From tools to agents: the next evolution of artificial intelligence
Artificial intelligence (AI) has long supported everyday decisions, often working quietly in the background. From recommending films and suggesting driving routes to helping banks detect fraud, AI has become a powerful tool for analysing data, identifying patterns and offering recommendations.
However, this form of AI is largely passive. It can advise and inform, but it waits for a human to decide what happens next. That is now changing.
From passive support to active assistance
A useful way to understand this shift is to compare a GPS navigation system with a self‑driving car. A GPS can show the best route, but it is the driver who takes the wheel. A self‑driving car, by contrast, uses that information to navigate traffic and reach the destination on your behalf.
With the advancement of agents, AI is not just stopping at recommendations but they are beginning to take a more active role in helping achieve outcomes.
What is an AI agent?
An AI agent is a system that can understand a broader goal, determine the steps required to achieve it and act across different systems, within clearly defined boundaries.
This is often described as AI with agency. Rather than simply providing answers, an AI agent helps complete tasks. The focus shifts from generating insight to supporting execution.
From insight to execution
The difference between traditional AI and AI agents is fundamental. It marks a move from answering questions to helping deliver outcomes.
Comparison of traditional AI and AI agents.
Aspect
Traditional AI
AI agents
Example (AI agent)
Purpose
Answers questions and provides analysis.
Achieves goals and completes tasks
An agent that organises your week at work: prioritises tasks, blocks focus time, and prepares materials you need.
How it works
Waits for instructions and executes them step by step
Understands a goal and determines the steps needed
You say: “Help me get ready for my meetings this week.” The agent gathers documents, drafts emails, and schedules prep sessions automatically.
What it produces
Static outputs such as reports or recommendations
Actions across systems and workflows
Automatically prepares a briefing pack, updates project trackers, and drafts follow up notes.
Interaction style
Responds to one request at a time
Manages multi-step processes autonomously over time.
Continuously monitors your inbox, calendar and tasks, reprioritising and adjusting your plan throughout the week.
Why agentic AI matters in banking
Banking processes often involve large volumes of information, strict regulatory requirements and the need for clear accountability. AI agents can help manage this complexity by supporting structured, multi‑step tasks.
A concrete example of agentic AI currently being used at Deutsche Bank is Third‑Party Risk Management. An agentic AI solution supports the review of evidence submitted by third parties against internal control standards, using three AI agents that work sequentially to retrieve relevant control questions, analyse supporting documentation and propose assessment outcomes. Trained assessors remain fully in control, reviewing, editing or overriding the AI’s proposals before final decisions are made. This human‑in‑the‑loop design improves speed and consistency while strengthening operational resilience and accountability.
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Artificial intelligence (AI) has long supported everyday decisions, often working quietly in the background. From recommending films and suggesting driving routes to helping banks detect fraud, AI has become a powerful tool for analysing data, identifying patterns and offering recommendations.
However, this form of AI is largely passive. It can advise and inform, but it waits for a human to decide what happens next. That is now changing.
From passive support to active assistance
A useful way to understand this shift is to compare a GPS navigation system with a self‑driving car. A GPS can show the best route, but it is the driver who takes the wheel. A self‑driving car, by contrast, uses that information to navigate traffic and reach the destination on your behalf.
With the advancement of agents, AI is not just stopping at recommendations but they are beginning to take a more active role in helping achieve outcomes.
What is an AI agent?
An AI agent is a system that can understand a broader goal, determine the steps required to achieve it and act across different systems, within clearly defined boundaries.
This is often described as AI with agency. Rather than simply providing answers, an AI agent helps complete tasks. The focus shifts from generating insight to supporting execution.
From insight to execution
The difference between traditional AI and AI agents is fundamental. It marks a move from answering questions to helping deliver outcomes.
Why agentic AI matters in banking
Banking processes often involve large volumes of information, strict regulatory requirements and the need for clear accountability. AI agents can help manage this complexity by supporting structured, multi‑step tasks.
A concrete example of agentic AI currently being used at Deutsche Bank is Third‑Party Risk Management. An agentic AI solution supports the review of evidence submitted by third parties against internal control standards, using three AI agents that work sequentially to retrieve relevant control questions, analyse supporting documentation and propose assessment outcomes. Trained assessors remain fully in control, reviewing, editing or overriding the AI’s proposals before final decisions are made. This human‑in‑the‑loop design improves speed and consistency while strengthening operational resilience and accountability.
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