Stop Your AI Agent From Making Things Up: 3 Guardrails That Actually Work

Keywords: AI agent guardrails, hallucinations, n8n AI workflows, JSON schema validation, context window, memory drift, input sanitization, fact checking

My AI agent was confidently wrong. It told users their refund would process in “2–3 business days” when our actual policy was 5–7. It created ticket IDs that didn’t exist. It forgot user preferences after a few messages.

At first I assumed it was “random AI behavior”. It wasn’t. It was a missing systems layer: guardrails. Once I added them, the agent stopped lying and started behaving like a reliable component.

Why Guardrails Aren’t Optional

Think of guardrails like seatbelts. You don’t build them because you’re pessimistic. You build them because production is unpredictable.

Without guardrails, you’ll see three predictable failures:

• Hallucinations: plausible-but-fake facts (“refund in 2 days”)
• Memory drift: the agent contradicts earlier context as the chat grows
• Format errors: JSON breaks, fields change, downstream nodes crash

I learned this while building a customer support categorization agent. It worked in testing… then started inventing categories like “urgent billing escalation” that didn’t even exist in our CRM.

Guardrail 1: Structured Output Validation (Schema + Allowlist)

Your first rule: never trust raw AI text. Treat the AI like an API that can return invalid responses. Define the format, then validate it.

In n8n: AI Node → Function Node (validate) → only then continue.

What to validate

• Is it valid JSON?
• Are all required fields present?
• Are values constrained to allowed lists?
• Are numbers in correct ranges?
• Does it contain prohibited claims/phrases?

Here’s a practical validator you can paste into an n8n Function node:

// n8n Function node: Validate AI JSON output (no external libs)
const validCategories = ["billing", "technical", "account", "general"];
const validPriorities = ["low", "medium", "high", "critical"];

function fail(error, raw) {
  return [{
    json: {
      valid: false,
      error,
      raw
    }
  }];
}

function ok(data) {
  return [{
    json: {
      valid: true,
      ...data
    }
  }];
}

const raw = $input.first().json.ai_response;

let parsed;
try {
  parsed = JSON.parse(raw);
} catch (e) {
  return fail(`Invalid JSON: ${e.message}`, raw);
}

// Required fields
const required = ["category", "priority", "confidence"];
for (const key of required) {
  if (!(key in parsed)) return fail(`Missing field: ${key}`, raw);
}

// Allowlist validation
if (!validCategories.includes(parsed.category)) {
  return fail(`Invalid category: ${parsed.category}`, raw);
}
if (!validPriorities.includes(parsed.priority)) {
  return fail(`Invalid priority: ${parsed.priority}`, raw);
}

// Range validation
if (typeof parsed.confidence !== "number" || parsed.confidence < 0 || parsed.confidence > 1) {
  return fail(`Invalid confidence: ${parsed.confidence}`, raw);
}

// Pass through validated output
return ok({ data: parsed });

This single node eliminates a huge chunk of “AI randomness”. If the model tries to return “super urgent”, your workflow blocks it immediately.

Bonus tip: log every failure (input + raw output + error) to a Google Sheet. Those failures become your prompt improvements and new allowlist entries.

Guardrail 2: Memory Window Management (Stop Context Drift)

Memory drift happens because models have limited context windows. As the conversation grows, older details get dropped or diluted. That’s not a bug — it’s physics.

The fix is simple: limit the window and keep a state summary.

My practical memory strategy

• Keep only the last N messages (example: 10–20)
• Maintain a compact “User State” JSON (preferences, last action, constraints)
• Send the state summary every time, even when trimming older messages

Example function to trim history (works with chat-style arrays):

// n8n Function node: Trim conversation window
const maxMessages = 15; // adjust per model
const history = $input.first().json.history || []; // [{role, content}, ...]
const system = history.find(m => m.role === "system");
const msgs = history.filter(m => m.role !== "system");

const trimmed = msgs.length <= maxMessages ? msgs : msgs.slice(-maxMessages);

return [{
  json: {
    history: system ? [system, ...trimmed] : trimmed
  }
}];

The “state summary” pattern (highly recommended)

Instead of forcing the model to remember everything, store stable facts in a small object:

{
  "user_state": {
    "customer_tier": "pro",
    "refund_policy_days": "5-7",
    "preferred_language": "en",
    "last_ticket_id": "SUP-10921"
  }
}

You can store that in Sheets/Postgres and inject it into the prompt every run. This is how you prevent “it forgot after 3 messages”.

Guardrail 3: Input Sanitization + Fact-Check Gates

Bad inputs create confident lies. If the user gives a wrong order ID, many agents will “helpfully” invent details. Your job is to stop that upstream.

Sanitize inputs

• Trim whitespace, normalize casing
• Extract IDs with regex (order IDs, ticket IDs)
• Normalize dates (“next Friday” → ISO date if possible)
• Remove dangerous prompt-injection text when using tools

Fact-check gates

Before calling the AI for policy/transactional answers, verify against known sources:

1. Extract order ID (Function node)
2. Lookup order in DB/Sheets (HTTP Request / DB node)
3. If not found → ask user to confirm ID (no AI guesswork)
4. If found → AI can explain next steps using validated facts

This single “exists check” can drop real-world errors massively. It turns hallucination-prone tasks into deterministic workflows.

Putting It Together: A Production-Ready n8n Flow

Workflow outline:

1. Trigger: new message (WhatsApp/Email/Discord/Webhook)
2. Sanitize input: clean text + extract IDs
3. Fact-check: verify IDs / policies via DB or source-of-truth
4. Load memory: last N messages + user_state JSON
5. AI node: generate structured JSON output
6. Validate output: schema + allowlists + range checks
7. Confidence gate: if confidence < 0.7 → human review queue
8. Update memory: append message + update user_state
9. Respond: send final message / trigger action

Notice the order: facts → AI → validation. Not AI first.

Common Mistakes (That Keep Agents Broken)

1) Validating too late

Validate immediately after the AI node. If invalid output reaches downstream nodes, you’ll debug symptoms instead of the cause.

2) Not logging failures

Every failure is data. Log input + model output + validator error into a sheet. That becomes your “prompt QA system”.

3) Ignoring confidence

If the model says it’s uncertain, believe it. Route low-confidence results to human review.

4) Using the same strictness for everything

Creative writing needs loose guardrails. Support/refunds/finance needs strict ones. Match guardrails to the cost of being wrong.

FAQ

Do guardrails slow workflows down?

Not meaningfully. Validation is milliseconds. Debugging hallucinations is hours.

Can I add these guardrails to an existing agent?

Yes. Start with output validation, then add memory trimming + state summary, then input + fact checks.

What if my model struggles to return JSON?

Force it with a contract prompt (“Return ONLY JSON with fields X”), and still validate. If it fails, retry once, then route to review.

The Before/After Reality

Before guardrails: confident lies, missing context, broken JSON, downstream crashes.

After guardrails: invalid outputs get blocked, chats stay coherent, workflows fail safely.

Guardrails don’t limit your agent. They make it usable.

Your Next Step

Start today with Guardrail #1: validate JSON + allowlist fields. That alone will kill most hallucination damage. Then add memory window + state summary. Then add fact-check gates where accuracy matters.

If you want, I can also provide:

• A ready-to-import n8n JSON template for these guardrails
• A Google Sheets schema for memory + logs
• Contract prompts that reliably produce parseable JSON

Comment "GUARDRAILS" and I’ll package the starter kit structure you can reuse across agents.

— Avnish Yadav