Project 3: State Invariants Harness

Build an invariant checker that validates agent state after every step and fails fast with human-readable violation reports.

Quick Reference

Attribute Value
Difficulty Level 2: Intermediate
Time Estimate 8–14 hours
Language Python or JavaScript
Prerequisites Project 2, schema validation
Key Topics invariants, contracts, validation, debugging

Learning Objectives

By completing this project, you will:

  1. Define precise invariants for agent state.
  2. Build a checker that returns actionable errors.
  3. Set severity levels (error vs warning).
  4. Integrate validation into the agent loop.
  5. Write tests that prove the checker works.

The Core Question You’re Answering

“What conditions must always be true for an agent to be trustworthy?”

Without explicit invariants, agents can silently drift into invalid state. This project makes invalid state impossible to ignore.

Concepts You Must Understand First

Concept Why It Matters Where to Learn
Design by contract Invariants define correctness Meyer, software engineering
Schema validation Ensures structure correctness Pydantic/Zod docs
State machines Prevent invalid transitions CS fundamentals
Provenance tracking Enables debugging Agent memory papers

Theoretical Foundation

Invariants as Runtime Guarantees

An invariant is a rule that must always be true. Examples:

  • goal is non-empty
  • step never decreases
  • plan has no cycles
  • all memory entries have timestamps

When a violation happens, the agent must stop or degrade safely.

Project Specification

What You’ll Build

A validation module that runs after each agent step and outputs a structured violation report.

Functional Requirements

  1. Invariant registry with named checks
  2. Severity levels: error vs warning
  3. Violation reports with expected vs actual
  4. CLI to validate saved state
  5. Unit tests per invariant

Non-Functional Requirements

  • Safe execution (checker never crashes)
  • Clear, actionable error messages
  • Configurable invariant sets

Real World Outcome

When a violation happens, you see a report like:

{
  "step": 4,
  "invariant": "goal_defined",
  "severity": "error",
  "expected": "goal is non-empty",
  "actual": "goal is null",
  "fix": "ensure goal is set before executing tools"
}

This lets you stop immediately instead of debugging downstream failures.

Architecture Overview

┌───────────────┐
│ Agent State   │
└──────┬────────┘
       ▼
┌───────────────┐
│ Invariant     │
│ Checker       │
└──────┬────────┘
       ▼
┌───────────────┐
│ Violation     │
│ Report        │
└───────────────┘

Implementation Guide

Phase 1: Core Invariants (3–4h)

  • Implement five critical invariants
  • Checkpoint: valid state passes

Phase 2: Reporting (2–4h)

  • Add expected vs actual fields
  • Checkpoint: error reports readable

Phase 3: Integration (3–6h)

  • Run checker after every agent step
  • Checkpoint: agent halts on critical violation

Common Pitfalls & Debugging

Pitfall Symptom Fix
Cryptic errors “validation failed” include expected vs actual
False positives valid states fail add edge-case tests
Slow checks agent stalls run expensive checks periodically

Interview Questions They’ll Ask

  1. Why are runtime invariants needed in addition to static typing?
  2. Which invariants should halt execution vs warn?
  3. How do you test an invariant checker itself?

Hints in Layers

  • Hint 1: Implement invariants as small pure functions.
  • Hint 2: Separate critical vs optional checks.
  • Hint 3: Add structured violation objects.
  • Hint 4: Run checker after every step in the agent loop.

Learning Milestones

  1. Valid State: all invariants pass.
  2. Actionable: violation report explains root cause.
  3. Integrated: agent halts on invalid state.

Submission / Completion Criteria

Minimum Completion

  • Invariant registry with 5 checks

Full Completion

  • Violation report + CLI

Excellence

  • Severity levels + property-based tests

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