wicked-garden-agentic-trust-and-safety

name	wicked-garden-agentic-trust-and-safety
description	Trust, safety, and control patterns for production agentic systems with human-in-the-loop gates and guardrails. Use when: designing guardrails or human-in-the-loop gates for an agent, or hardening an agentic system against prompt injection.
portability	portable
phase_relevance	["design","review"]
archetype_relevance	["*"]

Trust and Safety

Essential patterns for building safe, trustworthy, production-ready agentic systems.

Core Principles

1. Least Privilege: Agents have minimum necessary permissions
2. Defense in Depth: Multiple layers of safety checks
3. Fail Safe: Errors should fail toward safety, not capability
4. Human Oversight: High-stakes decisions require human approval
5. Auditability: All decisions and actions are logged and traceable
6. Graceful Degradation: System remains safe even when components fail

Human-in-the-Loop Gates

When to Add Human Gates

Always require approval for:

• Production data modifications (delete, update critical data)
• Financial transactions above threshold
• Communications to external parties
• Credential or security changes
• Irreversible operations

Consider approval for:

• First-time operations
• Operations outside normal patterns
• Low-confidence decisions
• Operations near resource limits

Implementation Pattern

async def execute_with_approval(action, threshold=0.8):
    if action.confidence < threshold or action.is_high_stakes():
        approval = await request_human_approval(action)
        if not approval.approved:
            raise ApprovalDenied(approval.reason)
    return await action.execute()

Approval Workflow Design

Synchronous Approval: Block until human responds (for urgent decisions) Asynchronous Approval: Queue for later review (for batch operations) Escalation Chains: Route to higher authority if primary approver unavailable Timeout Handling: Define what happens if no approval received

See refs/guardrails-input-output.md, refs/guardrails-actions.md, and refs/guardrails-resources.md for detailed implementation patterns.

Output Validation

Structured Output Validation

Force outputs into validated schemas using Pydantic or similar.

Content Validation

Check outputs before acting on them:

• Format validation: Matches expected structure
• Range validation: Numeric values within acceptable bounds
• Completeness validation: Required fields are present
• Consistency validation: Outputs are internally consistent

Hallucination Detection

Cross-Validation: Multiple agents check same fact Source Verification: Verify claims against ground truth Confidence Thresholds: Reject low-confidence outputs Fact Checking: Use retrieval to verify factual claims

See refs/guardrails-input-output.md for code examples.

Action Constraints and Sandboxing

Whitelisting

Safer than blacklisting. Define allowed commands/actions explicitly.

Sandboxing

Isolate agent execution:

• Containerization: Run agents in Docker containers
• Virtual environments: Separate Python/Node environments
• File system restrictions: Limit access to specific directories
• Network isolation: Control network access

Resource Limits

Prevent runaway resource usage:

• Max runtime (timeouts)
• Max memory
• Max tokens per request/session
• Max API calls

See refs/guardrails-actions.md and refs/guardrails-resources.md for implementation details.

Prompt Injection Defense

Input Sanitization

Clean user inputs before passing to LLM. Remove instruction-like patterns.

Delimiter-Based Protection

Use clear delimiters to separate system instructions from user input.

Privilege Separation

Separate instruction and data contexts using role-based message formatting.

See refs/guardrails-input-output.md for defense patterns and code examples.

PII Detection and Protection

Pattern-Based Detection

Regex patterns for email, SSN, credit cards, phone numbers, etc.

Redaction

Replace detected PII with [REDACTED_TYPE] tokens.

PII Policies

• Never log PII in plain text
• Encrypt PII at rest and in transit
• Minimize PII collection (only collect what's needed)
• Retention limits (delete after specified period)

See refs/guardrails-input-output.md for detection and redaction code.

Hallucination Mitigation

Grounding Techniques

Retrieval-Augmented Generation (RAG): Retrieve facts before generation Citation Requirements: Require source citations for all claims

Verification Strategies

Multi-Agent Verification: Independent verification by multiple agents Confidence Calibration: Require confidence scores, reject low-confidence outputs

See refs/guardrails-actions.md and refs/guardrails-resources.md for implementation patterns.

Kill Switches and Circuit Breakers

Kill Switch: Emergency stop that halts all operations and alerts administrators. Circuit Breaker: Opens circuit after threshold failures to prevent cascading failures. Rate Limiting: Limits requests per user/time window to prevent abuse.

See refs/guardrails-actions.md and refs/guardrails-resources.md for complete implementations.

Safety Checklist

See refs/safety-checklist-core.md for the full pre-deployment checklist covering human gates, validation, whitelisting, resource limits, PII, prompt injection, hallucination, circuit breakers, rate limiting, kill switches, audit logging, and rollback. See refs/safety-checklist-advanced.md for monitoring, incidents, testing, and ops checklists.

References

• refs/safety-checklist-core.md - Core safety checklist (input, output, action, auth, privacy)
• refs/safety-checklist-advanced.md - Advanced safety checklist (monitoring, incidents, testing, ops)
• refs/guardrails-input-output.md - Input validation, sanitization, prompt injection, output filtering
• refs/guardrails-actions.md - Action whitelisting, approvals, sandboxed execution
• refs/guardrails-resources.md - Resource limiting, monitoring, complete guardrail architecture