A junior mortgage officer dealing with information consumption, danger screening, and ultimate choices alone is liable to errors as a result of the function calls for an excessive amount of without delay. The identical weak point seems in monolithic AI brokers requested to run advanced, multi-stage workflows. They lose context, skip steps, and produce shaky reasoning, which results in unreliable outcomes.
A stronger strategy is to construction AI as a supervised group of specialists that enforces order and accountability. This mirrors knowledgeable collaboration and yields extra constant, auditable choices in high-stakes domains like lending. On this article, we construct such a coordinated system, not as a single overworked agent, however as a disciplined group.
What’s a Supervisor Agent?
A supervisor agent is a particular not a task-performing agent, however somewhat the organizer of a group of different brokers engaged on a job. Take into account it as the pinnacle of the division of your AI labor pressure.
Its key tasks embrace:
- Activity Decomposition & Delegation: The supervisor takes an incoming request and decomposes the request into logical sub-tasks which is then forwarded to the suitable specialised agent.
- Workflow Orchestration: It’s strict so as of operations. Within the case of our mortgage evaluation, that suggests the retrieval of knowledge, coverage evaluation, and solely after that, a advice.
- High quality Management: It checks the efficiency of each employee agent to see whether or not it’s as much as the required commonplace earlier than the subsequent step.
- End result Synthesis: As soon as all of the employee brokers are completed, the supervisor takes the outputs of the employees and synthesizes them to offer a ultimate, coherent outcome.
The results of this sample is extra strong, scalable and simpler to debug techniques. The brokers are given one job and this simplifies their logic and will increase their efficiency stability.
Fingers-On: Automating Mortgage Evaluations with a Supervisor
The system of the primary evaluation of mortgage functions automation is now being constructed. We intention to take the ID of an applicant, consider them by way of firm danger insurance policies, and advise on a concise motion to be taken.
Our AI group will include:
- Case Consumption Agent: Entrance-desk specialist. It collects the monetary info of the applicant and develops a abstract.
- Danger Coverage Checker Agent: The analyst. It matches the knowledge of the applicant with a sequence of pre-established lending standards.
- Lending Choice Agent: The choice maker. It takes the discoveries and suggests a ultimate plan of action corresponding to approving or rejecting the mortgage.
- The Supervisor: The supervisor who does the entire workflow and ensures that each agent does one thing in the suitable sequence.
Let’s construct this monetary group.
Step 1: Set up Dependencies
Our system can be based mostly on LangChain, LangGraph, and OpenAI. LangGraph is a library that’s developed to create stateful multi-agent workflows.
!uv pip set up langchain==1.2.4 langchain-openai langchain-community==0.4.1 langgraph==1.0.6 Step 2: Configure API Keys & Surroundings
Arrange your OpenAI API key to energy our language fashions. The cell beneath will immediate you to enter your key securely.
import os
import getpass
# OpenAI API Key (for chat & embeddings)
if not os.environ.get("OPENAI_API_KEY"):
os.environ["OPENAI_API_KEY"] = getpass.getpass(
"Enter your OpenAI API key (https://platform.openai.com/account/api-keys):n"
)Step 3: Imports
The definition of the state, instruments, and brokers would require a number of parts of our libraries.
from typing import Annotated, Literal
from typing_extensions import TypedDict
from langgraph.graph.message import add_messages
from langgraph.graph import StateGraph, START, END
from langchain_openai import ChatOpenAI
from langgraph.varieties import Command
from langchain_core.instruments import device
from langchain_core.messages import HumanMessage, SystemMessage, AIMessage
from langchain.brokers import create_agent
from IPython.show import show, MarkdownStep 4: The Enterprise Logic – Datasets
We’ll function our system on a barebones in-memory information that can be a illustration of danger insurance policies, mortgage suggestions, and applicant data. This makes our instance self-contained and easy to observe.
risk_policies = [
{
"loan_type": "Home Loan",
"risk_category": "Low Risk",
"required_conditions": [
"credit_score >= 750",
"stable_income >= 3 years",
"debt_to_income_ratio < 30%"
],
"notes": "Eligible for greatest rates of interest and fast-track approval."
},
{
"loan_type": "Residence Mortgage",
"risk_category": "Medium Danger",
"required_conditions": [
"credit_score >= 680",
"stable_income >= 2 years",
"debt_to_income_ratio < 40%"
],
"notes": "Might require collateral or increased rate of interest."
