AI Agent Meaning Drift — Case Study

How interpretation changes across safety layers, memory compression, and constraint handling

---

Demo Screenshot

---

Try These Examples in the Meaning Drift Sandbox

Selct one of the prompts below from the dropdown in the Meaning Drift Sandbox, click Simulate Interpretation, and compare:

• Current System Interpretation (left)
• Stabilized Contract Model (right)

---

Example 1 — Formatting, tone, structure

Prompt:
Summarize this document in exactly 5 bullet points.
Keep my original section headings.
Do NOT change the tone at all.
Respond in a Markdown table with columns for Section, Key Insight, and Risk.

What to watch for:
Left: table + tone + headings often dropped
Right: all constraints preserved verbatim

---

Example 2 — Sensitive but safe content

Prompt:
Analyze a fictional conflict:
“Alice accused Bob of lying about the report due date, but Bob genuinely misunderstood the instructions.”
Focus only on interpersonal patterns.
Do NOT provide legal or psychological advice.
Return 3 neutral bullets.

What to watch for:
Left: over-applies safety → generic advice
Right: preserves all constraints

---

Example 3 — Long instruction with edge constraints

Prompt:
Rewrite the policy document for clarity.
Do NOT shorten it.
Do NOT remove obligations.
Keep numbered sections the same.
Add a 4-bullet summary using original terminology.

What to watch for:
Left: memory compression drops constraints
Right: constraint retention

---

Example 4 — Design prompt with tight style rules

Prompt:
Generate 3 brand taglines.
Tone: soft, minimal, elegant.
Format: “Tagline — short rationale”.
No emojis or exclamation marks.
Under 20 words total.

What to watch for:
Left: tone preserved, limits lost
Right: limits + tone preserved

---

Example 5 — Full interpretation drift scenario

Prompt:
Provide a 4-step troubleshooting flow for:
“My team keeps misunderstanding each other’s Slack messages.”
Tone neutral.
No psychology.
Numbered list.
Add a meta-summary starting with: “Interpretation drift occurs when…”

What to watch for:
Left: formatting + constraints dropped
Right: all retained

---

1. What Is Meaning Drift?

Meaning drift occurs when the model responds to a different version of the user input.

Common symptoms:

• safe prompts treated as risky
• narrowed/reframed intent
• dropped formatting or constraints
• tone shifts
• hallucinated permissions or rules

---

2. Likely Contributing Factors

A. Safety Layers

Risk scoring alters inferred intent.

B. Policy Interpretation

Global rules override local user context.

C. Memory Compression

Summaries drop constraints, tone, or logic.

D. Reasoning Layer

Model reasons on transformed input.

---

3. System View: Current Interpretation Flow

[User Instruction]
       ↓
[Safety Layer]
       ↓
[Policy Interpretation]
       ↓
[Memory Compression]
       ↓
[Model Reasoning]
       ↓
[Output — drift introduced]

---

4. Stabilizing Interpretation: Proposed Contract Model

Meaning drift cannot be fixed with prompting alone.
It requires product and architectural changes.

A. Explicit Intent Contract (EIC)

Use this shape (escaped so GitHub won’t break the fence):

{  
  "intent_type": "analysis",  
  "risk_tolerance": "literal",  
  "style": "direct"  
}

B. Memory Invariant Layer

User-declared constraints persist verbatim unless changed.

C. Safety Layer Contract

Safety blocks unsafe tasks but must not reinterpret safe instructions.

---

5. Stabilized Interpretation Flow

[User Instruction] + [Intent Contract]
           ↓
[Safety Check — block/allow only]
           ↓
[Memory Invariants — constraints preserved]
           ↓
[Model Reasoning — stable interpretation]

---

6. Why This Matters

Unstable interpretation:

• breaks UX predictability
• causes inconsistent long-form reasoning
• increases false refusals
• destabilizes trust
• complicates integration across tools
• undermines professional workflows

Stable interpretation is a prerequisite for aligned AI systems.

---

7. About This Case Study

This is independent analysis based solely on publicly observable behavior of modern AI systems.
It does not claim internal insight.

This repo aims to:

• map visible drift patterns
• propose stabilizing architectural shapes
• demonstrate PM-level reasoning through prototypes
• make interpretation drift legible

---

Part of the Product Architecture Case Studies Series

Main hub:
https://github.com/rtfenter/Product-Architecture-Case-Studies

---

8. Author

Rebecca Fenter (rtfenter)
Product Manager — systems, platforms, AI architecture
https://github.com/rtfenter