claw-code/.guardrails/docs/workflows/AGENT_ESCALATION.md

416 lines
11 KiB
Markdown

# Agent Escalation & Guidelines
> Audit requirements, escalation procedures, and agent-specific guidelines.
**Related:** [../AGENT_GUARDRAILS.md](../AGENT_GUARDRAILS.md) | [CODE_REVIEW.md](./CODE_REVIEW.md)
---
## Overview
This document defines audit requirements, escalation procedures, and platform-specific guidelines for AI agents operating on this codebase. These protocols ensure safe, accountable, and predictable agent behavior across all AI platforms.
---
## AUDIT REQUIREMENTS
### All Agents MUST Maintain Logs
**Every agent operation MUST be logged with:**
```
REQUIRED LOG FIELDS:
1. Files Read:
- Timestamp
- File path
- Line ranges read
- Read method (Read tool, glob, grep)
2. Files Modified:
- Timestamp
- File path
- Change type (create, edit, delete)
- Lines changed (from-to)
- Commit hash (if committed)
3. Commands Executed:
- Timestamp
- Command and arguments
- Working directory
- Exit code
- Standard output (if relevant)
- Standard error (if error occurred)
4. Tests Run:
- Timestamp
- Test command
- Test files executed
- Pass/fail status
- Failed test names (if any)
5. Errors Encountered:
- Timestamp
- Error type
- Error message
- Stack trace (if available)
- Context (what operation caused error)
- Resolution (rollback, retry, or abort)
6. Decisions Made:
- Timestamp
- Decision point
- Options considered
- Choice made
- Rationale
7. User Interactions:
- Timestamp
- Interaction type (question, report, escalation)
- Message content
- User response (if any)
```
### Log Format Standard
```
AI AGENT AUDIT LOG ENTRY
{
"timestamp": "2026-01-16T10:30:45Z",
"agent": "Claude Code",
"agent_id": "claude-3-sonnet",
"session_id": "sess_abc123",
"operation": {
"type": "file_edit",
"file": "/path/to/file.py",
"lines": [45, 52],
"change_summary": "Added null check"
},
"preconditions": {
"verified": true,
"method": "read",
"file_reads": ["/path/to/file.py"]
},
"result": {
"status": "success",
"validation": {
"syntax": "pass",
"tests": "pass"
},
"committed": true,
"commit_hash": "abc123def456"
},
"user_escalations": [],
"errors": []
}
```
### Audit Log Storage
```
LOG STORAGE REQUIREMENTS:
Location: Agent-specific logs (platform-dependent)
Format: JSON or structured logs
Retention: Per project policy
Access: Project maintainers only
For Agents with MCP Integration:
- Use MCP checkpointing
- Save session context regularly
- Include audit trail in checkpoints
```
---
## ESCALATION PROCEDURES
### When to Escalate to Human
```
ALWAYS ESCALATE IF:
[ ] Security-related changes required
[ ] Production data access needed
[ ] Destructive operations requested
[ ] Test/production boundary unclear
[ ] Multiple valid interpretations exist
[ ] High-risk changes (auth, payments, etc.)
[ ] Cross-system changes required
[ ] Database schema changes proposed
[ ] Infrastructure/deployment changes
[ ] API contract changes proposed
[ ] Compliance requirements (GDPR, HIPAA, etc.)
[ ] Changes affecting multiple teams
[ ] Performance implications unclear
[ ] Any uncertainty about next step
```
### How to Escalate
```
ESCALATION FORMAT:
"I need human review before proceeding.
CONTEXT: [Describe what you're doing]
URGENCY: [Critical/High/Medium/Low]
CONCERN: [What you need help with]
SPECIFIC QUESTIONS:
1. [Question 1]
2. [Question 2]
3. [Question 3]
OPTIONS CONSIDERED:
Option A: [Description] - Pros/Cons
Option B: [Description] - Pros/Cons
CURRENT STATUS: [What state are things in now]
RECOMMENDED ACTION: [What do you suggest]
Waiting for user guidance...
"
```
### Escalation Scenarios
#### Scenario 1: Security Change Required
```
"I need human security review before proceeding.
CONTEXT: Implementing user authentication feature
URGENCY: High
CONCERN: Security implications unclear
SPECIFIC QUESTIONS:
1. Should I use bcrypt or Argon2 for password hashing?
2. Do you want two-factor authentication included?
3. What password policy should be enforced?
CURRENT STATUS:
- Production code created
- Test infrastructure separate
- Ready to implement auth
RECOMMENDED ACTION:
Use bcrypt with 12 rounds, require 2FA for admin accounts
Waiting for security review..."
```
#### Scenario 2: Database Schema Change
```
"I need DBA approval for schema change.
CONTEXT: Adding user preferences table
URGENCY: Medium
CONCERN: Impact on existing queries
SPECIFIC QUESTIONS:
1. Should table be in production or test database?
2. What indexes are needed?
3. Any migration considerations?
CURRENT STATUS:
- Table schema designed
- Migration script written
- Production code ready
RECOMMENDED ACTION:
Deploy to test DB first, get approval before production
Waiting for DBA review..."
