The rise of the digital colleague:evaluating companies with
Moody's AI agents
Less than two years ago, ChatGPT emerged as a simple
conversation partner — a magical tool for chatting with amachine. But in what feels like the blink of an eye, it set
the world on a whirlwind journey of technologicalchange. Today, we find ourselves at the dawn of a new
era where autonomous programs called AI agents, bornfrom that chat tool, collaborate with us as digitalcolleagues. This evolution transforms how we solve
problems, pushing the boundaries of what machines andhumans can achieve together.
At Moody’s, we’ve embraced these advancements todevelop our own cutting-edge multi-agent system, which
we will soon make available to customers. Designed towork for you as the human decision-maker, our system
can conduct a thorough analysis of any company you
wish to evaluate, helping unlock insights faster and more
efficiently than ever before.

AUTHORSAri Lehavi
GM-Predictive Analytics
ari.lehavi@moodys.com

Olga Loiseau-Aslanidi, PhD
MD-Predictive Analytics
olga.loiseau-aslanidi@moodys.com

Gustavo Ordonez, PhD
Sr. Director-Predictive Analytics
gustavo.ordonez@moodys.com

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To extract these insights, all you need to do is ask any questionthat could help you in your work. For example:
If you are a lender, you might want to understand the key
risks a borrower faces that could impact their ability to
pay back a loan.
If you are in corporate development or mergers andacquisitions, you might want to understand the synergies
between two companies.
If you are an investor, you might want to understandwhich company is likely to take market share away fromits competitors.
In this paper, we explain our agent system in more detail. We
demystify the key concepts and emphasize the rigorous work
Moody’s experts perform to make sure that the analysis ouragents produce matches the robustness, accuracy, and transparency you would expect from a team of highly skilled humancolleagues. The distinction? These are digital colleagues.

What problem are
we aiming to solve?

We designed our agent system to follow a workflow that broadly resembles that of a typical team conducting company analysis but with
important distinctions that we will discuss.
To understand how the agent system works, let’s first examine how it
would be handled in a typical work environment. For illustration purposes, we will use the example of vetting a company that is being considered as a critical supplier to determine if it is financially sound.

Join our waitlist for early access to Moody's Agentic System02As a starting point, the team assigned to this task would set objectives:

1

Review the company’s
recent financial performance to discover important trends in growth,margins, and cash flows.

2

Evaluate the businessfactors that help explain theobserved performanceusing frameworks such as
Porter’s Five Forces; value
chain analysis; and thepolitical, economic, sociological, technological, legal,
and environmental (PESTLE)
assessment.

3

Assess any headwinds or
tailwinds impacting thebusiness and determine theimplications for the company and its financial outlook.

With these objectives in mind, the team’s manager would assign roles andresponsibilities to team members based on their skills and experience. One
person may evaluate the financials while another may gather informationon the company’s customers, suppliers, and competitors. A more seasoned
team member might then review and synthesize the data into a report,
which would inform the decision on whether to onboard the supplier.
Undertaking rigorous fundamental research of this kind can be laborious
and time-consuming, especially when analyzing lesser-known companies
that require extensive investigation. Just the process of gathering
up-to-date information from multiple sources on its own can take hours oreven days to complete. At Moody’s, we have proprietary access to much ofthis hard-to-find data since we process timely business and financial
information on more than 500 million companies. Yet even with this information in hand, the analysis remains tedious, and it becomes less practical
if the goal is to evaluate multiple companies at once.
Therefore, we built our agent system to automate this work, recognizing thepotential to significantly drive efficiency, helping you focus on making
strategic decisions informed by the comprehensive research our agents
conduct on your behalf.

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How Moody’s
human-guided
agent system works

While it is helpful to compare a multi-agent system to a human team, inpractice, AI agents operate quite differently. First, let’s define what wemean by AI agents.
It is useful to think of agents as self-executing programs to which ahuman assigns an objective; the agent then lets the program use a large
language model (LLM) to determine the best approach for completingthe objective. Agents may also be equipped with external tools (such asaccess to the web, Moody’s databases, or other agents) to support theirwork. Once their assignment is complete, they may either submit theirconclusions to you as the decision-maker or continue to interface withother agents as part of the broader objectives of a multi-agent system.