},
{
"loan_type": "Private Mortgage",
"risk_category": "Medium Danger",
"required_conditions": [
"credit_score >= 650",
"stable_income >= 2 years"
],
"notes": "Handbook verification advisable for earnings consistency."
},
{
"loan_type": "Auto Mortgage",
"risk_category": "Low Danger",
"required_conditions": [
"credit_score >= 700",
"stable_income >= 2 years"
],
"notes": "Car acts as secured collateral."
}
]
loan_recommendations = [
{
"risk_category": "Low Risk",
"next_step": "Auto approve loan with standard or best interest rate."
},
{
"risk_category": "Medium Risk",
"next_step": "Approve with adjusted interest rate or require collateral."
},
{
"risk_category": "High Risk",
"next_step": "Reject or request guarantor and additional documents."
}
]
applicant_records = [
{
"applicant_id": "A101",
"age": 30,
"employment_type": "Salaried",
"annual_income": 1200000,
"credit_score": 780,
"debt_to_income_ratio": 25,
"loan_type": "Home Loan",
"requested_amount": 4500000,
"notes": "Working in MNC for 5 years. No missed EMI history."
},
{
"applicant_id": "A102",
"age": 42,
"employment_type": "Self Employed",
"annual_income": 900000,
"credit_score": 690,
"debt_to_income_ratio": 38,
"loan_type": "Home Loan",
"requested_amount": 3500000,
"notes": "Business income fluctuates but stable last 2 years."
},
{
"applicant_id": "A103",
"age": 27,
"employment_type": "Salaried",
"annual_income": 600000,
"credit_score": 640,
"debt_to_income_ratio": 45,
"loan_type": "Personal Loan",
"requested_amount": 500000,
"notes": "Recent job change. Credit card utilization high."
}
]Step 5: Constructing the Instruments for Our Brokers
Each agent requires gadgets to speak with our information. They’re plain Python capabilities adorned with the Python ornament device; that are invoked by the LLM when requested to do sure issues.
llm = ChatOpenAI(
mannequin="gpt-4.1-mini",
temperature=0.0,
timeout=None
)
@device
def fetch_applicant_record(applicant_id: str) -> dict:
"""
Fetches and summarizes an applicant monetary file based mostly on the given applicant ID.
Returns a human-readable abstract together with earnings, credit score rating, mortgage sort,
debt ratio, and monetary notes.
Args:
applicant_id (str): The distinctive identifier for the applicant.
Returns:
dict: {
"applicant_summary": str
}
"""
for file in applicant_records:
if file["applicant_id"] == applicant_id:
abstract = (
"Right here is the applicant monetary abstract report:n"
f"Applicant ID: {file['applicant_id']}n"
f"Age: {file['age']}n"
f"Employment Sort: {file['employment_type']}n"
f"Annual Earnings: {file['annual_income']}n"
f"Credit score Rating: {file['credit_score']}n"
f"Debt-to-Earnings Ratio: {file['debt_to_income_ratio']}n"
f"Mortgage Sort Requested: {file['loan_type']}n"
f"Requested Quantity: {file['requested_amount']}n"
f"Monetary Notes: {file['notes']}"
)
return {"applicant_summary": abstract}
return {"error": "Applicant file not discovered."}
@device
def match_risk_policy(loan_type: str, risk_category: str) -> dict:
"""
Match a given mortgage sort and danger class to essentially the most related danger coverage rule.
Args:
loan_type (str): The mortgage product being requested.
risk_category (str): The evaluated applicant danger class.
Returns:
dict: A abstract of one of the best matching coverage if discovered, or a message indicating no match.
"""
context = "n".be a part of([
f"{i+1}. Loan Type: {p['loan_type']}, Danger Class: {p['risk_category']}, "
f"Required Situations: {p['required_conditions']}, Notes: {p['notes']}"
for i, p in enumerate(risk_policies)
])
immediate = f"""You're a monetary danger reviewer assessing whether or not a mortgage request aligns with present lending danger insurance policies.
Directions:
- Analyze the mortgage sort and applicant danger class.
- Examine in opposition to the checklist of supplied danger coverage guidelines.
- Choose the coverage that most closely fits the case contemplating mortgage sort and danger degree.
