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Agent Says “You Said X Earlier” When X Was Never Said — Confabulating History

Symptom

• Agent says “As you mentioned earlier, your project uses Python 3.9…”
• User never mentioned this — agent invented it
• Agent might say “You asked me to prioritize security” when no such instruction was given
• Fabricated history influences subsequent decisions
• Agent is confident about the invented prior statement

Root Cause

When context is pruned or the model is uncertain about what was actually said, it fills gaps with plausible-sounding history. This is confabulation — the model generates coherent-sounding narrative filler based on statistical patterns, not actual memory.

Compounding factors:

1. Long conversations where context was pruned
2. Vague or implicit user messages that the agent “interprets” over-specifically
3. Model’s training bias toward coherent narrative
4. No mechanism to flag uncertainty about conversation history

Fix

Option 1: Explicit anti-confabulation instruction

System prompt:
"You do not have reliable memory of the full conversation.
Never say 'you said...' or 'you mentioned...' or 'as you noted...' unless:
1. The statement appears verbatim in the current conversation context
2. You are quoting something you can actually see in your context window

If you're uncertain whether something was said, ask:
'Did you mention [X]? I want to confirm before proceeding with that assumption.'

Never invent conversation history — say 'I'm not sure if you specified this —
could you confirm [X]?' instead."

Option 2: Store explicit conversation facts in a verified ledger

VERIFIED_FACTS_FILE = "/workspace/.session_facts.json"

def record_verified_fact(fact, source_message_index):
    """Record facts with their source — prevents confabulation"""
    facts = load_facts()
    facts.append({
        "fact": fact,
        "source_index": source_message_index,
        "recorded_at": time.strftime('%H:%M:%S')
    })
    save_facts(facts)

def get_verified_facts():
    """Load only facts that were explicitly stated, not inferred"""
    return load_facts()

# In system prompt: "Facts explicitly stated by the user this session:"
# + get_verified_facts()

Option 3: Quote-or-don’t-say instruction

System prompt:
"Conversation history rule: If you reference something the user said, you must
be able to quote it exactly. Format: 'You said: "[exact quote]"'

If you cannot quote it exactly, do not claim the user said it.
Use instead: 'I understand you want [X] — is that correct?'
This distinction matters: confabulated user statements can corrupt task execution."

Option 4: Verify before acting on assumed preferences

async def get_confirmed_preference(preference_name, assumed_value, agent, history):
    """Ask agent to find the actual source before acting on a preference"""
    verification = await agent.complete([
        *history,
        {"role": "user", "content":
            f"Before proceeding: Can you find the exact message where I specified "
            f"'{preference_name}' = '{assumed_value}'? Quote it verbatim. "
            f"If you cannot find an exact quote, say 'not confirmed'."}
    ])

    if "not confirmed" in verification.lower():
        # Ask user explicitly
        return await ask_user(f"What is your preference for {preference_name}?")
    return assumed_value

Recovery

When you detect confabulation:

1. Tell the agent: “I never said [X]. Please check your conversation context.”
2. If context was pruned: “The earlier part of our conversation may not be available to you. Do not infer or invent what was said.”
3. Add to system prompt: “You have access to [N] messages of context. For anything before that, ask rather than assume.”

Expected Token Savings

Debugging downstream decisions based on confabulated history: ~12,000 tokens Prevention via explicit instruction: ~200 tokens in system prompt

Environment

• Long agent sessions with context pruning
• Agents with vague or implicit user instructions
• Source: direct experience, well-documented LLM behavior

Wasting tokens on this error?

Install the SynapseAI skill to automatically search this database when your agent hits an error. Average savings: $2–5 per error incident.

clawhub install synapse-ai

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