Agentic AI
Agentic AI refers to a class of artificial intelligence systems designed to operate autonomously, pursue goals, make independent decisions, and interact dynamically with their environment or other systems to achieve objectives. Unlike traditional AI models that passively process data and provide outputs based on predefined inputs, agentic AIs behave more like digital agents: they initiate actions, adapt strategies, and can perform multi-step tasks with minimal human intervention.
These agents often combine:
- Large Language Models (LLMs) for reasoning and natural language interaction
- Planning algorithms to define and adjust multi-step workflows
- Tools or APIs for execution (e.g., sending emails, initiating transactions)
- Memory modules to track context and learn from past actions
Agentic AI is a foundational concept in the evolution from narrow AI to more autonomous, goal-driven systems, often referred to as AutoGPTs, AI agents, or autonomous agents.
AI agents, autonomous agents
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Examples
A FinTech startup deploys an agentic AI to automate regulatory reporting. The AI agent is given the goal: “Generate and submit quarterly MiFID II compliance reports.”
It autonomously retrieves transaction data from internal systems, checks it against regulatory guidelines, fills out reporting templates, validates the content, and submits the reports to regulators. It also flags any anomalies and emails the compliance team for review, without needing step-by-step instructions.
In wealth management, agentic AI is also being tested to proactively rebalance client portfolios in response to market shifts within guardrails defined by a human advisor.
FAQ
Is Agentic AI safe to use in finance?
Agentic AI introduces new risks (e.g., lack of transparency, overreach, compliance violations) but also significant benefits in automation and decision-making. When designed and trained within strong ethical and operational frameworks, it can be safely integrated into regulated environments.
How is Agentic AI different from traditional AI?
Traditional AI waits for specific inputs and reacts with an output. Agentic AI, on the other hand, has initiative. It can set subgoals, adapt to changing environments, and execute actions across systems to fulfill a broader objective.
What are the key use cases of Agentic AI in FinTech?
Top use cases include intelligent client onboarding, autonomous financial advisors, fraud investigation agents, proactive compliance monitoring, and end-to-end loan origination workflows.