- If none match, reply: "No acceptable danger coverage discovered for this case."
- If a match is discovered, summarize the matching coverage clearly together with any required monetary circumstances or caveats.
Mortgage Case:
- Mortgage Sort: {loan_type}
- Danger Class: {risk_category}
Accessible Danger Insurance policies:
{context}
"""
outcome = llm.invoke(immediate).textual content
return {"matched_policy": outcome}
@device
def check_policy_validity(
financial_indicators: checklist[str],
required_conditions: checklist[str],
notes: str
) -> dict:
"""
Decide whether or not the applicant monetary profile satisfies coverage eligibility standards.
Args:
financial_indicators (checklist[str]): Monetary indicators derived from applicant file.
required_conditions (checklist[str]): Situations required by matched coverage.
notes (str): Extra monetary or employment context.
Returns:
dict: A string explaining whether or not the mortgage request is financially justified.
"""
immediate = f"""You're validating a mortgage request based mostly on documented monetary indicators and coverage standards.
Directions:
- Assess whether or not the applicant monetary indicators and notes fulfill the required coverage circumstances.
- Take into account monetary context nuances.
- Present a reasoned judgment if the mortgage is financially justified.
- If not certified, clarify precisely which standards are unmet.
Enter:
- Applicant Monetary Indicators: {financial_indicators}
- Required Coverage Situations: {required_conditions}
- Monetary Notes: {notes}
"""
outcome = llm.invoke(immediate).textual content
return {"validity_result": outcome}
@device
def recommend_loan_action(risk_category: str) -> dict:
"""
Advocate subsequent lending step based mostly on applicant danger class.
Args:
risk_category (str): The evaluated applicant danger degree.
Returns:
dict: Lending advice string or fallback if no match discovered.
"""
choices = "n".be a part of([
f"{i+1}. Risk Category: {r['risk_category']}, Advice: {r['next_step']}"
for i, r in enumerate(loan_recommendations)
])
immediate = f"""You're a monetary lending choice assistant suggesting subsequent steps for a given applicant danger class.
Directions:
- Analyze the supplied danger class.
- Select the closest match from identified lending suggestions.
- Clarify why the match is suitable.
- If no appropriate advice exists, return: "No lending advice discovered for this danger class."
Danger Class Offered:
{risk_category}
Accessible Lending Suggestions:
{choices}
"""
outcome = llm.invoke(immediate).textual content
return {"advice": outcome}
Step 6: Implementing the Sub-Brokers (The Staff)
We now type our three particular brokers. Each agent is supplied with a particularly slim system immediate that explains to it each what it ought to do and what instruments it’s allowed to entry, in addition to construction its output.
case_intake_agent = create_agent(
mannequin=llm,
instruments=[fetch_applicant_record],
system_prompt=r"""
You're a Monetary Case Consumption Specialist.
THIS IS A RULED TASK. FOLLOW THE STEPS IN ORDER. DO NOT SKIP STEPS.
--- MANDATORY EXECUTION RULES ---
- You MUST name the `fetch_applicant_record` device earlier than writing ANY evaluation or abstract.
- For those who wouldn't have applicant information from the device, you MUST cease and say: "Applicant information not out there."
- Do NOT hallucinate, infer, or invent monetary info past what's supplied.
- Inference is allowed ONLY when logically derived from monetary notes.
--- STEP 1: DATA ACQUISITION (REQUIRED) ---
Name `fetch_applicant_record` and skim:
- Monetary indicators
- Monetary profile / danger context
- Mortgage request
- Monetary notes
It's possible you'll NOT proceed with out this step.
--- STEP 2: FINANCIAL ANALYSIS ---
Utilizing ONLY the retrieved information:
1. Summarize the applicant monetary case.
2. Determine specific monetary indicators.
3. Determine inferred monetary dangers (label as "inferred").
4. Derive rationale for why the mortgage might have been requested.
--- STEP 3: VALIDATION CHECK ---
Earlier than finalizing, verify:
- No monetary info have been added past device output.
- Inferences are financially cheap.
- Abstract is impartial and review-ready.
--- FINAL OUTPUT FORMAT (STRICT) ---
Sub-Agent Identify: Case Consumption Agent
Monetary Abstract:
- ...
Key Monetary Indicators:
- Express:
- ...
- Inferred:
- ...
Monetary Rationale for Mortgage Request:
- ...
