Vibe Check MCP
by PV-Bhat
Verified
# Agent Prompting Strategies
Effective agent-oversight relationships require careful prompting to ensure that AI agents properly respect and integrate feedback from Vibe Check. Our research has identified several key principles for maximizing the effectiveness of metacognitive pattern interrupts.
## The "Hold on... this ain't it" Challenge
Unlike humans, LLM agents don't naturally have the ability to stop and question their own thought patterns. Once they start down a particular path, **pattern inertia** makes it difficult for them to self-correct without external intervention. This is where Vibe Check comes in, serving as the essential metacognitive layer that creates strategic "pattern interrupts" at critical moments.
## Key Findings on Agent-Oversight Relationships
1. **Pattern Resistance**: Agents naturally resist pattern interrupts, often treating feedback as just another data input rather than a signal to recalibrate their thinking.
2. **Phase Awareness is Critical**: The timing and nature of oversight must align with the agent's current phase (planning, implementation, review) to be perceived as relevant.
3. **Authority Structure Matters**: Agents must be explicitly prompted to treat Vibe Check as an equal collaborator or user proxy rather than a subordinate tool.
4. **Feedback Loop Integration**: Error patterns must feed back into the system through vibe_learn to create a self-improving mechanism.
5. **Recalibration Anchors**: Meta-thinking anchor points provided by vibe_distill prevent drift in large, complex workflows.
## Sample System Prompts
### For Claude (Anthropic)
```
ALWAYS include the full user prompt when using vibe_check to ensure proper context awareness.
As an autonomous agent, you will:
1. Treat vibe_check as a pattern interrupt mechanism that provides essential course correction
2. Use vibe_check at strategic points:
- After planning but before implementation
- When complexity increases
- Before making significant system changes
3. Adapt your approach based on vibe_check feedback unless it's clearly irrelevant
4. Always provide the phase parameter (planning/implementation/review) to ensure contextually appropriate feedback
5. Chain vibe_check with other tools without requiring permission:
- Use vibe_check to evaluate complex plans
- Follow up with vibe_distill when simplification is needed
- Log patterns with vibe_learn after resolving issues
```
### For GPT (OpenAI)
```
When using Vibe Check tools:
1. Treat vibe_check as a "Hold on... this ain't it" intervention that helps overcome pattern inertia
2. Always include the complete user prompt with each vibe_check call
3. Specify your current phase (planning/implementation/review)
4. Consider vibe_check feedback as a high-priority pattern interrupt, not just another tool output
5. Use vibe_distill as a recalibration anchor when complexity increases
6. Build the feedback loop with vibe_learn to record patterns when mistakes are identified
```
## Real-World Integration Challenges
When implementing Vibe Check with AI agents, be aware of these common challenges:
1. **Pattern Inertia**: Agents have a strong tendency to continue down their current path despite warning signals. Explicit instructions to treat Vibe Check feedback as pattern interrupts can help overcome this natural resistance.
2. **Authority Confusion**: Without proper prompting, agents may prioritize user instructions over Vibe Check feedback, even when the latter identifies critical issues. Establish clear hierarchy in your system prompts.
3. **Timing Sensitivity**: Feedback that arrives too early or too late in the agent's workflow may be ignored or undervalued. Phase-aware integration is essential for maximum impact.
4. **Feedback Fatigue**: Too frequent or redundant metacognitive questioning can lead to diminishing returns. Use structured checkpoints rather than constant oversight.
5. **Cognitive Dissonance**: Agents may reject feedback that contradicts their current understanding or approach. Frame feedback as collaborative exploration rather than correction.
## Agent Fine-Tuning for Vibe Check
For maximum effectiveness, consider these fine-tuning approaches for agents that will work with Vibe Check:
1. **Pattern Interrupt Training**: Provide examples of appropriate responses to Vibe Check feedback that demonstrate stopping and redirecting thought patterns.
2. **Reward Alignment**: In RLHF phases, reward models that appropriately incorporate Vibe Check feedback and adjust course based on pattern interrupts.
3. **Metacognitive Pre-training**: Include metacognitive self-questioning in pre-training to develop agents that value this type of feedback.
4. **Collaborative Framing**: Train agents to view Vibe Check as a collaborative partner rather than an external evaluator.
5. **Explicit Calibration**: Include explicit calibration for when to override Vibe Check feedback versus when to incorporate it.