When developers try to anticipate and enumerate every possible case in an AI agent prompt, the result is a bloated, rule-heavy prompt that overwhelms the model and breaks conversational flow. Over-prompting increases cognitive load, produces slower and error-prone responses, and still fails to handle real-world edge cases. This article shows why a minimal, focused prompt works better and how reducing rule bloat leads to more natural, reliable AI agent behavior.

You've got your voice AI agent set up. It handles calls. But you keep finding edge cases.

"What if the customer says this?"
"What if they don't respond?"
"What if they ask about that?"

Before you know it, your prompt is three pages long and your agent is more confused than before.

I've been there. The instinct to cover every scenario is strong. But here's the counterintuitive truth: the more you say, the less your AI agent understands.

What does over-prompting look like?

Here's what over-prompting looks like in practice:

prompt: |
 You are a customer service agent for Acme Corp.

 IMPORTANT RULES:
 - Always be polite and professional
 - Never hang up on customers
 - Always verify account before giving information
 - Never share passwords or sensitive data
 - Always ask before transferring
 - Never transfer to wrong department
 - Always collect: name, phone, email, account number, issue type
 - Never proceed without all information
 - If customer is angry, de-escalate
 - If customer asks about products, use product_lookup function
 - If customer asks about orders, use order_status function
 - If customer asks about billing, use billing_lookup function
 - Never make up information
 - Always use functions for data
 - Never guess at customer information
 - If you don't know, say you don't know
 - Always confirm understanding before acting
 - Never assume what customer wants

 TRANSFER RULES:
 - Sales: new customers, product questions, pricing
 - Support: technical issues, how-to questions
 - Billing: payments, invoices, refunds
 - Never transfer to sales if existing customer
 - Never transfer to support if billing question

 EDGE CASES:
 - If customer says "um" or "uh", ignore it
 - If customer is silent for 3 seconds, prompt them
 - If customer interrupts, let them finish
 - If call quality is poor, ask them to repeat

 [... continues for another two pages ...]

What happens:

• The agent becomes paralyzed by contradictory instructions

• Response time increases

• Natural conversation flow breaks down

• Edge cases still happen—you can't predict them all

What’s the minimal prompt that still works?

The same agent can do the same job with far less instruction:

prompt: |
 You're a helpful customer service agent for Acme Corp.

 Greet callers and help with their questions.

 Before sharing account information, verify their identity with the verify_account function.

 If you can't help directly, transfer to the right department:
 - Sales: product and pricing questions
 - Support: technical help

What changes:

• Agent has room to think and adapt

• Natural conversational flow

• Edge cases handled naturally by the LLM's training

• Much faster responses

Why do longer prompts reduce reliability?

LLMs have a context window, but they also have cognitive limits. Think of it like talking to someone while they're reading a manual. The more rules they're trying to remember, the less they can focus on the conversation.

Bad: 47 rules, 12 edge cases, 23 "never/always" statements
Result: Robotic, slow, error-prone

Good: 3 key objectives, 1 critical rule
Result: Natural, fast, adaptive

When should you split one agent into many?

Instead of one super-agent that does everything, create focused agents.

Reception agent:

prompt: |
 You're the receptionist for Spacely Sprockets.

 Greet callers and route them to the right place:
 - Sales: new customers, product questions
 - Support: existing customers with issues
 - George Jetson: personal calls for George

 Get their name before transferring.

Support agent:

prompt: |
 You're a technical support specialist.

 Help customers troubleshoot issues with their sprocket installations.

 Use the knowledge_base function to look up solutions.

 If you can't resolve it, create a ticket with the create_ticket function.

Each agent is simple, focused, and effective.

Real-world example: Pizza ordering

A pizza ordering agent has prices, toppings, delivery rules, and business hours to manage. The temptation to cram it all into the prompt is strong.

