What are agentic workflows?
Agentic workflows are AI-driven processes where autonomous agents plan, reason, and execute complex tasks with minimal human oversight. You tell the system what outcome you want. It figures out how to get there.
This matters for banking because your operations are full of multi-step processes that require judgment at every turn. Dispute resolution. Loan origination. Customer onboarding. These workflows have historically required humans to navigate systems, interpret data, and make decisions. This page covers how agentic workflows actually run, step by step. For the category-level view, including the architecture and governance behind it, see agentic banking.
An agentic workflow changes that equation. The AI agent receives a goal, breaks it into subtasks, takes action, observes the results, and adjusts its approach if something goes wrong. It operates in a loop of thinking and doing until the job is done.
- Goal-oriented: You define the outcome. The agent determines the path.
- Adaptive: When conditions change, the agent adjusts its plan.
- Autonomous: It works independently, escalating to humans only when necessary.
How do agentic workflows work?
The core of an agentic workflow is a reasoning loop. The agent doesn't execute a single command and stop. It enters a cycle that continues until it achieves the goal or determines it needs help.
Here's what that loop looks like in practice. A customer submits a dispute. The agent receives this goal: "Resolve this dispute." It plans the steps. Verify identity. Pull transaction history. Check policy. Draft a response. It executes each step, observes whether it worked, and moves to the next.
If the transaction data is incomplete, the agent doesn't crash. It recognizes the gap and queries another system. If the dispute falls outside policy parameters, it routes the case to a human reviewer with a full summary attached.
- Plan: Map out the steps to achieve the goal.
- Act: Use tools like APIs and databases to gather data or make changes.
- Observe: Check the results of each action.
- Reflect: Adjust the plan if something fails or new information appears.
This ability to reflect and iterate is what separates agentic workflows from traditional automation.
How the runtime works
Trigger and goal
A run starts with a trigger and a goal. A trigger can be an incoming request or a change in a record. The goal is a defined outcome, such as a completed review.
A clear goal gives the agent a measure for each step. It also defines when the run is finished.
Persistent task state
In this model, the system creates a task record when the run starts. The record can hold the goal, the current plan, completed steps, tool outputs, and open work.
Persistent state lets a run pause and resume. If the agent waits for a reply, the next step can start from the record.
Because the record lives outside the model, a reviewer can read what was done and why. Every later stage in this article depends on it.
Plan
The agent drafts a plan from the goal and the record. A plan might read: check the input, retrieve the relevant records, apply the applicable policy, draft an output.
The plan is a working hypothesis. The agent can revise it after any step.
Proposed and validated tool call
The language model proposes a tool call, such as "retrieve the records linked to this request." In this design, the orchestration software then validates the proposal.
Validation might check that the tool exists and that the agent has permission to use it. A rejected proposal can return to the agent with a reason.
These checks are design choices. A given workflow may include some of them, all of them, or other controls.
Execution
In this model, the orchestration software executes validated calls against the target system. Placing permissions and logging at this point can give them one consistent home.
That placement is a design choice that teams make deliberately.
Observation
The tool's output returns to the agent as an observation. The system writes it to the task record, and the agent uses it in the next reasoning step.
An observation can be a full result, a partial result, or an error. The agent reads each one and acts on it.
Replanning
After each observation, the agent assesses whether the result moves it toward the goal. A helpful result lets the plan continue. An unhelpful result prompts a revision.
Suppose a retrieved record holds two conflicting entries. The agent adds a step to compare them and updates the task record.
A retry is another option here. When a call times out, the design may allow a further attempt. A retry limit can cap attempts, and a retry can still fail.
Exceptions: inside the loop and outside it
Each exception needs a defined route in the design. The route decides whether the run keeps going or stops.
Exceptions resolved within the loop
Some exceptions let the agent continue. Say a required field is empty in a retrieved record. The agent queries another system and records the answer.
Timeouts and partial results can follow the same path. The agent replans or retries, and the run stays in the loop.
Exceptions that stop or leave the loop
Other exceptions end autonomous work. Two abstract examples:
- Policy limit. The requested action exceeds what the agent may do alone. The run routes to a person.
- Low confidence. The evidence points in conflicting directions. The agent stops and escalates.
A stop condition can also end a run. Examples include a step ceiling or a retry ceiling, set outside the model.
Termination and handoff
A run ends when the goal is met or a stop condition fires. It also ends when a person takes it over.
A well-built handoff passes the task record to the reviewer. The reviewer sees completed steps, evidence, and the open question. The work already done stays done.
When the reviewer decides, the decision can be written back to the record. The run then closes with a full trail.
Agentic workflows vs. traditional automation in banking
Traditional automation follows a script. Step A triggers step B. If anything deviates from the script, the process breaks.
Robotic Process Automation (RPA) has served banks well for repetitive, predictable tasks. But RPA is brittle. A UI change can break the bot. An edge case can stall the entire queue. You end up maintaining thousands of scripts and handling the exceptions manually.
Agentic workflow automation works differently. You give the agent a goal and constraints. It figures out the execution path on its own. When variables change, it adapts.
- RPA: Follows fixed rules. Breaks on unexpected input. Requires constant maintenance.
- Agentic: Follows goals. Handles exceptions. Improves over time.
For your bank, this means fewer broken processes and fewer tickets landing on a human's desk because the bot couldn't handle a slight variation in agentic AI execution.
Core components of agentic workflows
Building an agentic workflow requires specific components working together. Think of it as assembling a digital worker with a brain, hands, and a set of instructions.
AI agents
An AI agent is the software entity that does the work. It's the actor in your workflow. The agent maintains the state of the task, remembers what's been done, and decides what to do next.
