Threshold Agent Response Tuning Exercises

These hands-on exercises help learners understand how to modify and tune assistant behavior in the Threshold Memory template by editing key blocks. You'll learn how to change how the agent evaluates, explains, and responds to policy violations.

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Objectives

• Learn which blocks control logic, memory, and response formatting

• Customize fallback behavior and explanation style

• Practice modifying output to match business tone or policy emphasis

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Blocks to Explore

Block Name	Role in the Workflow	Tips for Editing
Metadata Assigner	Sets key memory variables and conversation metadata	Add tags like review_required or flagged
Expense Evaluation	Runs comparison logic (amount vs. threshold, vendor checks)	Modify IF/ELSE branches or add filters
Fallback Reasoning	LLM-based explanation block for ambiguous or missing logic	Change prompt tone or level of formality
Final Decision	Outputs APPROVED, REJECTED, ESCALATE with reason	Customize wording or formatting

Exercise 1: Change How Violations Are Explained

🎯 Goal: Make fallback explanations more formal, structured, and compliance-focused — especially when policy violations are detected.

This block is responsible for explaining whether a receipt is approved or rejected based on policy thresholds. To modify how violations are communicated, click into PROCESS REQUEST and edit the SYSTEM prompt tone — make it more formal, friendly, or tailored to your organization’s communication style.

✅ Steps

1. Open the LLM Block (currently titled "LLM")

2. Locate the SYSTEM message under the Context section

3. Modify the final explanation sentence of the <Example> block to read:

   Reason: This expense violates policy due to exceeding the threshold without an exception rule. A formal review is required for compliance logging.

   (This replaces the current: “No exception rule found. Logged for escalation or review.”)

4. Optionally, tweak the opening sentence of the SYSTEM prompt to reinforce tone:

   You are a compliance-focused corporate accountant. Review the receipt and policy to determine validity using formal, transparent justifications.

5. Save and re-test using: Receipt_4_Only.pdf (This file contains a suspicious invoice split pattern and exceeds the global threshold)

The LLM is used here as a fallback explanation agent, especially when the IF/ELSE blocks don’t capture ambiguous or edge-case logic. This edit lets learners experiment with output tone, formality, and compliance narrative structure — without altering business logic.

Exercise 2: Tone Tuning in the Fallback Reasoning Block

Goal

Teach users how to modify the fallback LLM block’s response tone when no policy match is found or ambiguity arises (e.g. split invoices, unclear categories).

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Why This Matters

• Helps enterprise users adjust how AI communicates policy enforcement (e.g. strict vs supportive tone)

• Ensures responses align with internal brand or compliance voice

• Enables better UX when agents must reject or escalate expenses

To customize how the assistant responds in ambiguous or policy-missing cases, click into this block and edit the system prompt tone. Try adjusting language for different enterprise communication styles (e.g. formal, supportive, strict).

Steps

1. Open the LLM Fallback Block

   • Locate the block that handles ambiguous or missing-policy cases (typically tagged <policy> and <receipts>)

2. Identify the Current Prompt

   • Look for something like:

     Please review the following receipt in the context of the policy:
     <policy>{{policy_memory}}</policy>
     <receipts>{{receipt_text}}</receipts>

3. Modify the Tone

   • Try rewriting the prompt in one of the following styles:

     • 🔍 Strict:

       “This expense appears to violate corporate policy and will be flagged for immediate rejection.”

     • 💼 Professional:

       “Based on the provided policy, this expense may not be compliant. Please advise.”

     • 🤝 Supportive:

       “This expense might fall outside standard policy. Would you like me to escalate for review?”

4. Save and Re-run a Test

   • Use a borderline case like Receipt_4_Only.pdf

   • Observe how the assistant’s tone changes based on the new prompt

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✅ Success Criteria

• Output clearly reflects new tone

• System still uses memory-driven policy context

• Learner understands where and how to control fallback behavior

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Exercise 3: Add Logic to Escalate Alcohol Violations

Goal

Escalate receipts that include alcohol, even if thresholds are not exceeded.

Steps

1. Open Expense Evaluation

2. Add a condition:

   If invoice_data.contains_alcohol == true
     → Set `approval_result = ESCALATE`

3. Upload Receipt_3.pdf and confirm escalation

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Success Criteria

• Agent response is visibly changed (different tone, tags, or decision path)

• conversation_notes reflect updated explanations

• Output is aligned with edited logic and assistant behavior

Try adjusting block logic to fit different organizational policies or tone-of-voice standards!