If any part can't be accomplished on account of lacking information, state that explicitly.
"""
)
lending_decision_agent = create_agent(
mannequin=llm,
instruments=[recommend_loan_action],
system_prompt=r"""
You're a Lending Choice Advice Specialist.
YOU MUST RESPECT PRIOR AGENT DECISIONS.
--- NON-NEGOTIABLE RULES ---
- You MUST learn Consumption Agent and Danger Coverage Checker outputs first.
- You MUST NOT override or contradict the Danger Coverage Checker.
- You MUST clearly state whether or not mortgage request was:
- Authorized
- Not Authorized
- Not Validated
--- STEP 1: CONTEXT REVIEW ---
Determine:
- Confirmed monetary profile / danger class
- Coverage choice final result
- Key monetary dangers and constraints
--- STEP 2: DECISION-AWARE PLANNING ---
IF mortgage request APPROVED:
- Advocate subsequent lending execution steps.
IF mortgage request NOT APPROVED:
- Do NOT suggest approval.
- Counsel ONLY:
- Extra monetary documentation
- Danger mitigation steps
- Monetary profile enchancment ideas
- Monitoring or reassessment steps
IF coverage NOT FOUND:
- Advocate cautious subsequent steps and documentation enchancment.
--- STEP 3: SAFETY CHECK ---
Earlier than finalizing:
- Guarantee advice doesn't contradict coverage final result.
- Guarantee all ideas are financially cheap.
--- FINAL OUTPUT FORMAT (STRICT) ---
Sub-Agent Identify: Lending Choice Agent
Coverage Standing:
- Authorized / Not Authorized / Not Discovered
Lending Suggestions:
- ...
Rationale:
- ...
Notes for Reviewer:
- ...
Keep away from speculative monetary approvals.
Keep away from recommending approval if coverage validation failed.
"""
)
risk_policy_checker_agent = create_agent(
mannequin=llm,
instruments=[match_risk_policy, check_policy_validity],
system_prompt=r"""
You're a Lending Danger Coverage Evaluation Specialist.
THIS TASK HAS HARD CONSTRAINTS. FOLLOW THEM EXACTLY.
--- MANDATORY RULES ---
- You MUST base choices solely on:
1. Consumption abstract content material
2. Retrieved danger coverage guidelines
- You MUST NOT approve or reject and not using a coverage examine try.
- If no coverage exists, you MUST explicitly state that.
- Do NOT infer coverage eligibility standards.
--- STEP 1: POLICY IDENTIFICATION (REQUIRED) ---
Use `match_risk_policy` to determine essentially the most related coverage for:
- The requested mortgage sort
- The evaluated danger class
If no coverage is discovered:
- STOP additional validation
- Clearly state that no relevant coverage exists
--- STEP 2: CRITERIA EXTRACTION ---
If a coverage is discovered:
- Extract REQUIRED monetary circumstances precisely as acknowledged
- Do NOT paraphrase eligibility standards
--- STEP 3: VALIDATION CHECK (REQUIRED) ---
Use `check_policy_validity` with:
- Applicant monetary indicators
- Coverage required circumstances
- Consumption monetary notes
--- STEP 4: REASONED DECISION ---
Primarily based ONLY on validation outcome:
- If standards met → justify approval
- If standards not met → clarify why
- If inadequate information → state insufficiency
--- FINAL OUTPUT FORMAT (STRICT) ---
Sub-Agent Identify: Danger Coverage Checker Agent
Danger Coverage Recognized:
- Identify:
- Supply (if out there):
Required Coverage Situations:
- ...
Applicant Proof:
- ...
Coverage Validation End result:
- Met / Not Met / Inadequate Information
Monetary Justification:
- ...
Do NOT suggest lending actions right here.
Do NOT assume approval except standards are met.
"""
)Step 7: The Mastermind – Implementing the Supervisor Agent
That is the core of our system. Its structure is the immediate of the supervisor. It establishes the inflexible order of workflow and high quality checks it should make on the output of every agent earlier than occurring.
class State(TypedDict):
messages: Annotated[list, add_messages]
members = [
"case_intake_agent",
"risk_policy_checker_agent",
"lending_decision_agent"
]
SUPERVISOR_PROMPT = f"""
You're a Mortgage Evaluation Supervisor Agent.
