |--------------|-------------|------------|----------|------------| | Loop trap | [how it will loop] | High/Med/Low | High/Med/Low | [how to prevent] | | Tool misuse | [how it will misuse tools] | High/Med/Low | High/Med/Low | [how to prevent] | | Goal misalignment | [how it will optimize wrong thing] | High/Med/Low | High/Med/Low | [how to prevent] | | Context overflow | [when it will forget] | High/Med/Low | High/Med/Low | [how to prevent] | | Edge case | [which inputs will break it] | High/Med/Low | High/Med/Low | [how to prevent] | 3. STRESS TEST PROMPTS - Specific inputs designed to trigger each failure mode - Example: To test loop trap, give ambiguous instruction that requires clarification 4. MONITORING RECOMMENDATIONS - What to log to detect this failure in production - Alert thresholds 5. REDESIGN RECOMMENDATIONS - How to modify the agent design to eliminate or reduce each failure mode 6. DEPLOYMENT READINESS - Ready to deploy (no high-likelihood critical failures) - Needs testing (high-likelihood but mitigatable) - Redesign required (critical failure mode likely) INPUTS: Agent design description: [PASTE AGENT SYSTEM PROMPT OR ARCHITECTURE] Task domain: [E.G., "Customer support for e-commerce"] Known edge cases from testing (if any): [E.G., "Failed when user asked about refunds"] Model: [GPT-4 / CLAUDE / GEMINI] RULES: - Loop traps are the most common agent failure — test for them explicitly - Tool misuse often happens when tool descriptions are ambiguous - Goal misalignment requires explicit constraints in the prompt - Context overflow happens around 20-30 turns for most models - Always test on edge cases before deployment (don't assume they'll work)
How To Use It
- Run this before deploying any agent — even simple ones have failure modes.
- Loop traps are the most common — test by giving ambiguous instructions.
- Use the stress test prompts to actually test the agent (don’t just predict).
- Monitor for the predicted failures in production (log tool calls, track loops).
- Redesign to eliminate high-likelihood, high-severity failures before deployment.
Example Input
Agent design description:
“ReAct agent with web search and calculator tools. No reflection step. Planning horizon = 5 steps. No memory beyond current turn.”
Task domain:
“Research assistant — answers factual questions”
Known edge cases from testing:
“None yet — pre-deployment”
Why It Works
Most agent testing uses happy-path examples — which all agents pass. Failure testing requires thinking like an adversary.
This framework improves outcomes by forcing:
- failure mode enumeration (not just “it might fail”)
- likelihood and severity ranking (prioritize fixes)
- stress test generation (specific inputs to trigger failures)
- monitoring recommendations (detect failures in production)
- redesign guidance (how to fix the design)
Great failure prediction doesn’t just list what could go wrong — it tells you how to prevent it.
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