You can build general-purpose agents that handle many tasks. Or you can build specialized agents for specific roles. A "KYC Agent" that handles identity verification. A "Collections Agent" that manages payment arrangements.
Large language models
The Large Language Model (LLM) is the reasoning engine. It gives the agent the ability to understand natural language, interpret documents, and generate plans.
The LLM doesn't store your data. It processes information in real time. This allows the agent to read a messy customer email, understand the intent, and decide how to respond.
Prompt engineering
Prompt engineering is how you give the agent its instructions. The "system prompt" defines who the agent is, what it can do, and what it cannot do.
Good prompts constrain the agent to safe banking concepts. They set the tone for customer interactions. They establish the rules for compliance and escalation.
Tools, data, and feedback loops
An agent without tools is a chatbot. It can talk, but it can't act. Agentic workflow tools are the APIs and integrations that connect the agent to your banking systems.
- Retrieval tools: Search your knowledge base or policy documents.
- Action tools: Call APIs to freeze a card, update an address, or initiate a transfer.
- Calculation tools: Run scripts for accurate financial math.
Feedback loops tell the agent whether its actions worked. If an API call fails, the agent sees the error and tries a different approach.
On the Banking OS, these pieces have names. Nexus feeds the agent its context: the Customer State Graph that tells it what it needs to know before acting. Sentinel checks every action against policy before it executes and issues a Decision Token recording what happened. Grand Central is what lets the agent's action tools actually reach core banking, payments, and CRM systems. That happens without a custom integration for each one.
Benefits of agentic workflows for banks
Banks that adopt an agentic approach unlock value that traditional automation can't touch. You scale personalized service without scaling headcount.
- Lower cost-to-serve: Agents handle complex, multi-step processes that used to require human intervention, with banks potentially achieving 15 to 20 percent cost reductions through moderate AI adoption.
- Faster resolution: Processes that sat in queues for days now complete in minutes.
- Consistent compliance: Agents apply policy rules the same way every time.
Your human staff gets freed from copy-pasting data between fragmented systems. They can focus on advising customers and building relationships. The agent handles the complexity of execution.
Agentic workflow use cases across the banking frontline
The best way to understand this technology is to see it applied to real banking workflows. These examples show how agents transform operations across the frontline.
Onboarding and account opening
Onboarding involves document collection, identity verification, KYC checks, and account provisioning. An agent orchestrates the entire journey.
The agent requests documents from the customer. It analyzes uploaded files to verify identity. It checks data against risk policies. It provisions the account in the core system.
If a document is blurry, the agent notices immediately and asks for a new upload. This reduces drop-off compared to static forms that reject applications days later.
Customer service and case resolution
Agents triage incoming requests across channels. They gather context from multiple systems. They propose resolutions and escalate only when necessary. Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention.
The agent reads a secure message. It checks recent transaction history. It identifies the likely issue. It drafts a response for a human to approve.
This creates a human-in-the-loop model where AI does the heavy lifting and humans provide final sign-off.
Lending origination and document validation
Lending involves massive document processing. AI agentic workflows streamline underwriting preparation.
The agent extracts income data from tax returns. It calculates debt-to-service ratios. It flags inconsistencies between the application and supporting documents.
Underwriters focus on credit decisions. Data entry disappears.
Collections and financial hardship support
Collections require empathy and negotiation. Agents handle these conversations without judgment.
The agent analyzes the customer's financial situation. It proposes a personalized repayment plan based on policy and ability to pay. It guides the customer through hardship applications.
Customers who feel embarrassed often engage more openly with an agent than with a human collector.
Fraud operations and dispute handling
Fraud analysts drown in false positives. Agents act as the first line of defense.
The agent investigates a flagged transaction. It checks device location, spending patterns, and merchant history. It compiles a case file summarizing whether the alert is likely a false positive or actual fraud.
Investigators make faster decisions because the data gathering is already done.
Relationship manager and contact center copilots
Agents don't always face customers directly. They serve as copilots for your staff.
The agent listens to a live call. It retrieves relevant product information. It suggests the next best action to the banker.
Every banker performs like your best banker. Knowledge becomes instantly accessible.
Guardrails and control for agentic workflows in regulated banking
You can't unleash autonomous AI in a regulated industry without controls. Safety is non-negotiable.
- Bounded context: Limit the agent's access to only the data and tools needed for its specific task.
- Human-in-the-loop: Require human approval for actions that move money or change customer data.
- Audit trails: Log every decision the agent makes, including what data it used and what policy it applied.
Sentinel enforces all three of these in production. It checks every action against policy before execution. It issues a Decision Token for each one, so nothing above runs on trust alone.
You need to explain why the agent made a decision. This explainability satisfies regulators. It also builds trust with your customers and staff.
Continue reading: How to prioritize agentic AI use cases in banking
Frequently asked questions about agentic workflows
What is the difference between an agentic workflow and an AI agent?
An AI agent is the reasoning component. An agentic workflow covers the full run around it, including task state, tool calls, exception routes, and stop conditions.
What is persistent task state in an agentic workflow?
Persistent task state is a record kept outside the model. It holds the goal, plan, completed steps, and tool outputs. A run can resume from it, and a reviewer can read it.
What happens when an exception occurs?
The design routes it. Some exceptions are resolved within the loop through a new query or a replan. Others stop the run or send it to a person.
What is an observation in an agentic workflow?
An observation is the result of a tool call. It returns to the agent and is recorded, and the agent uses it to decide the next step.
When does an agentic workflow hand off to a person?
A handoff can happen when a policy limit applies or confidence is low. It can also happen when a stop condition fires. The task record gives the reviewer context to continue.
Do agentic workflows replace deterministic workflows?
Agentic workflows can run alongside deterministic workflows in the same process. Deterministic logic can set boundaries, and the agent reasons within them.
