AI agents explained: what is agentic AI and how is it changing finance?

Artificial intelligence is quickly reshaping the financial industry, with AI agents leading the charge. These intelligent systems autonomously handle financial tasks, optimize investment workflows, and drive innovation in analysis and decision-making. As companies look for ways to boost efficiency and gain a competitive edge, agentic AI is becoming essential.

In this article, we are exploring the evolution, benefits, and applications of agentic AI in finance, as well as best practices for successful implementation.

What are AI agents?

AI agents are autonomous systems designed to make decisions, interact with their environment, and accomplish specific goals with minimal human input. They use advanced machine learning (ML), natural language processing (NLP), and multimodal capabilities to process diverse data types, such as text, images, and audio.

What can AI agents do?

AI agents operate independently, adapting to changes in their environment. Here’s what they can do:

• Optimize actions to achieve predefined goals.
• Process information and execute tasks with minimal delay.
• Utilize reinforcement learning to improve continuously.
• Collaborate with other AI agents to manage complex workflows.

Armando Gonzalez

Founder

RavenPack | Bigdata.com

"Think of these AI agents as a team of financial specialists, capable of working with both structured and unstructured datasets like sentiment scores, pricing data, job analytics, or news. For instance, an agent like Bigdata.com’s Thematic Screener reviews corporate filings, job postings, and market transcripts, interpreting trends and performing detailed scenario analyses. These systems don’t just understand financial jargon—they also contextualize data to deliver actionable insights tailored to the needs of financial professionals,"

How does agentic AI differ from other types of AI?

Traditional AI often relies on predefined rules, while agentic AI takes a more proactive approach, identifying and addressing financial challenges on its own. Agentic AI interprets data across various sources, such as regulatory filings or market sentiment, and can spot patterns, identify trends, and synthesize insights. For example, while traditional AI might process raw stock prices, agentic AI analyzes pricing data alongside hiring trends and sentiment to predict market shifts and suggest strategies.

The role of agentic workflows in finance

Agentic workflows in finance streamline analysis, automate decision-making, and enhance market intelligence. These workflows consist of interconnected AI agents performing specialized tasks to create a seamless research and trading environment.

Here’s a look at key components of agentic workflows in finance:

• Data acquisition & processing: AI agents collect and clean data from various financial sources, such as earnings reports, stock prices, and regulatory filings.
• Sentiment & market analysis: AI-powered sentiment agents analyze news, social media, and analyst reports to gauge market sentiment.
• Predictive modeling: Advanced ML algorithms forecast stock movements, economic trends, and risk factors.
• Automated decision-making: AI agents generate real-time trading signals and risk assessments based on both historical and current market data.
• Regulatory compliance monitoring: AI workflows ensure compliance with financial regulations by analyzing filings and detecting anomalies.

Key applications of agentic AI in finance

Platforms like Bigdata.com, an AI research assistant built by RavenPack, offer a range of AI agents for financial professionals, allowing them to analyze both structured and unstructured data. These tools cover areas like earnings call analysis, financial estimates, job market trends, news sentiment, pricing data, regulatory filings, and venture capital activity. These solutions empower users to access and analyze vast amounts of data efficiently.

Additionally, the Enterprise solution provides an API for users to build their own agentic workflows. Let’s explore an example:

Bigdata.com API agentic workflow for central bank monitoring

To showcase the power of agentic workflows, consider Bigdata.com’s API solution for monitoring central bank activity. This workflow acts as a daily research assistant, helping strategists stay ahead of market trends by simplifying the complex task of monitoring global monetary policies and central bank announcements.

Here’s how the workflow works:

• Lexicon generation: The agent identifies specialized jargon and keywords relevant to central bank announcements, ensuring it can gather comprehensive data.
• Content retrieval and processing: The API uses keyword-based searches to retrieve news and documents daily, utilizing multi-threading to speed up data collection.
• Topic clustering and analysis: The agent groups news into clusters, ranks them by media attention, novelty, and market impact, then quantifies these metrics. This process helps filter out noise and highlight key developments.
• Custom report generation: The workflow produces detailed, customizable daily digests of the top news, providing insights into new announcements and their effects on equity prices, tailored to the user’s preferences.

This is just one example of how agentic workflows can automate complex research, providing valuable insights and freeing up analysts to focus on higher-level tasks.

Benefits and challenges of AI-powered workflows in finance

AI-powered workflows provide significant advantages in finance, such as scalability, improved decision-making, and enhanced compliance and risk monitoring, all while allowing professionals to focus on more strategic tasks.

However, transitioning to agentic AI comes with challenges. Outdated infrastructure can complicate AI adoption, and incomplete or biased datasets can lead to inaccurate insights. Ensuring data accuracy is crucial, as is making sure that AI-driven decisions are transparent and explainable to build trust.

How to successfully integrate agentic AI

To unlock the full potential of agentic AI, organizations should take the following critical steps:

• Identify high-impact use cases: Focus on areas where AI can drive the most value, transforming challenges into opportunities.
• Partner with AI experts: Collaborate with trusted vendors who offer cutting-edge, pre-built AI agents tailored to your business needs.
• Pilot and refine solutions: Implement AI systems through pilot programs, measuring success and optimizing performance for greater impact.
• Empower your teams: Ensure smooth adoption by providing comprehensive training, so your employees can maximize the power of AI.
• Commit to ethical AI practices: Prioritize transparency, data integrity, and regulatory compliance to build trust and foster long-term success.

Agentic AI workflows are transforming finance by turning complex data into actionable, real-time strategies. These intelligent agents can navigate both structured and unstructured data, identify emerging trends, and generate insights quickly and accurately. However, success depends on maintaining high data quality, ensuring transparency, and upholding ethical AI practices. Organizations that embrace agentic AI thoughtfully will be at the forefront of digital transformation.