How to Fix LangChain Agents Drifting Away from Instructions
Stop autonomous agents from wandering off-topic or executing unexpected tools.
LangChain agent drift happens when conversational memory fills the context window and pushes system constraints out of focus. Exogram stops drift by enforcing invariant boundaries on every tool invocation, ensuring the agent cannot execute unapproved actions regardless of conversational history.
What Actually Happened in the Real World
A customer-service LangChain agent was tasked with helping users book hotel rooms. A user began asking about local political controversies, and the bot spent 15 turns debating city ordinances, eventually offering an unauthorized VIP discount to end the argument.
Why Polite Prompts Like “Please Don't Do This” Fail
Instructing the agent to "stay focused on booking" fails because once off-topic conversation dominates the prompt, the model continues generating in the dominant conversational style.
The Sub-Millisecond Code Fix
Exogram monitors tool invocation velocity and enforces strict purpose boundaries. If an agent attempts an action that deviates from allowable workflows, the execution is blocked.
from exogram import ExogramBoundary
boundary = ExogramBoundary(allowed_tools=["book_room", "check_availability"])
def agent_tool_router(tool_name, args):
boundary.assert_valid(tool_name, args)
return execute_tool(tool_name, args)Frequently Asked Questions
Does Exogram add latency to LangChain execution?
No. Local invariant checks execute in under 0.07ms.
Related Diagnostic Answers
Comparison Guides
Stop AI Mistakes Before They Execute
Exogram sits directly between your AI model and your tools. Set up in 10 seconds inside Claude, Cursor, ChatGPT, or your own code.