The next frontier in artificial intelligence comes with ambition, autonomy, and agency
Artificial intelligence has become a fixture in financial services, streamlining everything from credit risk assessment to customer engagement. But as adoption expands and use matures, the next frontier is no longer about just automation, but autonomous intelligence. In other words, agents.
Agentic AI introduces systems that don’t simply accelerate individual tasks. Instead, they pursue objectives, manage complex workflows, adapt to evolving inputs, and produce decision-informing responses with minimal oversight. This is AI not just as a tool, but as a coordinated team of collaborators working under your direction to scale expertise, extend capacity, and deliver high-quality outcomes with speed and precision.
At Moody’s, we’re already putting agentic AI to work, focusing not just on its potential, but on concrete steps to both automate and enhance workflows.
What Is Agentic AI?
An evolution of generative AI, which is traditionally understood to perform discrete, single-step functions, agentic AI systems exhibit a higher level of autonomy while being able to achieve very complex interconnected tasks. They operate across time, initiate actions, adjust strategies based on outcomes, and in some cases, collaborate with other agents or tools. Built on top of powerful foundation models, agentic systems can simulate reasoning, invoke tools via APIs, and learn from feedback loops. To an extent and when appropriate, they can be tasked to manage workflows with minimal human supervision.
This reflects a shift from assisted intelligence, where AI supports single-task acceleration such as single request analysis, document summarization or thematic synthesis, to autonomous intelligence, in which systems execute full analytical processes from start to finish. Rather than helping a user complete individual steps, agentic AI functions as a digital co-worker: coordinating tasks, drawing from credible sourced data, and delivering quality, consistent, comprehensive, and decision-informing outputs that are ready for stakeholder review.
In practice, when tasked with assessing a company’s credit profile, for example, an agentic system goes far beyond pulling data. It retrieves and analyzes earnings call transcripts, credit ratings, regulatory filings, sectorial and macroeconomic indicators, ESG disclosures, and peer comparisons to name a few. It then analyzes this information, identifies risk factors and strengths, and produces a structured, automated credit memo aligned with institutional standards.
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Simulated Intent, Real Impact
Agentic AI is best understood as a class of systems that simulate goal-directed behavior. These systems can receive an objective, formulate sub-tasks, and pursue those sub-tasks through autonomous action, potentially through an extended duration, and with minimal human input between tasks. The term “agentic” distinguishes these models from specialized assistants: an agentic system is not responding to a simple command or answering a question, but managing a process. While this added autonomy opens the door to powerful new workflows, it does also raise concerns. Without proper guardrails, agentic systems may misinterpret vague goals, pursue them in unintended ways, or interact unpredictably with external systems.
It is of utmost importance to remember that an agent’s emergent behaviors are driven not by understanding, but by probabilistic optimization. As a result, financial institutions must treat agentic AI not as completely autonomous decision-makers, but as powerful assistants that require supervision, validation, and control. When an AI, in a manner of speaking, can set its own to-do list, oversight matters more than ever. The recommendation is to always “keep the human in the loop”.
What the Finance Industry Gains
Moody’s agentic systems are already reshaping key financial workflows. These are not general-purpose assistants or simple automation tools. They are domain-specific AI architecture, purpose-built to improve speed, consistency, and analytical depth across high-value processes. They leverage Moody’s 100+ years of combined experience in data and financial analytics to marry technology to data intelligence.
1. Credit Assessment and Issuer Evaluation
Assessing credit risk is a complex process that requires pulling together financial data, sector dynamics, regulatory filings, macroeconomic signals, as well as news and media. Traditionally, this has involved time-consuming manual work and significant variation in quality and timing.
Agentic systems streamline this by ingesting a wide range of inputs, from deep historical financial and liquidity ratios to ESG metrics, earnings transcripts, peer benchmarks, and credit opinions. These data sets are interpreted in context to evaluate business model durability, systemic risk exposure, and market positioning. The outputs are then synthesized into structured entity and counterparty credit memos, complete with visual summaries and actionable conclusions.
The result is not just faster analysis. It’s potentially more consistent, deeper insight, delivered with reduced effort.
2. Portfolio Monitoring and Early Warning
For portfolio managers tracking thousands of counterparties, traditional monitoring approaches often struggle to keep pace with market developments. Manual reviews are periodic and reactive by nature.