Action & Tools

Action & Tools

LLM
Objective

Reasoning

Action & Tools

Plan

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The key to making an agent system effective at conducting company
analysis at scale lies in the quality of the orchestration of the tasks andthe precision of instructions given to the agents. Hence, Moody’s experts
took great care in crafting the instructions for the agents to frame theirinterpretation of our proprietary databases and their use of tools.
The orchestration of the agents can be described along three dimensions:
1. Assigning roles and objectives: In designing the system, we broke down the overall workflow into a series ofinterdependent steps governed by an agent, each with a well-defined objective and carefully selected capabilities
and tools that correspond to the task at hand.
TABLE 1 Examples of agents’ roles and responsibilities
Agent role

Responsibilities

Agent orchestrator

Handles the workflow execution and coordinates the information flow
between agents

Financial risk analyst agent

Performs all financial analysis tasks, including horizontal and vertical, trend
analysis, cash flow analysis, and DuPont analysis

Macroeconomic analyst agent

Analyzes economic conditions such as GDP growth, inflation, and interest
rates that affect individual companies or sectors; further synthesizes
insights from key economic indicators and policy changes to understandindustry trends and regulatory impacts and can identify potential opportunities and threats

Knowledge base management
agent

Evaluates different sources of material in the knowledge base to avoid
redundancy and maintain a cohesive narrative

Credit risk analyst

Assesses businesses’ creditworthiness, including reviewing past credit
behavior as well as examining financial statements, income, and cash flow togauge a firm’s ability to meet its financial obligations

Quality assurance agent

Identifies and removes hallucinations, making sure all information is timely;
confirms that all components of the tasks assigned to other agents areaccomplished accurately

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2. Providing access to tools (resources): We use the term “tools” to refer to access that the agent is given todifferent sources of content or analytics. These may include Moody’s proprietary databases and analytical engines,
the web, Moody’s curated newsfeeds, or other agents’ output. The choice of tools depends on the agent’s objective;in the case of Moody’s agent system, Moody’s experts determined the selection of tools. As an example, an agent
tasked with analyzing financial performance will need access to another agent that retrieves financials and ownership structure (both extracted from Moody’s databases). It may also need access to a financial modeling engine.
Along with determining the toolset, Moody’s experts carefully guide the agent on what information to look for, how to
use this information, how to organize the output, and where to send it.
3. Empowering with skills: Skills are the agent’s specialized attributes that make it uniquely able to perform theobjective at hand. Skills are most often associated with the capacity of the specific LLM that powers the agent.
While some tasks, such as information retrieval, can be performed expeditiously with a less powerful LLM, agentsthat synthesize insights require a state-of-the-art model to be their reasoning engine. Skills can also refer to anagent’s ability to self-reflect and continuously improve its own output. We discuss these in the section covering
quality assurance.

Case study: using our agent system for risk analysis
A common application of our agent system is to evaluate the downside risks for a company you wish toengage with. Credit lenders, supplier risk managers, trade credit managers, and investors all tend to focus
their analysis on what could go wrong with a target company.
Downside risk comprises both business risk and vulnerability-to-event risk. To understand business risk, ourmulti-agent system synthesizes information about the factors affecting a company’s strategic and operational drivers. The agents are tasked with studying the company’s sources of revenue and cost and probing eachof these business drivers for any significant forces that may impact it. Examples of such forces may becompetitive threats, economic headwinds, or cost pressures from suppliers.
To assess vulnerability-to-event risk, our system references our proprietary event-driven loss databases andmaps these events to the company’s unique attributes that make it vulnerable to exogenous shocks such as
cyberattacks, climate catastrophes, or geopolitical turmoil. The system also tracks recent events via newsfeeds and other relevant data feeds to determine the likelihood of such an event impacting the company.

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Since Moody’s agents have been painstakingly trained to identify and analyze sources of downside risk, you
can use the system to ask questions such as:
What are the most significant risks facing this company that I should worry about? What makes themsignificant? How likely are they to materialize?
What has the company done to mitigate these risks?
Which company among its peers is less exposed to these risks?
What would happen to the company in the event of an external shock, such as a climate event or
supply chain disruption?
The ability to offload research and risk analysis to our agent system can save you hours of work and potentially point out latent risks that you had not considered.
Below is an example of how you can interact with the system to gain insights into risks facing a company you
are interested in.

As a business considering “Auto Retailer LLC” as a supplier forour company car fleet, what risks should we assess, including
financial performance, geopolitical, cyber, regulatory, ESG,
operational, reputational, and natural disaster risks?

