diff --git a/dataherald/sql_generator/dataherald_sqlagent.py b/dataherald/sql_generator/dataherald_sqlagent.py
index e7b3cdbd..64b949ba 100644
--- a/dataherald/sql_generator/dataherald_sqlagent.py
+++ b/dataherald/sql_generator/dataherald_sqlagent.py
@@ -10,7 +10,6 @@
 import openai
 import pandas as pd
 import sqlalchemy
-import tiktoken
 from bson.objectid import ObjectId
 from google.api_core.exceptions import GoogleAPIError
 from langchain.agents.agent import AgentExecutor
@@ -41,7 +40,6 @@
     DatabaseConnection,
 )
 from dataherald.sql_generator import EngineTimeOutORItemLimitError, SQLGenerator
-from dataherald.sql_generator.adaptive_agent_executor import AdaptiveAgentExecutor
 from dataherald.types import Question, Response
 from dataherald.utils.agent_prompts import (
     AGENT_PREFIX,
@@ -53,7 +51,6 @@
     SUFFIX_WITH_FEW_SHOT_SAMPLES,
     SUFFIX_WITHOUT_FEW_SHOT_SAMPLES,
 )
-from dataherald.utils.models_context_window import OPENAI_CONTEXT_WIDNOW_SIZES
 
 logger = logging.getLogger(__name__)
 
@@ -585,24 +582,15 @@ def create_sql_agent(
             input_variables=input_variables,
         )
         llm_chain = LLMChain(
-            llm=self.short_context_llm,
+            llm=self.llm,
             prompt=prompt,
             callback_manager=callback_manager,
         )
         tool_names = [tool.name for tool in tools]
         agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
-        return AdaptiveAgentExecutor.from_agent_and_tools(
+        return AgentExecutor.from_agent_and_tools(
             agent=agent,
             tools=tools,
-            llm_list={
-                "short_context_llm": self.short_context_llm,
-                "long_context_llm": self.long_context_llm,
-            },
-            switch_to_larger_model_threshold=OPENAI_CONTEXT_WIDNOW_SIZES[
-                self.short_context_llm.model_name
-            ]
-            - 500,
-            encoding=tiktoken.encoding_for_model(self.short_context_llm.model_name),
             callback_manager=callback_manager,
             verbose=verbose,
             max_iterations=max_iterations,
@@ -622,15 +610,10 @@ def generate_response(
         start_time = time.time()
         context_store = self.system.instance(ContextStore)
         storage = self.system.instance(DB)
-        self.short_context_llm = self.model.get_model(
+        self.llm = self.model.get_model(
             database_connection=database_connection,
             temperature=0,
-            model_name=os.getenv("LLM_MODEL", "gpt-4"),
-        )
-        self.long_context_llm = self.model.get_model(
-            database_connection=database_connection,
-            temperature=0,
-            model_name=os.getenv("AGENT_LLM_MODEL", "gpt-4-32k"),
+            model_name=os.getenv("LLM_MODEL", "gpt-4-1106-preview"),
         )
         repository = TableDescriptionRepository(storage)
         db_scan = repository.get_all_tables_by_db(