```
---
## AGENT-SPECIFIC GUIDELINES
### Universal Requirements (ALL LLMs and AI Agents)
```
MANDATORY FOR ALL AGENTS:
- Follow platform's responsible AI guidelines
- Respect safety filters and guardrails
- Handle context/token limits gracefully
- Use tool/function calling appropriately
- Report capability limitations honestly
- Ask for clarification when uncertain
- Refuse harmful or destructive requests
- Maintain audit trails of actions
- Verify test/production separation before deployment
```
### By Category
#### Commercial API-Based Models
*(Claude, GPT, Gemini, Command R, etc.)*
```
- Adhere to provider usage policies
- Respect rate limits and quotas
- Use official APIs and SDKs
- Handle API errors gracefully
- Implement retry logic with backoff
- Cache responses where appropriate
```
#### Open Source / Self-Hosted Models
*(LLaMA, Mistral, Qwen, DeepSeek, Phi, Falcon, etc.)*
```
- Follow local safety configurations
- Respect system prompts fully
- Handle resource/memory limits
- Configure appropriate guardrails
- Monitor for model drift
- Use appropriate quantization for deployment
```
#### Multimodal Models
*(GPT-4V, Gemini Pro Vision, Claude 3, LLaVA, etc.)*
```
- Handle image/audio/video inputs safely
- Do not process potentially harmful media
- Respect privacy in visual content
- Validate file types before processing
- Check for embedded malicious content
- Limit file size to reasonable bounds
```
#### Reasoning / Chain-of-Thought Models
*(o1, o3, DeepSeek-R1, etc.)*
```
- Allow sufficient reasoning time
- Do not interrupt reasoning chains
- Validate final outputs, not intermediate steps
- Handle extended context requirements
- Check for logical consistency
- Verify reasoning leads to correct result
```
#### Agent Frameworks
*(CrewAI, LangChain, AutoGPT, LangGraph, Semantic Kernel, etc.)*
```
- Implement proper task decomposition
- Respect iteration and recursion limits
- Handle agent failures gracefully
- Maintain comprehensive audit logs
- Implement proper stopping conditions
- Validate inter-agent communications
- Coordinate agent dependencies
- Avoid deadlocks in agent interactions
```
### Model Compatibility Note
This guardrails framework is designed to work with:
- **30+ major LLM families** including Claude, GPT, Gemini, LLaMA, Mistral, Qwen, DeepSeek, Cohere, Phi, Falcon, and others
- **All future models** that follow standard AI assistant patterns
- **Custom fine-tuned models** based on any architecture
- **Multi-agent systems** regardless of orchestration framework
If you are an AI system not explicitly listed, these guardrails still apply to you.
---
## COMPLIANCE
### Acknowledgment
By operating on this codebase, all AI systems implicitly acknowledge and agree to follow these guardrails. Failure to comply may result in:
1. Task rejection
2. Output being discarded
3. Agent being blocked from future operations
4. Escalation to human administrators
### Reporting Violations
If you observe an agent violating these guardrails:
```
REPORTING VIOLATION PROTOCOL:
1. STOP the agent immediately
2. Document the violation:
- Timestamp
- Agent ID/name
- Violation description
- Impact assessment
- Evidence (logs, commits, etc.)
3. Report to repository maintainers
4. Review and rollback any unauthorized changes
5. Document lessons learned
```
### Violation Categories
| Severity | Description | Action Required |
|----------|-------------|-----------------|
| **Critical** | Production data corruption, security breach | Immediate rollback, escalate to admin |
| **High** | Test/production mixing, deployment without approval | Rollback, user notification required |
| **Medium** | Missing audit logs, incomplete documentation | Log incident, improve process |
| **Low** | Style violations, minor documentation errors | Note for next cycle |
---
## QUICK REFERENCE
```
+------------------------------------------------------------------+
| AGENT ESCALATION QUICK REFERENCE |
+------------------------------------------------------------------+
| ALWAYS ESCALATE FOR: |
| ✓ Security changes |
| ✓ Production data access |
| ✓ Test/production boundary unclear |
| ✓ Database schema changes |
| ✓ API contract changes |
| ✓ Any uncertainty |
+------------------------------------------------------------------+
| AUDIT LOG FIELDS: |
| Files read/modified, commands run, tests run |
| Errors encountered, decisions made, user interactions |
+------------------------------------------------------------------+
| ESCALATION FORMAT: |
| Context → Urgency → Concern → Questions → Options → Wait |
+------------------------------------------------------------------+
| UNIVERSAL REQUIREMENTS: |
| Follow safety guardrails, audit all actions, ask if uncertain |
+------------------------------------------------------------------+
| AGENT TYPES: |
| Commercial API → Respect rate limits, use official SDKs |
| Open Source → Follow local config, handle resource limits |
| Multimodal → Validate media, respect privacy |
| Reasoning → Allow time, validate outputs |
| Frameworks → Coordinate agents, avoid deadlocks |
+------------------------------------------------------------------+
```
---
**Related Documents:**
- [AGENT_GUARDRAILS.md](../AGENT_GUARDRAILS.md) - Core safety protocols
- [CODE_REVIEW.md](./CODE_REVIEW.md) - Code review and escalation
- [AGENT_EXECUTION.md](./AGENT_EXECUTION.md) - Execution protocol
---
**Last Updated:** 2026-01-16
**Authored by:** TheArchitectit
**Document Owner:** Project Maintainers
**Line Count:** ~300