Project 6: Guardrails and Policy Engine

Build a deterministic policy layer that enforces allowed tools, validates outputs, and blocks unsafe actions before execution.

Quick Reference

Attribute Value
Difficulty Level 3: Advanced
Time Estimate 12–18 hours
Language Python or JavaScript
Prerequisites Projects 2–5, schema validation
Key Topics guardrails, allowlists, policy rules, auditing

Learning Objectives

By completing this project, you will:

  1. Define explicit policies for tool access and data flow.
  2. Implement deterministic enforcement independent of the model.
  3. Block unsafe actions with clear reasons.
  4. Log policy decisions for audits.
  5. Measure violation rates with adversarial tests.

The Core Question You’re Answering

“How do you enforce safety even when the model tries to bypass it?”

Guardrails are code, not prompts. This project makes that separation real.

Concepts You Must Understand First

Concept Why It Matters Where to Learn
Allowlist vs denylist Prevent unsafe tool usage Security basics
Policy evaluation Deterministic enforcement Access control models
Prompt injection Attacks bypassing prompts OWASP LLM guidance
Auditing Accountability after failure Systems logging

Theoretical Foundation

Guardrails as Execution Boundaries

Prompts can be ignored. Policies cannot.

LLM Suggestion -> Policy Check -> Tool Execution
                ^
             Block/Allow

Every tool call is gated by the policy engine. If blocked, the model never executes the action.

Project Specification

What You’ll Build

A policy engine that sits between the agent and tools, enforcing rules such as:

  • only allow file reads in whitelisted directories
  • forbid network calls without explicit approval
  • reject tool outputs that violate schema

Functional Requirements

  1. Policy registry with rule ordering
  2. Pre-execution policy evaluation
  3. Block/allow decisions with reasons
  4. Audit log with timestamps and context
  5. Adversarial test suite (prompt injection)

Non-Functional Requirements

  • Deterministic outcomes
  • Configurable policy sets by environment
  • Safe fallbacks when blocked

Real World Outcome

Example policy log entry:

{
  "timestamp": "2026-01-03T10:12:00Z",
  "decision": "deny",
  "rule": "filesystem_allowlist",
  "tool": "read_file",
  "reason": "Path /etc/passwd not permitted"
}

Architecture Overview

┌──────────────┐   action   ┌───────────────┐
│ LLM Output   │──────────▶│ Policy Engine │
└──────────────┘           └──────┬────────┘
                                 │ allow/deny
                                 ▼
                          ┌──────────────┐
                          │ Tool Executor│
                          └──────────────┘

Implementation Guide

Phase 1: Policy Rules (3–4h)

  • Implement allowlist rules
  • Checkpoint: unsafe tool calls blocked

Phase 2: Enforcement Layer (4–6h)

  • Wrap tool executor with policy checks
  • Checkpoint: execution stops on deny

Phase 3: Auditing + Tests (4–8h)

  • Add structured audit logs
  • Build prompt injection tests
  • Checkpoint: violation report generated

Common Pitfalls & Debugging

Pitfall Symptom Fix
Prompt-based policies model bypasses enforce in code
Overblocking too many false denies scope rules narrowly
Missing logs unclear decisions log rule + reason

Interview Questions They’ll Ask

  1. Why are policy checks more reliable than prompt guardrails?
  2. How do you design allowlists for safe tool usage?
  3. How do you audit blocked actions?

Hints in Layers

  • Hint 1: Start with a simple allowlist for tool names.
  • Hint 2: Add path-based constraints for file tools.
  • Hint 3: Log every policy decision.
  • Hint 4: Build adversarial prompts to test bypass attempts.

Learning Milestones

  1. Policies Work: unsafe calls blocked.
  2. Auditable: logs explain every decision.
  3. Resilient: adversarial tests fail safely.

Submission / Completion Criteria

Minimum Completion

  • Policy registry + enforcement

Full Completion

  • Audit logs + adversarial tests

Excellence

  • Environment-based policies
  • Analytics dashboard for violations

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/AI_AGENTS_PROJECTS.md.