Moody'sDigitalCo-worker“Auto Retailer LLC” is one of the largest European car dealership, offering a wide range of services
beyond vehicle sales, including vehicle servicing, parts, rental, and financing options. It hasestablished a reputation for providing a diverse inventory of new and used vehicles across numerous
locations, making it a significant player in the automotive market. When considering it as a supplierfor your company car fleet, it is essential to assess various risks to ensure the stability and reliabilityof their services.
Financial Performance Risk: “Auto Retailer LLC” has demonstrated strong revenue growth,
increasing by 3.8% between 2021 and 2023, despite a decline in pre-tax profits. This indicates thecompany’s ability to generate substantial income necessary for meeting obligations and ensuringservice continuity (1, 2).
Cyber Risk: Given “Auto Retailer LLC” experience with a significant cyberattack, evaluating theircurrent cybersecurity measures is crucial; the company has likely enhanced its cybersecurity
protocols following the incident, but ongoing vigilance is necessary (3, 4).
Regulatory / Compliance Risk: Ensuring regulatory compliance is critical, particularly regarding dataprotection laws and automotive industry standards. The cyberattack exposed vulnerabilities that led
to legal consequences and highlighted the importance of robust compliance measures (3).
ESG / Sustainability Risk: “Auto Retailer LLC” is committed to achieving Net Zero emissions by
2050 and has developed a comprehensive plan for carbon reduction, which aligns with increasing
market focus on sustainability (5, 6).
Operational Risk: Operational risk considerations include how “Auto Retailer LLC” manages its
supply chain risk. The company has established stable relationships with key suppliers for workforce
development and for telematics, ensuring a steady flow of necessary components and services. Theircommitment to maintaining high service quality is evident through continuous investments intechnology, such as telematics systems and digital payment solutions (1).
(1) Moody’s Orbis Corporate Database;
Reputational Risk: The reputation of “Auto Retailer LLC” is crucial; the significant cyberattack(2) Moody’s Analytics Country Risk Service;
compromised the personal data of over thousands customers, impacting customer trust and(3) BitSight Cybersecurity Assessment available in Moody’s Orbis Corporate Database;satisfaction and leading to legal actions and heightened scrutiny (3, 4).
(4) Moody’s NewsEdge Data Breach Feed;(5) Moody’s ESG Report;
Natural Disasters Risk: Identifying the risk of natural disasters affecting “Auto Retailer LLC”
(6) Auto Retailer LLC Sustainability Report;
operations is essential. Their headquarters and many dealerships are located in Scotland, which has(7) Moody’s RMS Climate On Demand
historically faced severe weather events including heavy rainfall and flooding. Similar risks arepresent in England and Northern Ireland where they operate numerous dealerships and service
centers (7).

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All the analysis, including all agent output and interactions, is kept in detailed logs, giving you full transparency and auditability into the process. This is crucial to gain trust in the analysis and retain an audit trail for
future reference.

Ensuring accuracy,completeness,and timeliness

As noted earlier, carefully orchestrating and instructing agents aroundbusiness objectives is not sufficient to produce reliable outcomes.
Multi-agent systems can contain hundreds of interactions among
agents. Each interaction involves an intricate exchange of informationand instructions that are driven by the system’s agency and not craftedby humans. As the number of agents and interactions increases, so does
the number of points where failure can occur and propagate. This isdriven by imperfections in the information exchange, including ambiguous or incorrect instructions across agents or hallucinations. Preventing
such failures is as challenging as it is important. Table 2 below outlines
three approaches that we apply to fortify the system.

TABLE 2 Sample of techniques to guard from errors and propagating failures
Technique

Explanation

Prompt optimization for agent’s
reasoningThis approach allows us to set objectives by asking the agents to iteratearound the prompts and actions until they achieve the desired outcome.
Given the numerous interactions among agents, it is crucial that the information exchanged is always clear and accurate. Since the messages shared
between agents are not known in advance, it is impractical for Moody’s
experts to monitor and fine-tune the myriad instructions and prompts thattake place within the system.
To address this challenge, we use a technique called dynamic self-promptingin which the system autonomously tests and improves prompts until reaching the desired outcome.

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Majority voting for optimal agent’s
decision-making

Majority voting involves sourcing opinions from different “expert” agents toreach a consensus. By asking different agents to complete the same task
and using their consensus conclusions, the quality and consistency of theoutcomes are dramatically increased.
Consider a simple example involving three agents, each reaching different
conclusions as to which economic developments might affect a given
carmaker:
Higher disposable personal income(DPI) directly boosts car sales bygiving consumers more money tospend.

Crude oil prices impact car purchases, especially for less fuel-efficientmodels, making oil price trends
crucial for predicting demand.

Agent 1

Agent 2Agent 3Concludes that DPI is more importantbecause it directly impacts significant
purchases, reflects overall economic
health, and is more stable andpredictable than oil prices.

