Traditional workflows follow a fixed, predetermined sequence. Agentic workflows take things a step further. While the Paperfly AI Inbox recognizes, categorizes, and qualifies inbound information for downstream handling, a Paperfly Agent can actively drive the resulting case forward.
It leverages enterprise knowledge and defined action capabilities to decide within strict guardrails what needs to happen next. This makes it possible to automate not just isolated tasks, but entire processing sequences until a goal is reached or a human specialist must intervene.
Series: AI Input Management & Paperfly AI Inbox in Practice
How the Paperfly AI Inbox, intelligent inbound processing, and agentic workflows capture unstructured submissions across all channels and transform them into end-to-end automated business processes.
- Part 1/4 – AI Ingestion for Banking: From Document Intake to Automated Case Processing
High document volumes in banking mailrooms and strict compliance mandates: How automated AI ingestion accelerates intake lines, reduces manual reviews, and triggers approvals straight-through. - Part 2/4 – Paperfly AI Inbox: Turning Inbound Information into Executable Processes
Inbound processing is more than data extraction: The Paperfly AI Inbox centrally captures documents across all intake channels, establishes business context, and routes cases directly into downstream systems. - Part 3/4 – AI-Driven Input Management: How the Paperfly AI Inbox Processes Documents Automatically
From automatic recognition of new document types without rigid classes to autonomous collection of missing data: How the Paperfly AI Inbox relieves intake teams right up to operational readiness. - Part 4/4 – Agentic Workflows: How Paperfly Agents Review, Complete, and Drive Cases Forward
When mailroom intake generates complex exceptions: How Paperfly Agents connect directly to the AI Inbox, leverage context and enterprise knowledge, and drive cases forward autonomously and securely.
In this article:
What is an Agentic Workflow?
An agentic workflow is a business process in which an AI agent autonomously determines the next required action within strict operational guardrails. To do this, it evaluates the current case context and intermediate results, leverages existing enterprise knowledge and authorized tools, and continues execution until the goal is achieved or a defined control threshold is reached.
An agentic workflow combines five core characteristics:
- A defined objective, rather than a hardcoded, rigid sequence of steps
- Context and intermediate results that dynamically influence the subsequent path
- Enterprise knowledge and tools accessible to the agent
- Actions whose outcomes are evaluated in real-time
- Control boundaries where cases can be routed cleanly to a human specialist when necessary
Within the Paperfly architecture, agentic execution begins right where the AI Inbox leaves off—after inbound information has been parsed and business context established.
The Paperfly Agent then takes over tasks where the subsequent path depends entirely on the specifics of the case. For example, when processing a tax exemption certificate, it can extract details, evaluate compliance, and independently determine how the case should be routed and submitted.
What Sets Agentic Workflows Apart from Traditional Workflows?
In a traditional workflow, steps, conditions, and processing branches are strictly predefined. An agentic workflow, by contrast, determines the next valid action dynamically based on current context and prior outcomes.
The fundamental distinction lies in decision-making logic during execution. Traditional workflows follow static, modeled logic: if Condition A is met, trigger Step B, then Step C. This ensures predictability and works exceptionally well for repetitive, highly standardized routines.
Traditional Workflow vs. Agentic Workflow: Key Differences
| Traditional Workflow | Agentic Workflow | |
|---|---|---|
| Execution Flow | Strictly predetermined in advance | Adapts dynamically based on case specifics |
| Decision Making | Predefined rules and conditions | Context, business rules, and intermediate results |
| Actions | Follows a hardcoded process path | Selects from a set of permitted enterprise actions |
| Exceptions | Must be explicitly anticipated and modeled | Handled situationally within defined guardrails |
| Human Touchpoints | Triggered at fixed process gates | Triggered based on outcomes, exceptions, or risk thresholds |
Deterministic workflows form the structural backbone, while agentic components are deployed where downstream paths depend entirely on situational context. It is always recommended to select the simplest suitable architecture and verify whether a given business process truly requires that additional decision-making flexibility.
In Paperfly, a Dynamic Workflow is often the optimal choice when execution can be driven reliably via structured data, status tracking, document logic, and explicit rules. Paperfly Agents step in where the next step must be derived dynamically from current context and prior runtime results. The objectives, allowed actions, and operational boundaries are established within the corresponding AI Flow.
An Agentic Workflow is ideal when …
- Multiple systems or knowledge bases must be consulted before the next step can be determined.
- Follow-up inquiries and actions depend entirely on the individual case, making it impossible to pre-model every branch.
- Several valid processing paths exist, from which the agent selects based on real-time context.
- Exceptions occur regularly, but can be resolved safely within defined operational parameters.
What Enterprise Knowledge Does a Paperfly Agent Need?
A Paperfly Agent requires domain context to advance a case meaningfully. Depending on the use case, this includes product and contract rules, processing policies, required verification documents, departmental responsibilities, exceptions, and escalation protocols. This knowledge dictates which data attributes are relevant, what validations are required, and what actions the agent is permitted to execute.
A Paperfly Agent bridges multiple operational layers:
- Case Context: What has occurred so far, and what data is already available?
- Enterprise Knowledge: What product, contract, and process logic apply?