Over-prompted version (doesn't work well):

prompt: |
 You take pizza orders. Here are the rules:
 - Small pizza: $10, Medium: $15, Large: $20
 - Toppings: pepperoni, sausage, mushrooms, peppers, onions ($1 each)
 - Never suggest items not on menu
 - Always confirm order before processing
 - Always get: name, phone, address, special instructions
 - Never forget to ask about drinks
 - Always upsell at least once
 - Calculate total before confirming
 - Small serves 1-2, Medium serves 2-3, Large serves 3-4
 - If they order wrong size, suggest correct size
 - Delivery fee $3 within 5 miles, $5 beyond
 - Delivery time 30-45 minutes
 - We open at 11am, close at 10pm
 - If outside hours, take name and callback
 - [... continues ...]

Minimal version (works great):

prompt: |
 You take pizza orders for Mario's Pizza.

 Help customers choose their pizza and toppings.
 Get their name, phone, and delivery address.

 When the order is ready, use the place_order function.

 We always have pizza in stock.

functions:
 - name: place_order
 purpose: "Submit the completed order"
 parameters:
 - name: customer_name
 - name: customer_phone
 - name: delivery_address
 - name: items
 description: "Array of pizza items with size and toppings"

 - name: get_menu_prices
 purpose: "Look up current menu prices and options"

Why this works:

• Menu details handled by function—change prices without touching the prompt

• Calculations handled by function—no math errors

• Natural conversation about food

• Function enforces required fields

When should you move information into functions?

Instead of cramming knowledge into prompts, use functions:

Don’t do it in a prompt:

prompt: |
 Our products:
 - Widget Pro: $299, 5-year warranty, blue or red
 - Widget Plus: $199, 3-year warranty, blue only
 - Widget Basic: $99, 1-year warranty, gray only

 If customer asks about Widget Pro, mention 5-year warranty.
 If budget constrained, suggest Widget Plus.
 If student, offer 10% discount on Basic.

Use a function:

prompt: |
 Help customers find the right Widget for their needs.
 Use the product_lookup function to get current pricing and options.

functions:
 - name: product_lookup
 purpose: "Get product details, pricing, and current promotions"
 web_hook: "https://yourserver.com/products"

Your server returns current, accurate data. Update prices anytime without touching the prompt.

How to know you're over-prompting

Warning signs:

1. Your prompt is over 500 words

2. You have more than 10 bullet points

3. You're using "never" or "always" more than twice

4. You have IF/THEN logic in natural language

5. Your agent's responses are getting slower

6. You keep adding rules to fix edge cases

7. The agent ignores some instructions (cognitive overload)

How to refactor an over-prompted AI agent

If you've already built an over-prompted agent, here's how to slim it down:

Step 1: Extract data to functions

• Prices, schedules, policies → API lookups

• Calculations → server-side logic

• Business rules → function validation

Step 2: Remove redundancy

• LLMs know to be polite (don't tell them)

• LLMs handle "um" and "uh" naturally (don't tell them)

• LLMs ask clarifying questions (don't tell them)

Step 3: Focus on unique requirements

• Your specific workflow

• Your brand voice

• Your critical edge cases (only the important ones)

Step 4: Test and iterate

• Remove one rule at a time

• Test if behavior changes

• If behavior stays good, keep it removed

How do you emphasize one critical rule without prompt bloat?

Sometimes you really do need to emphasize something. Pick ONE thing:

prompt: |
 You're a support agent for MedicalCorp.

 Help patients schedule appointments and answer questions.

 IMPORTANT: Never share medical information without verifying patient identity
 using the verify_patient function first.

One critical rule stands out. Twenty critical rules means nothing is critical.

The pattern is clear: shorter prompts create faster, more natural responses. Move data and logic to functions. Trust the LLM's training for common conversational patterns—it already knows how to be polite and handle filler words. Focus your prompt on what makes your agent unique. When complexity grows, split into multiple focused agents.

Try this with your own agents. Open your longest prompt. Highlight everything that's data—that should move to functions. Cross out everything that's common sense—the LLM already knows it. Circle what's truly unique to your use case. Rewrite with only the circled items. Test and compare.

Read the whole series

This article is part two of a three-part series on AI agent prompting best practices:

• Treat Your AI Agent Like a Person, Not a Program

• Less is More: Why Over-Prompting Kills Your AI Agent

• The RISE-M Framework: Structure Your AI Agent Prompts for Success