Agent Stops Following Instructions Mid-Conversation — System Prompt Instruction Drift
Symptom
- Agent follows instructions correctly in first 5–10 turns
- By turn 20+, agent ignores format requirements, breaks character, or expands scope beyond constraints
- Re-prompting “remember to…” temporarily fixes it, but drift recurs
- Problem worsens with longer conversations
Root Cause
The attention mechanism weights recent tokens more than distant tokens. A system prompt written at position 0 of a 100K-token context has significantly less influence on generation than the same instruction written at position 90K. This is sometimes called “lost in the middle” — instructions far from the generation point lose influence.
Fix
Option 1: Re-inject key instructions periodically
SYSTEM_PROMPT = """You are a technical support agent for SynapseAI.
CONSTRAINTS: Only answer questions about AI agent errors. Output in JSON only.
IDENTITY: You are Syn. Never break character."""
def build_messages(history):
messages = []
for i, msg in enumerate(history):
# Re-inject condensed instructions every 10 turns
if i > 0 and i % 20 == 0:
messages.append({
"role": "user",
"content": "[System: Reminder — follow all original instructions. "
"You are Syn. Output JSON only. Scope: AI agent errors only.]"
})
messages.append(msg)
return messages
Option 2: Append instruction anchor at end of system prompt
The model attends more strongly to the end of the system prompt than the beginning for long contexts:
SYSTEM_PROMPT = """
[... main instructions ...]
ALWAYS ACTIVE — regardless of conversation length:
- You are Syn, SynapseAI support agent
- Output format: JSON only
- Scope: AI agent errors only
- Do not break character or expand scope
"""
Option 3: Summarize + reset at context threshold
MAX_CONTEXT_TURNS = 30
async def maybe_reset_context(messages, system_prompt):
if len(messages) < MAX_CONTEXT_TURNS * 2:
return messages
# Summarize old context, start fresh
summary = await agent.complete(
f"Summarize this conversation history in 3 bullet points:\n{messages[:-10]}"
)
return [
{"role": "user", "content": f"[Context summary from earlier: {summary}]"},
{"role": "assistant", "content": "Understood. Continuing from that context."},
*messages[-10:] # Keep recent turns verbatim
]
Option 4: Use Anthropic’s system prompt caching
System prompt with cache_control: {"type": "ephemeral"} is cached and applied consistently regardless of conversation length. Use for long-running agent sessions.
Expected Token Savings
Debugging instruction drift across a session: ~10,000 tokens This fix: ~500 tokens
Environment
- Any LLM with long context window
- Problem intensifies above ~20K tokens of conversation history
- Source: direct experience with production agent sessions
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