Our agentic systems provide a continuously running layer of analysis. They monitor financials, sentiment signals, ratings changes, earnings commentary, and sector-level indicators in real time. By correlating data across entities, they identify early signs of deterioration and flag systemic risks before they escalate. The outputs include alerts and summaries that support faster, more insightful portfolio-level decisions.
This shift toward proactive surveillance allows teams to manage risk with greater agility and foresight.
3. Sales and Marketing Enablement
Sales and marketing teams often have access to large volumes of data but limited capacity to extract targeted commercial insight. Agentic systems can help close that gap.
These systems aggregate firmographic data, prospects’ strategic priorities, market behavior, sectorial intelligence, and media sentiment to identify leads with potentially strong commercial potential. They assess each target's objectives, position within the market, and likely buying signals. The outcome is not just a list of prospects, but a complete briefing ready for human review: tailored insights, contextual analysis, and recommended engagement strategies.
This helps go-to-market teams focus their efforts, personalize their outreach, and operate with greater strategic precision.
Across all three domains, agentic systems do more than automated work. They elevate the institutional decision-making process. By combining Moody’s expansive data estate with orchestrated AI workflows, these systems can help professionals to act faster, with greater clarity and confidence. This is not about cutting corners. It’s about raising the bar and delivering consistent, high-quality analysis at scale.
Implementation Considerations
Despite their promise, agentic systems are not plug-and-play. Their deployment requires careful orchestration across infrastructure, governance, and talent, with a close eye on security and data privacy.
Technical Integration: Most agentic systems are composed of multiple layers: language models, orchestration frameworks (like LangChain or Autogen), tool access APIs, and memory stores. This modularity allows for flexibility but also increases complexity. Institutions must ensure robust observability, fallback systems, and secure execution environments.
Human-in-the-Loop Oversight: We advocate for hybrid workflows, where AI agents assist and bring efficiency and quality rather than replace domain experts. Structured interfaces, feedback loops, and clearly defined scopes of action help foster safety and accountability.
Governance and Compliance: As AI agents take on more responsibility, the question of transparency becomes acute. Regulators and clients alike may demand auditable agent reasoning and decision paths. Institutions should invest in robust observability tooling and governance framework that can provide clear lineage from input to output. Well-documented workflows using unambiguous sourcing methodologies are essential for trust, accountability, and long-term adoption.
Culture and Talent: Embedding agentic AI within an organization also requires a shift in mindset. Business users must learn to interact with AI not as a tool, but as a collaborator — one that requires prompting, supervision, and calibration. Cross-functional teams spanning data science, domain expertise, and compliance will be key.
A Long-Term Transformation
Moody’s views agentic AI as more than a technological upgrade. It is a conceptual shift in how we structure work. It enables financial professionals to scale their expertise, interact with information dynamically, and respond to uncertainty with greater agility. We believe in the efficiency and increased quality this new technology brings to the industry when used with quality data in a responsible manner.
We are one of the earliest adopters of AI in the financial space, and we know that the potential for improvement and growth within AI in general, and agentic AI in particular, is vast. As we continue to evolve alongside innovative market advancements, our agentic solutions aim to unlock a powerful form of digital labor: one that is efficient, tireless, and increasingly capable of handling nuance.
As financial services firms consider their own agentic strategies, we encourage a pragmatic approach. Start with narrow, well-scoped use cases. Validate outputs rigorously. Keep humans in the loop. And above all, align system design with core institutional values such as accuracy, transparency, and trust.
About the author:
Nicolas Pintart is a seasoned expert in advanced technologies with extensive experience helping financial institutions and corporations harness artificial intelligence and advanced analytics to drive smarter decisions and operational efficiency. Specializing in the implementation of AI-powered solutions that transform how organizations leverage data across credit risk assessment, portfolio monitoring and strategic planning, Nicolas helps organizations bridge the gap between complex technical capabilities and real-world business impact enabling the confident adoption of AI technologies that deliver measurable value.
A respected industry voice on AI in financial services, Nicolas frequently speaks at international conferences on the future of AI in financial services, data orchestration, responsible AI deployment and intelligent automation.
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