Pandas groupby: change values in one column based on values in another column

I'll try to explain my problem as best as possible. But I'm new to Pandas, so please bear with me. I have a Pandas dataframe df:

    Random_ID   Seq_ID  Type    Seq Token
0   8             1     User    First
1   8             2     Agent   Second
2   8             3     User    Second
3   8             4     User    Second
4   8             5     Agent   Second
5   13            1     User    First
6   13            2     Agent   Second
7   13            3     User    Second
8   13            4     Agent   Second
9   13            5     User    Second
10  13            6     Agent   Second
11  13            7     User    Second
12  13            8     Agent   Second
13  13            9     User    Second
14  13            10    Agent   Second

I have been trying to change the values of Seq Token(User_First, Agent_Last...) based on the occurrence of User and Agent in Type in each group of df.groupby('Random_ID'). To illustrate further, take the last row of each group:

grouped = df.groupby('Random ID').last()

which gives:

          Seq_ID       Type     Seq Token
Random_ID           
8          5           Agent    Second
13         10          Agent    Second

Here, if Type=Agent, then Seq token should be Agent_Final. Then, the df should look like:

    Random_ID   Seq_ID  Type    Seq Token
0   8             1     User    First
1   8             2     Agent   Second
2   8             3     User    Second
3   8             4     User    Second
4   8             5     Agent   Agent_Final
5   13            1     User    First
6   13            2     Agent   Second
7   13            3     User    Second
8   13            4     Agent   Second
9   13            5     User    Second
10  13            6     Agent   Second
11  13            7     User    Second
12  13            8     Agent   Second
13  13            9     User    Second
14  13            10    Agent   Agent_Final

I've tried the following:

grouped = df.groupby('Random_ID', as_index=False).last()['Type']
for i in grouped:
    if i == 'Agent':
        df['Seq Token'] =  'Agent_Final'

but this assigns all items in Seq token as 'Agent_Final':

     Random_ID  Seq_ID  Type    Seq Token
0       8         1     User    Agent_Final
1       8         2     Agent   Agent_Final
2       8         3     User    Agent_Final
3       8         4     User    Agent_Final
4       8         5     Agent   Agent_Final

I read about groupby and it creates a copy of the original df and does not allow changing it unless one explicitly changes a df[column]. I hope this makes sense.

I've managed to set the first row "group values" to "First" using np.where() like this:

df['Seq Token'] = np.where((np.logical_and(np.equal(df['Type'],'User'), np.equal(df['Seq_ID'],1))), 'First', 'Second')

You can see that I've implemented this in the df already. Note that I used the Seq_ID value to get the first row in the group.

If there is a way to chain np.where() in such as way that I can assign Seq Token as User_First(same as First), User_Middle(if Type=User occurs in the middle), Agent_Middle(if Type=Agent occurs in the middle), Agent_Last(as explained above: if Agent is last), then it would be the most ideal solution. However, any other solutions are welcome too.

Thanks in advance!