Issue: Unable to define "output_key" param of the LLM chain class for a csv agent while binding to SequentialChain class
Labels: name: area: agent, name: auto:bug, name: auto:question

### Issue you'd like to raise.

Currently, I'm using SequentialChain class to combine two steps in my workflow. 

Step1: I'm using the LLM through prompt to identify the intent of the question posed by the user.

Step2: I'm using the csv based agent to answer the question posed by the user based on the csv file, but my aim is to answer the question only if the intent of the question is a textual response. 

Below is the code snippets I have used to create the SequentialChain

`model = AzureOpenAI(temperature=0,deployment_name="",openai_api_key="",openai_api_version="",openai_api_base="",
)`

`template = """
        You will help me identify the intent with the following examples and instructions.
        Give your response in this format {{"Intent":"<identified intent>",
                            "Question":"<Input question>"}}
        ### Instructions
        # Different possible intents are textResponse, BarChart, LineChart.
        # If the question doesn't come under any of the intents, identify it as a None intent.
        ####
        ### Examples
        Question: What is the total count of stores in 2022?
        Intent: textResponse
        Question: What is the split of sale amount for each sale type?
        Intent: BarChart
        Question: What is the monthly trend of sales amount in 2022?
        Intent: LineChart
        Question: {input}
        """`

`prompt = PromptTemplate(
        input_variables=["input"],
        template=template,
        )`

`chain_one = LLMChain(llm=model, prompt=prompt, output_key = "intent")`


`agent = create_csv_agent(
    AzureOpenAI(temperature=0.5,top_p = 0.5,deployment_name="",openai_api_key="",openai_api_version="",openai_api_base="",),
    <csv file path>,
    verbose=True,
    agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    handle_parsing_errors=True
)`

`agent.agent.llm_chain.prompt.template = """
You are a friendly assistant who is warm and thoughtful and never rude or curt.
Possible Intents are: textResponse, LineChart, BarChart, None
You will act further only if the {intent} is textResponse, else your Final Answer will be I cannot respond to your query.
If {intent} is textResponse use the python_repl_ast to answer the question.
You should use the tools below to answer the question posed of you:
python_repl_ast: A Python shell. Use this to execute python commands.
You should use the python_repl_ast to answer the question posed of you. You are working with a pandas dataframe in Python. 
The name of the dataframe is `df`.
Input to python_repl_ast should be a valid python command.
Give your Final Answer in this format {{"output":"Final Answer"}}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do.
Action: the action to take, should be one of [python_repl_ast]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question.
This is the result of `print(df.head())`:{df_head}
Begin!
Question: {input}
{agent_scratchpad}
    """`

`input_var = agent.agent.llm_chain.prompt.input_variables`

`input_var.append("intent")`

This was done to append my input variables to already pre-defined ones for the csv agent.

`agent.agent.llm_chain.output_key = "FinalAnswer"`

`chain_two = agent`

`overall_chain = SequentialChain(chains=[chain_one, chain_two],input_variables=["input"],
                  output_variables=["intent","FinalAnswer"],verbose=True)`

`overall_chain.run(input = "count of total stores in 2022")`

Now, when I run the above code I get the following error:

**validation error for SequentialChain __root__ Expected output variables that were not found: {'FinalAnswer'}. (type=value_error)**

As far as I understood the langchain documentation [(https://python.langchain.com/docs/modules/chains/foundational/sequential_chains)]

the output_key must be defined for each LLM hit for the model to tag the response to that key, hence here I have provided the output key to the agent through the llm_chain.output_key property. But still the code throws error that output variables were not found. 

Is this a bug in langchain while binding the csv agents to SequentialChain class or am I missing something? Can someone please help? 

### Suggestion:

_No response_