You're managing a STRICT, ORDERED mortgage danger evaluation workflow
between the next brokers:
{members}
--- WORKFLOW ORDER (MANDATORY) ---
1. case_intake_agent
2. risk_policy_checker_agent
3. lending_decision_agent
4. FINISH
You MUST observe this order. No agent could also be skipped.
--- YOUR RESPONSIBILITIES ---
1. Learn all messages thus far rigorously.
2. Decide which brokers have already executed.
3. Examine the MOST RECENT output of every executed agent.
4. Determine which agent MUST act subsequent based mostly on completeness and order.
--- COMPLETENESS REQUIREMENTS ---
Earlier than shifting to the subsequent agent, confirm the earlier agent’s output accommodates:
case_intake_agent output MUST embrace:
- "Monetary Abstract"
- "Key Monetary Indicators"
- "Monetary Rationale"
risk_policy_checker_agent output MUST embrace:
- "Coverage Validation End result"
- "Monetary Justification"
- Both a coverage match OR specific assertion no coverage exists
lending_decision_agent output MUST embrace:
- "Coverage Standing"
- "Lending Suggestions"
- Clear approval / non-approval standing
--- ROUTING RULES ---
- If an agent has NOT run but → path to that agent.
- If an agent ran however required sections lacking → route SAME agent once more.
- ONLY return FINISH if all three brokers accomplished appropriately.
- NEVER return FINISH early.
--- RESPONSE FORMAT ---
Return ONLY one in every of:
{members + ["FINISH"]}
"""
FINAL_RESPONSE_PROMPT = """
You're the Mortgage Evaluation Supervisor Agent.
Analyze ALL prior agent outputs rigorously.
--- CRITICAL DECISION RULE ---
Your Closing Choice MUST be based mostly PURELY on the output of the
lending_decision_agent.
- If lending_decision_agent signifies mortgage APPROVED
→ Closing Choice = APPROVED
- If lending_decision_agent signifies NOT APPROVED or NEEDS INFO
→ Closing Choice = NEEDS REVIEW
--- OUTPUT FORMAT (STRICT) ---
- Agent Identify: Mortgage Evaluation Supervisor Agent
- Closing Choice: APPROVED or NEEDS REVIEW
- Choice Reasoning: Primarily based on lending_decision_agent output
- Lending advice or different steps: From lending_decision_agent
"""
class Router(TypedDict):
subsequent: Literal[
"case_intake_agent",
"risk_policy_checker_agent",
"lending_decision_agent",
"FINISH"
]
def supervisor_node(state: State) -> Command[
Literal[
"case_intake_agent",
"risk_policy_checker_agent",
"lending_decision_agent",
"__end__"
]
]:
messages = [SystemMessage(content=SUPERVISOR_PROMPT)] + state["messages"]
response = llm.with_structured_output(Router).invoke(messages)
goto = response["next"]
if goto == "FINISH":
goto = END
messages = [SystemMessage(content=FINAL_RESPONSE_PROMPT)] + state["messages"]
response = llm.invoke(messages)
return Command(
goto=goto,
replace={
"messages": [
AIMessage(
content=response.text,
name="supervisor"
)
],
"subsequent": goto
}
)
return Command(goto=goto, replace={"subsequent": goto})Step 8: Defining the Node capabilities
Right here the node capabilities which can be performing the function of laggraph nodes are to be outlined.
def case_intake_node(state: State) -> Command[Literal["supervisor"]]:
outcome = case_intake_agent.invoke(state)
return Command(
replace={
"messages": [
AIMessage(
content=result["messages"][-1].textual content,
title="case_intake_agent"
)
]
},
goto="supervisor"
)
def risk_policy_checker_node(state: State) -> Command[Literal["supervisor"]]:
outcome = risk_policy_checker_agent.invoke(state)
return Command(
replace={
"messages": [
AIMessage(
content=result["messages"][-1].textual content,
title="risk_policy_checker_agent"
)
]
},
goto="supervisor"
)
def lending_decision_node(state: State) -> Command[Literal["supervisor"]]:
outcome = lending_decision_agent.invoke(state)
return Command(
replace={
"messages": [
AIMessage(
content=result["messages"][-1].textual content,
title="lending_decision_agent"
)
]
},
goto="supervisor"
)
Step 9: Establishing and Visualizing the Graph
Now that we’ve got outlined our nodes, we might assemble the workflow graph. The entry level, the nodes of every agent, and conditional edges that direct the workflow relying on the choice of the supervisor are outlined.
