Agent Memory Corruption

The Problem

Agent's conversational memory becomes corrupted or inconsistent, causing it to forget context, hallucinate past interactions, or contradict itself.

Symptoms

• ❌ Agent forgets previous conversation

• ❌ Refers to things never discussed

• ❌ Contradicts earlier statements

• ❌ Context window overflow corrupts memory

• ❌ Memory persists incorrectly across sessions

Real-World Example

Turn 1:
User: "I'm working on Project Phoenix"
Agent: "Great! Project Phoenix is your team's OAuth integration."

Turn 2 (5 turns later):
User: "What was I working on?"
Agent: "I don't have information about your current project."

Memory lost - Agent forgot "Project Phoenix"

Or worse:
Agent: "You mentioned Project Apollo earlier"
→ Hallucination - user never mentioned Apollo
→ Memory corruption

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Deep Technical Analysis

Memory Storage Issues

Context Window Overflow:

8K context window:
→ Conversation history: 6,000 tokens (growing)
→ System prompt: 500 tokens
→ Current query + response: 2,000 tokens
→ Total: 8,500 tokens → Overflow!

Result:
→ Oldest turns truncated
→ Agent loses early context
→ Forgets user's initial question

No Explicit Memory:

Stateless API calls:
→ Each request independent
→ No persistent memory

Application must:
→ Track conversation history
→ Pass to each API call
→ Manage truncation

If not implemented:
→ Agent has amnesia

Memory Retrieval Failures

Semantic Memory Lookup:

Instead of full conversation history:
→ Embed past turns
→ Retrieve relevant past context

Query: "What project am I on?"
→ Retrieve: Turn 1 ("Project Phoenix")
→ Answer: "Project Phoenix"

But if retrieval fails:
→ Relevant turn not retrieved
→ Agent can't answer

Memory Priority:

Recent turns more important:
→ Last 3 turns: Always include
→ Older turns: Retrieve if relevant

Balance:
→ Recency (what just discussed)
→ Relevance (related past context)

Hallucinated Memory

LLM Confabulation:

User: "What did I say earlier about authentication?"
Agent searches memory: No mention of authentication

But LLM generates:
"You mentioned using OAuth for authentication"
→ Hallucinated memory
→ User never said this

Dangerous: Fabricates past conversation

Cross-Session Leakage:

User A session: Discussed Project Phoenix
User B session: Different user

Agent in User B session:
"As we discussed, Project Phoenix uses OAuth..."
→ Leaked User A's context to User B

Memory isolation failure

Memory Compression

Summarization:

Long conversation (50 turns):
→ Raw: 15,000 tokens (too large)

Compress:
→ Summarize early turns
→ "User asked about API authentication. Agent explained OAuth flow."
→ Compressed: 30 tokens

Trade-off:
+ Fits in context window
- Loses detail

Selective Retention:

Identify important turns:
→ User provides key info (name, project, role)
→ Agent makes commitments
→ Errors/corrections

Retain these verbatim:
→ Less important turns: Summarize or drop

Memory Validation

Fact-Checking Memory:

Agent recalls: "You said your rate limit is 500/hour"

Validate:
→ Check conversation history
→ User actually said: "1000/hour"
→ Mismatch detected

Correct:
→ "Apologies, I misremembered. You said 1000/hour."

Memory Consistency:

Track facts mentioned:
→ User's name: Alice
→ User's team: Engineering
→ User's project: Phoenix

If agent later says:
→ "Your project, Apollo..."
→ Contradiction: Phoenix != Apollo
→ Flag: Memory corruption

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How to Solve

Implement explicit memory storage (database or vector DB for past turns) + use semantic retrieval for relevant past context + compress old turns via summarization + always include recent 3-5 turns + validate memory recalls against stored history + isolate memory by session/user (no cross-session leakage) + detect memory hallucination (check if recalled info actually exists) + monitor memory consistency (no contradictions) + expire memory after session timeout. See Memory Management.