Agentic AI vs LLMs: What's the Difference and Why Financial Services Need Both

CTRL AI DEL is Dailoqa's weekly, no-nonsense look at what's actually happening with AI in financial services, minus the hype and the jargon. This issue breaks down Agentic AI in financial services versus traditional LLMs, in plain terms.

Agentic AI in financial services goes beyond what a traditional LLM can do. An LLM answers the question you ask. Agentic AI pursues a goal, taking multi-agent, autonomous action to get there - Enterprise banking needs both to work, together, and not one at the expense of the other.

Not Just Another Trend

Ok, so intros are over; let’s get down to business. Just last year, mentioning Multi-agentic AI would have likely been met with blank stares or confused head scratches. Fast forward to today, and it's a firmly established term in the AI vocabulary. Now, it seems everyone is talking about it and, perhaps more importantly, claiming to implement it. But let me emphasise, this isn't just another fleeting trend.

In fact, Agentic AI might be the only way to embed AI's transformative power deeply within the financial services industry. This goes far beyond simple chatbots. It offers the promise of delivering predictable and understandable results, which are crucial for satisfying both internal controls and external regulators.

What actually makes an AI system agentic, rather than just conversational, comes down to a repeating cycle. It gathers information from the systems around it, reasons about what that information means, plans a sequence of steps to reach a goal, takes action on that plan, and then reflects on whether the action worked before adjusting its next move. Strip away the buzzwords, and that cycle of gathering, reasoning, planning, acting, reflecting, is the entire difference between a system that answers you and one that works for you.

Still a little unclear on the difference between a traditional LLM (like ChatGPT) and Agentic AI?

Let's break it down in simple terms:

LLMs, The Super-Smart Librarian

Imagine an LLM with access to a vast library and a skilled librarian. This librarian can find a lot of useful information for you, summarise it, and organise it clearly.

The catch? This librarian never leaves the library. They are limited to the information inside their walls and will only answer the specific questions you ask. Some of the information in the library may be outdated. So, your questions need to be clear and accurately reflect what you want.

Agentic AI, Your Proactive Personal Assistant

Now, imagine Agentic AI as a library with a librarian, plus a personal assistant that gets things done and checks the details.

For example, if you ask this assistant to plan a party, it won’t just request ideas and themes from the LLM. It will take the initiative to check and book the venue, contact the caterers, order decorations, and send out invitations.

This assistant has a clear goal. It can make decisions and act on its own to reach that goal.

This is not just a theory. JPMorgan's autonomous anti-money laundering agents handle millions of transactions every day and have reduced false positive alerts by up to 95 per cent. This change allows investigators to focus on real risks instead of routine checks. That’s the proactive personal assistant applied to compliance instead of party planning.

There is also an important difference in memory. A traditional LLM mostly works within the conversation you provide. It can summarise a single chat but tends to lose track once that session ends. A proactive assistant remembers. It keeps track of what has already happened, what was promised, and what is still outstanding. It carries that context forward the next time it resumes a task. For a bank managing a customer relationship over months rather than minutes, this continuity is essential.

The Symbiosis of LLMs and Agents

Don't get me wrong, LLMs still play an important role in the agentic process. However, by using deterministic agents, you can achieve results that are more predictable, transparent, and easy to audit. This is especially true in areas of the process that require clear, stable outcomes. The more flexible, non-deterministic LLMs can then manage the initial interface and responses in these processes.

There’s a straightforward way to think about the trade-off: instruction versus intention. An LLM quickly and cheaply follows a single instruction. An agentic system works toward an intention, reasoning and acting through several steps, which naturally take more time and resources. The key is to match the architecture to the task.

This is exactly the pattern we mentioned in the Issue 1 regarding AI customer service, KYC, and onboarding. Neither system works well as a single, large chatbot. The best setups combine a fast, low-cost LLM layer for the conversational surface with a slower, reliable agent layer underneath for tasks that require verification, logging, and auditing. If you get that split right, you aren’t choosing between LLMs and Agentic AI; you’re using both, with each handling the tasks it performs best.

The organisations that will succeed are not the ones chasing the latest model. They are the ones building the system around it, the framework that allows a fast LLM and a careful agent to work together without either one slowing things down. That is infrastructure work, not a model upgrade. It may seem less exciting, but that’s where the real advantage lies.

If you want to know more, feel free to reach out.

FAQ 

What is Agentic AI in financial services?

Agentic AI Platforms in finincial services that goes beyond answering questions. It sets a goal, plans the steps, takes action across systems, and adjusts along the way, with limited human input at each step.

Is Agentic AI the same as a multi-agent system?

Not always, but the two are closely linked. Most Agentic AI in enterprise settings is built as a multi-agent system, with specialised agents handling different parts of a workflow.

How does Agentic AI differ from a traditional LLM like ChatGPT?

An LLM answers the specific question you ask, using only the information it has access to. Agentic AI takes a goal, plans a sequence of actions, and executes them with minimal supervision.

Is Agentic AI suitable for enterprise banking?

Yes, provided it is built with deterministic, auditable controls around the parts of the process that need predictable, explainable outcomes. That governance is what makes it viable in a regulated environment.

Can AI move beyond chatbots into real business processes with this approach?

Yes. That is the core shift Agentic AI enables, from a tool that responds to a question to a system that completes a workflow end-to-end.

Does Agentic AI replace the need for LLMs?

No. LLMs remain part of the agentic process, typically handling the flexible, language-driven parts of the interaction, while deterministic agents handle the parts that need to be stable and auditable.

How do you deploy AI safely in a regulated environment?

By combining the flexibility of LLMs with the predictability of deterministic agents, and by building in explainability and audit trails from the start rather than adding them on afterwards.

Glossary

• Agentic AI is AI that can set goals, plan, decide, and act across multiple steps with limited human input.

• Multi-Agent Systems involve several specialised AI agents working together, with each agent handling a specific part of a workflow.

• Enterprise Agentic AI refers to agentic AI deployed on an organisation-wide scale, complete with governance, integration, and audit controls.

• AI Agents in Banking are individual AI agents assigned to specific banking tasks, such as verification, routing, or monitoring.

• Deterministic AI consists of AI components designed to produce consistent, predictable, rule-bound outputs, rather than open-ended generation.

• Enterprise AI Architecture is the combined infrastructure, orchestration, and governance layer that allows AI to operate safely at an enterprise scale.

• LLM stands for Large Language Model, which is the underlying technology that enables AI to understand and generate language.

References 

1. Google Cloud: What is agentic AI? Definition and differentiators, 2026. https://cloud.google.com/discover/what-is-agentic-ai

2. Uptiq, AI Agents vs LLM Workflows in Financial Services, What's the Difference?, June 2026. https://www.uptiq.ai/blogs/ai-agents-vs-llm-workflows-in-financial-services

3. Sprinklr, Agentic AI vs LLM, The Difference That Actually Matters for Customer Experience, February 2026. https://www.sprinklr.com/blog/agentic-ai-vs-llm/