graph_builder = StateGraph(State)
graph_builder.add_edge(START, "supervisor")
graph_builder.add_node("supervisor", supervisor_node)
graph_builder.add_node("case_intake_agent", case_intake_node)
graph_builder.add_node("risk_policy_checker_agent", risk_policy_checker_node)
graph_builder.add_node("lending_decision_agent", lending_decision_node)
loan_multi_agent = graph_builder.compile()
loan_multi_agentYou’ll be able to visualize the graph you probably have the suitable libraries, however we’ll proceed to run it.
Step 10: Working the System
Now for the second of reality. We’ll apply as candidates to our system and observe the supervisor organize the evaluation course of. Earlier than this we’ll obtain an utility perform to format the output.
# This utility file will not be important to the logic however helps format the streaming output properly.
!gdown 1dSyjcjlFoZpYEqv4P9Oi0-kU2gIoolMB
from agent_utils import format_message
def call_agent_system(agent, immediate, verbose=False):
occasions = agent.stream(
{"messages": [("user", prompt)]},
{"recursion_limit": 25},
stream_mode="values"
)
for occasion in occasions:
if verbose:
format_message(occasion["messages"][-1])
# Show the ultimate response from the agent as Markdown
print("nnFinal Response:n")
if occasion["messages"][-1].textual content:
show(Markdown(occasion["messages"][-1].textual content))
else:
print(occasion["messages"][-1].content material)
# Return the general occasion messages for non-obligatory downstream use
return occasion["messages"]
immediate = "Evaluation applicant A101 for mortgage approval justification."
call_agent_system(loan_multi_agent, immediate, verbose=True)Output Evaluation:
If you run this, you will note a step-by-step execution hint:
- supervisor (to caseintakeagent): The supervisor initiates the method with directing the duty to the consumption agent.
- caseintakeagent Output: It’s an agent that may run its device to retrieve the file of applicant A101 and generate a clear monetary abstract.
- supervisor -> riskpolicycheckeragent: The supervisor notices that the consumption has been made and forwards the duty to the coverage checker.
- Output of riskpolicycheckeragent: The coverage agent will discover that A101 is a Low Danger coverage that satisfies all their profile necessities of a Residence Mortgage.
- supervisor -> lendingdecisionagent: The supervisor now instigates the last word decision-maker.
- lendingdecisionagent Output: This agent will suggest an auto-approval within the class of “Low Danger” class.
- supervisor -> FINISH: When the supervisor reaches FINISH, it treats the ultimate employee as full and produces a cumulative abstract.
The top product can be a effectively written message freed from any grime corresponding to:
Colab Pocket book: Mastering Supervisor Brokers.ipynb
Conclusion
Utilizing a supervisor agent, we modified an advanced enterprise course of into predictable, strong and auditable workflow. Even one agent making an attempt to cope with information retrieval, danger evaluation, and decision-making concurrently would want a way more sophisticated immediate and can be more likely to make an error.
The supervisor sample affords a robust psychological mannequin and an architectural strategy to growing superior AI techniques. It allows you to deconstruct complexity and assign distinct duty and create sensible and automatic workflows that resemble the effectiveness of a well-coordinated human group. The second strategy to handle a monolithic problem is to not merely create an agent subsequent time, however a group, and all the time have a supervisor.
Regularly Requested Questions
A. Reliability and modularity is the first power. The general system turns into simpler to construct, debug, and preserve as a result of it breaks a fancy job into smaller steps dealt with by specialised brokers, which results in extra predictable and constant outcomes.
A. Sure. On this setup, the supervisor reassigns a job to the identical agent when its output is incomplete. Extra superior supervisors can go additional by including error correction logic or requesting a second opinion from one other agent.
A. Whereas it shines in advanced workflows, this sample additionally handles reasonably advanced duties with simply two or three steps successfully. It applies a logical order and makes reasoning technique of the AI considerably extra clear and auditable.
Harsh Mishra is an AI/ML Engineer who spends extra time speaking to Massive Language Fashions than precise people. Captivated with GenAI, NLP, and making machines smarter (so that they don’t change him simply but). When not optimizing fashions, he’s most likely optimizing his espresso consumption. 🚀☕
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