Training Best Practices for Your AI Agent 

Training Quack AI Agents is less about writing perfect prompts and more about teaching a teammate how to think, decide, and act in the right situations. When training is too vague, inconsistent, or rushed, agents may escalate incorrectly, or produce uneven answers.

This guide outlines practical best practices and examples to help you train agents faster, with higher accuracy and far less confusion.

1. Start With Broad but Precise Guidelines

When giving instructions to Quack, always include enough context so the rule applies only in the correct situations.

Poor guideline example: 
"If the customer cannot connect, escalate"

Why this fails: 
"Cannot connect" could mean login issues, temporary outages, incorrect credentials, or user error: leading to unnecessary escalations.

Improved guideline example: 
 "If the customer cannot connect their ticketing system to Quack after completing all required troubleshooting steps and the issue persists, then escalate to a human agent."

Best practices:

  1. Define conditions, not just outcomes.

  2. Specify prerequisites before escalation. 

  3. Clarify exceptions when escalation should not happen. 

  4. Use real support language that your agents already use, and your customers know.

  5. Explain all acronyms and internal terms. 

  6. Use a clear IF / THEN / ELSE structure whenever one customer term can map to multiple intents.

Example:
“If a customer asks whether an SLA (Service Level Agreement) was breached, first ask for the ticket number and then send the resolution timeframes."

"IF the user mentions being an ‘affiliate’ or ‘ambassador’ THEN ask: ‘Are you a current affiliate or are you looking to apply?’ ELSE continue with the standard support flow."

2. Train One Concept at a Time

Avoid teaching multiple behaviors or decision points in a single training step. Quack learns most effectively when each rule or behavior is introduced in isolation.

Instead of Training login issues, account recovery, and escalation logic all at once.

Do this:

  • Train "Cannot access account" as a standalone case. 

  • Train troubleshooting steps as a separate case. 

  • Train escalation criteria only after troubleshooting is complete.

  • This mirrors how human agents are trained and produces cleaner decision-making paths.

3. Provide Positive Feedback Only Once Per Question

Once you test a question or flow, wait until you are fully satisfied with the response before giving positive feedback.

Multiple "good" feedback entries for different answers to the same question can introduce contradictions, and conflicting reinforcement can reduce accuracy and consistency.

Best practices:

  1. Iterate internally until the answer meets your expectations.

  2. Submit positive feedback only on the final, correct behavior. 

  3. If you notice older or incorrect feedback influencing results, use the "Forget it" option to remove it.

4. Train Sequential Flows as Separate Scenarios

Complex conversations often involve multiple turns and branching paths. Training them all at once reduces learning efficiency.

Example flow: 
"I can't access my account" → Agent sends troubleshooting steps → Customer replies: "I still can't access"

Recommended training approach: 
Scenario 1: "I can't access my account" → correct troubleshooting response. 
Scenario 2: "I already tried the following troubleshooting steps and still can't access my account" → next-step resolution or escalation.

This allows Quack to recognize prior context explicitly rather than infer it.

5. Use Realistic, High-Signal Inputs

Train using messages that resemble what customers actually write, not idealized or overly clean phrasing.

Instead of:  "User reports login failure"
Use:  "I tried logging in three times and it keeps saying something went wrong" , "Your app won’t let me sign in since yesterday"

This improves recognition across channels and reduces false negatives.

6. Combine Clear Actions With Targeted Guardrails

It is OK and often necessary to include negative rules, especially for security, compliance, or sensitive actions. However, before adding them, ensure that there is no source, brief or instruction causing the behavior. If not, you can add a new rule. Keep in mind that negative rules work best when they are paired with clear instructions on what the agent should do instead.

Use this hierarchy when training:

  1. First, define the correct action or decision flow.

  2. Then, add negative rules only where risk, policy, or ambiguity exists.

Examples:

1. Negative rule alone (less effective):
“Do not ask for passwords or sensitive credentials.”

Improved combined guidance:
 “When authentication is required, guide the customer through the official password reset or verification flow. Never request passwords or sensitive credentials directly.”

2. Negative rule alone:
 “Do not escalate for first-time login errors.”

Improved combined guidance:
 “For first-time login errors, provide troubleshooting steps. Escalate only if the issue persists after all steps are completed.”

This blended approach keeps Quack flexible and intelligent while still enforcing the boundaries that matter most.

7. Test, Then Expand Coverage Gradually

Avoid testing dozens of variations before you are satisfied with the core behavior. 

Recommended workflow: 

  1. Train one core scenario.

  2. Test 3-5 realistic variations.

  3. Lock in correct behavior with a single positive feedback. 

  4. Expand to edge cases and adjacent topics.

This reduces noise and accelerates accuracy gains.

Pro Tip

Train Quack the same way you would onboard a new agent: clear expectations first, scoped scenarios second, feedback only when confident, and continuous coaching over time.