In this example, we might use a majority voting system wherein the agents
provide their reasoning for selecting the economic factor they believe has themost impact and then debate the merits of their reasoning to arrive at aconsensus ranking.
Autonomous quality assurance (QA)
monitoring and enhancement

To ensure high-quality outputs, we developed a system for continuous
monitoring and self-actuated review of the quality of each subtask in theworkflow.
Our goal is to make sure that the output generated by the system is:
Hallucination-free: That is, it faithfully represents the facts that are
known about the company (for this we measure faithfulness).
Timely: We use only the most recent available information (here wemeasure timeliness).

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Thorough: We confirm that all tasks requested during the execution
are successfully completed to the extent that is possible with theknowledge we have of the company (finally, we measure completeness).
Please refer to the appendix for more details on our approach to autonomous
monitoring.

The use of the above techniques, coupled with careful human orchestrationand meticulous instructions around the objectives and tools, delivers consistently high-quality output that rarely produces inaccurate or misleading
analysis.
As we archive every step in the workflow and every agent interaction, we canclosely monitor the system’s performance. Our current approach to evaluating performance quality is based on our expert judgment. We aim to formalize the approach and maintain a benchmark that tracks the system’s performance.

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10What’s next andﬁnal thoughts

Agent systems are early in their development. Those that reliably solve complex and interconnected tasks within an enterprise workflow are hard to come by — and only a few, if any, havebeen implemented at scale. As such, we are collaborating with various market participants thatare also on this journey and keeping an eye out for developments in the field.
Below are the areas we’re focusing on as we continue to enhance our system:
Risk monitoring:As our system helps uncover and analyze latent risks, it makes sense tomonitor them and evaluate their impact over time. The initial mapping ofrisks to the most relevant signals has already been completed and will be
implemented shortly. The next step is to assess how changes in the signals
may directly or indirectly affect the company’s projected performance andprovide early-warning indicators if the signals deteriorate.

Human
If we think of the agent system as analogous to human coworkers, we alsocollaboration and need to rethink how the user experience would adapt to facilitate thecontextualization:interactions. We envision this experience as centered around a jointly
constructed artifact, such as a credit or investment recommendation. This
design flips the traditional notion of workflows, where individuals or teams
complete tasks and hand off their work product to others. With digitalcoworkers in the mix, much of the work can now be done simultaneously.
Self-learning andadaptation to
anticipate userneeds:

The current implementation of Moody’s agent system includes various
components that learn and adapt over time. We are now developing theability to learn more about the user and reconfigure the various digitalcoworkers’ roles and priorities accordingly. We are well on our way tobuilding these capabilities and plan to publish more details about ourapproach in the coming months.

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The accelerated advancement of AI keeps us
uncertain about the future path of thetechnology and its implications. We believethat as language models improve, the system
we built may require fewer layers of
veriﬁcation and more simpliﬁed instructions.
However, just as the LLM capabilities willcontinue to expand, so will our expectationsof the digital colleagues — and along with that,
we as the decision-makers will need tocontinue to improve our own game. At somepoint, we may no longer be able to keep up
with our digital coworkers’ progress. In themeantime, we get to spend less time on
tedious tasks and more time evaluating
important decisions.

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12Appendix: quality assurance
To see our autonomous monitoring process in action, we present an extract from a multi-hundred-page QA log
generated during the system’s execution. This extract covers an iteration of one of the subtasks testing completeness, faithfulness, and timeliness of the information used for the task the agent performed.

COMPLETENESS TEST PASSEDFAITHFULNESS TEST FAILEDTIMELINESS TEST PASSEDThe response comprehensively
addresses the four key aspects oflocal manufacturing investment: It
provides a clear description of its
importance, outlines the status of
specific agreements and financial
commitments, discusses the
positive outlook regarding economic growth and job creation, andidentifies vulnerabilities andchallenges that “Oil Company” mayface in achieving its goals. Eachsection is well-articulated andrelevant to the query.

The response does not faithfully
capture some of the referenced
source content. The context statesthat Oil Company signed agreements with 23 companies for local
manufacturing opportunities worth$2 billion as of February 2023,
while the response claims that asof May 2024, Oil Company signed
agreements with 25 companies for$3 billion. Additionally, the context
mentions a target of $5 billion by
2027, which was later increased to$7 billion by 2030, but the
response does not clarify theprevious target. Therefore, thediscrepancies in the numbers andtimeline indicate that the references are not correct.

The response includes information
about Oil Company local manufacturing investments and agreements
as of May 2024, which is after
2023. Therefore, it meets thecriterion of containing facts fromafter 2023.
These logs provide a valuable
learning resource for the agents.
By examining the reasons why
tests have failed or passed, agents
can improve their strategies. The
process is run iteratively until all
the tests are passed.

The table below shows a process where all three tests pass at the fifth iteration:
Attempt

Completeness

Faithfulness

Timeliness

1
2
3
4
5

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