How to Succeed with Agentic AI: Give It Major Tasks, But Don’t Hand It Entire Jobs
By Max Vermeir vice president of AI strategy at ABBYY.
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Imagine building a self-driving car. You would not drop it onto a busy highway and tell it to figure out the rules of the road. First, you would have to teach it context, give it a map, and clearly define its operational boundaries.
The same rule applies to AI agents in banking. When financial providers give an agent too much freedom to replace an entire role, costs climb, and workflows break down.
Redesigning work means assigning specific tasks, not entire jobs.
Key insight: Capability without structure does not scale — robust system architecture must be the foundation for predictable artificial intelligence.
Need to Know:
- Artificial intelligence agents thrive on strict boundaries, turning messy human roles into predictable, automated outcomes.
- Replacing a role is a flawed strategy because it attempts to automate the chaos around a process rather than the process itself.
- Guardrails keep operational costs low by offering controls that prevent processing expenses such as excessive token usage. (Tokens represent how AI processing is allocated.
- Proof precedes reasoning — ensuring intelligent systems only act on trusted, verified data.
- System architecture defines the true value of an agent, giving it the precise map it needs to communicate decisions.
Breaking Down the Automation Illusion
There is a strong narrative going that intelligent agents can completely replace complex banking roles such as mortgage underwriters.
The promise of fewer people and faster decisions sounds great — until the computing bill arrives.
Without clear boundaries, agents try to solve each edge case they encounter — unusual, atypical situations. This stance consumes tokens at every stage of reasoning, tool calling, and memory lookup — driving up costs as the agents relentlessly go on.
Key tactical insight: You are not automating a process when you deploy unstructured artificial intelligence. You are letting an intelligent agent wander through your system with free rein over what it should or should not do. The agent has no context, no structure, no control.
When you consider using this agent to replace an entire job, you are making the assumption that every aspect of a function’s day-to-day decision making can be managed with no prior training.
Does that make sense? A job consists of a complex network of small decisions, undocumented workarounds, and unwritten rules. Trying to automate an entire role scales complexity, not efficiency. And it dangerously underestimates the importance of human judgment.
Why this matters: Uncontrolled agent workflows often exceed the cost of the human workers they were supposed to replace.
It’s important to:
• Deconstruct existing roles into specific, isolated tasks.
• Define strict inputs, outputs and operational boundaries for every automated step.
• Focus on replacing individual workflow components rather than the human worker.
The upshot is that giving an agent a narrow task with controlled data means it will perform consistently well.
Read more: How Financial Institutions Must Update Data Strategies for the Next Wave of AI
Building Trust Through Verified Data
Consider a complex banking scenario like mortgage know your customer (KYC) onboarding, which we’ll use as a running example. This process involves multiple document types, edge cases and a mix of structured and unstructured information.
Instead of blindly handing over the keys to an agent, structure the process in distinct stages.
The first step is making your data consumable for AI models. Using Document AI enables you to separate and classify the files and extract the key data to create a reliable, structured view of customer submissions. You want facts, not generative assumptions. (Document AI techniques develop computer models capable of analyzing documents in a manner similar to human review.)
Key insight: There is absolutely no value in analyzing or reasoning over data you cannot trust.
• Use Document AI to extract facts before any reasoning occurs.
• Remove generative AI functionality from the initial data gathering phase.
• Build a structured, verified baseline of customer submissions.
Bottom line: Ground your automation in verified data extraction to prevent compounding errors downstream.
Read more: The Next Wave of AI in Banking Will Have Nothing to Do with Technology
Set Control Points to Safeguard Your Process
You cannot assume every submitted document is legitimate — in fact it’s critical not to.
Before any further processing happens, you must introduce strict control points. Assess the documents to determine if they have been artificially generated or tampered with. If the evaluation is not perfectly clear, the system should follow an exception path to notify a fraud team for human review.
Next, validate the data deterministically: Ask binary questions:
• Do the bank statements align with the paystubs?
• Do the documents belong to the same person?
• Are the forms complete?
If these checks fail, the process will adapt. The system creates a structured summary highlighting the missing or mismatched data.
The challenge: Agents fail when they attempt to perform deep analysis on incomplete or inconsistent data.
• Implement fraud detection immediately after document extraction.
• Create dedicated exception paths for flagged or suspicious files.
• Apply deterministic checks to ensure data consistency before invoking a large language model.
Bottom line: The process must always produce a structured outcome, whether an application passes verification, fails validation, or triggers a fraud alert.
Read more: How a 160-Year-Old Law Will Regulate AI for National Banks for Years to Come
The Agent as a Scoped Communicator
Don’t introduce a large language model to your process until your data is completely valid and consistent. Even then, keep its scope tight.
Do not ask the agent to run underwriting logic, perform extraction, or look at raw documents. It should act more like a highly efficient dispatcher. It receives a clean summary and performs a single task, which is communicating the outcome to the internal case team and the customer.
Tactical insight: The key is to have the system architecture do the heavy lifting. The agent should simply act on a defined state. You can add more validation checks or expand your document types without ever changing the core role of the agent. This approach orchestrates the flow brilliantly and scales easily.
Opportunity: Supplying agents with structured summaries will keep token usage low and behavior highly predictable.
• Limit large language models to analyzing transaction data and summarizing financial behavior.
• Design the system so the agent only handles communication and next steps.
• Scale your workflow by adding upstream validation checks rather than expanding the agent’s responsibilities.
Key takeaway: Stop focusing on what the agent can do and start designing the architecture that gives it the right information to succeed.
Read next: How to Pivot AI Momentum to Measurable Banking Results
