Artificial Intelligence (AI) models are evolving at an unprecedented pace, and the latest entrant making waves is Agentic AI. It is not just a buzzword; it represents a fundamental shift in how AI interacts with tasks, systems, and decision-making.
To set the context, let’s briefly revisit the AI models we’re already using in our daily workflows. For example, Grammarly, which adjusts your writing, is an Assistive AI. ChatGPT, which helps you brainstorm, draft, and break down complex concepts, is Generative AI. These tools are powerful, but they’re also reactive. They require a human to initiate the interaction – a prompt, a question, a direction.
What if there are AI models that can proceed to the next step without human interaction? What if it didn’t just respond to commands but could independently pursue a goal, adapt to changing variables, and learn from outcomes? That’s Agentic AI for you.
Agentic AI refers to models that go beyond passive assistance. These systems can proactively execute multi-step tasks, adapt to changing conditions, and improve performance over time, without needing human direction at every stage. While Assistive and Generative AI act as tools, Agentic AI acts more like a collaborator, capable of making context-aware decisions and course-correcting on the fly.
While Agentic AI provides the broader framework for autonomous decision-making and task execution, the actual units within that framework are called AI agents. These agents are purpose-built models designed to carry out specific objectives with a degree of autonomy, often by reasoning through a goal, breaking it down into subtasks, and interacting with other systems and data sources to achieve the intended outcome.
How AI Agents Work
At the core of an AI agent’s functionality is its ability to translate objectives into actionable workflows. The process typically begins with a human-provided task or an automatically generated prompt from another system. The agent then interprets the instruction, extracts key elements, and uses internal reasoning models to understand the task’s scope and context. This planning stage enables the agent to divide the objective into manageable subtasks, each with a clear path toward execution.
Once the subtasks are mapped, the agent identifies relevant data, constraints, and variables. It may integrate information from external sources such as APIs, internal databases, or real-time market feeds to inform its decisions. In a financial setting, this may involve updating risk models, macroeconomic indicators, or liquidity data to reflect current market conditions.
The agent then moves to the execution phase. This could involve sending out meeting invites optimized for participants’ availability, reconciling accounts across platforms, or rebalancing an investment portfolio based on shifting economic conditions. Execution is not the endpoint, however. After completing the task, the agent absorbs feedback, whether from human review, data outcomes, or performance metrics, and uses it to reassess and refine its approach. Over time, the agent’s memory stores patterns, exceptions, and responses that can improve future performance.
What sets Agentic AI apart is this feedback-driven learning loop. Unlike one-off models that repeat the same logic each time, AI agents evolve and adapt. They learn what works, adapt to edge cases, and become more efficient with repetition and data exposure.
Single-Agent vs. Multi-Agent Systems
While many use cases can be addressed by a single intelligent agent, more complex workflows often require coordination among multiple agents. In a multi-agent system, each agent specializes in a different aspect of a broader task. These agents may communicate with each other, share context, and negotiate task ownership autonomously. In some configurations, they don’t even need a direct human prompt: one agent’s output can serve as another’s input, enabling an end-to-end execution chain across departments or systems.
For example, in a finance function, one agent might monitor economic indicators and detect early signs of market volatility. It could then trigger a second agent tasked with portfolio rebalancing, which consults with a third agent that handles regulatory compliance before any action is finalized. The entire process can unfold without human intervention, though still under the watch of human oversight.
Why This Matters to Finance Leaders
For finance leaders, the rise of Agentic AI is more than a technical development; it’s a strategic opportunity to streamline high-impact financial operations. One of the most compelling applications lies in core areas of Order-to-Cash processes such as credit risk analysis, collections prioritization, cash application, and payments reconciliation.
Imagine AI agents that not only pull real-time credit reports but dynamically assess customer risk based on payment trends, macroeconomic signals, and transaction history. Or agents that prioritize collections activities based on expected recovery likelihood, automate follow-ups, and resolve disputes by referencing customer contracts or invoice trails. On the cash application side, agents can match payments to invoices with high accuracy, even across fragmented data systems, while learning from exceptions to improve future matches. These use cases are not hypothetical—they’re already in development and adoption across advanced finance teams.
The potential benefits are significant: increased efficiency, real-time responsiveness, reduced manual intervention, and more time for strategic decision-making. However, realizing this value requires not just deploying Agentic AI tools, but also rethinking workflows, data pipelines, governance, and trusting the right cash applications to deploy these autonomous agents safely.
Agentic AI marks a shift from AI as a passive tool to AI as an active, autonomous participant in business operations. For finance leaders, this evolution presents an opportunity to unlock operational agility, strategic foresight, and continuous improvement. The challenges and opportunities reside in identifying the proper use cases for your financial teams.