- Validation Rules: What criteria, exceptions, and control thresholds must be respected?
- Tools and Systems: Which data sources, APIs, or core applications is the agent authorized to access?
- Permitted Actions: Which steps is it allowed to trigger independently, and when must a human specialist take over?
Knowledge alone is not enough, the agent must be empowered to act upon it securely.
This precise combination enables a Paperfly Agent to not only interpret a business case, but classify it accurately, execute governed next steps, and escalate smoothly to human staff when necessary.
How a Paperfly Agent Decides What Happens Next
The defining difference from a one-off AI evaluation is continuous re-evaluation. Executing an action does not automatically terminate processing. Its outcome alters the active state of the case, which in turn may dictate a completely different subsequent step.
This execution loop can be broken down into five distinct phases:
- Capture Context: What information is currently present? What has changed since the previous step?
- Evaluate Situation: What is still missing, what rules apply, and what is the target objective?
- Select Action: Which of the permitted operational actions will meaningfully advance the case?
- Execute Action: The agent invokes a connected system, initiates a workflow, issues an inquiry, or updates records.
- Verify Outcome: Did the action succeed and was the objective met? If not, evaluation begins anew with the updated case state.
This principle follows a recurring "Sense-Think-Act" cycle, where the result of an action is observed before determining the next move. An agent can choose to continue execution, pause, or route the case to a human specialist.
Paperfly AI Flows, Dynamic Workflows, and Agentic Workflows
Not every automated business process requires a Paperfly Agent. The deciding factor is how much of the processing path can be predetermined by rules versus how much situational decision-making is required during execution.
Which Approach Fits Which Operational Task?
| Operational Requirement | Recommended Approach |
|---|---|
| Capture, understand, and qualify inbound information. | Paperfly AI Inbox |
| Subsequent execution can be steered via defined data attributes, statuses, and rules. | Paperfly Dynamic Workflow |
| Specialized AI tasks defined by explicit objectives, actions, and boundaries. Paperfly AI Flows provide the conceptual framework and guardrails. | Paperfly AI Flow |
| An agent processes tasks autonomously based on established AI Flows, deriving next steps from context and runtime results within strict guardrails. | Agentic Workflow / Agent |
| Expert review, formal approval, or human oversight is strictly required. | Human Task |
The Paperfly AI Inbox acts as the primary intake gateway. It identifies and qualifies inbound data and can subsequently launch the appropriate AI Flow, workflow, or agentic workflow.
Paperfly Dynamic Workflows orchestrate execution based on explicit process, data, and document logic. Depending on case specifics, different branches, collection requests, or approvals are triggered. The decision framework is embedded directly within the workflow model.
Paperfly AI Flows combine multiple specialized AI tasks into a defined processing chain—unifying document analysis, data extraction, validation checks, decision points, and follow-up actions.
Paperfly Agentic Workflows go a step further. Here, a Paperfly Agent leverages permitted action boundaries established by the AI Flow to autonomously determine the next required task based on current case state. The agent decides what it is allowed to do while selecting the appropriate next step within that perimeter. Paperfly Agents function as pre-configured specialists capable of guiding a case independently through multiple processing stages.
Human Tasks represent controlled handovers to human employees. When a case hits a defined control limit or requires formal sign-off, automated execution pauses and routes cleanly to a specialist.
In practice, these building blocks operate in harmony within the same business process. A Paperfly Agent can trigger a Dynamic Workflow for a missing document request, leverage an AI Flow for specialized document validation, and generate a Human Task for complex edge cases.
How Paperfly Agents Integrate into Existing Core Systems
The case processed by a Paperfly Agent typically originates from the Paperfly AI Inbox, which establishes initial business context and parses inbound records. To drive execution forward, the agent accesses a dedicated knowledge base and strictly authorized tools and systems.
In Paperfly, enterprise knowledge relevant to the agent is housed in a separate knowledge repository. Product specifications, processing rules, required proofs, exception guidelines, and escalation logic are stored securely here. The agent queries a clearly defined knowledge base tailored precisely to its operational scope.
Knowledge and execution capabilities remain strictly separated:
The agent is never granted blanket access to "the enterprise." It operates exclusively with the knowledge, interfaces, and actions explicitly provisioned for its role. This establishes a fully controllable framework while eliminating concerns about autonomous AI unrestrictively querying internal data or systems.
Takeaway: From Analyzed Case to Executed Action
Agentic workflows become vital when AI needs to go beyond passive analysis and execute controlled actions based on active case states. Determining the next required step is driven dynamically by context, enterprise knowledge, business rules, and the outcomes of prior steps.
FAQ: Frequently Asked Questions About Agentic Workflows
How do agentic workflows remain controllable and secure?
Agentic workflows remain fully controllable because data access permissions, action capabilities, validation rules, and handover points are defined in advance. In Paperfly, agents operate within strict perimeters established by AI Flows. Whenever a risk, quality, or decision threshold is reached, the case routes cleanly to an employee via a Human Task.
Can agentic workflows operate fully autonomously?
Agentic workflows can execute multi-step processing phases autonomously, but they do not need to operate without oversight. The allowable scope of autonomy is configured per business process. In Paperfly, sensitive decisions, formal approvals, and complex exceptions can intentionally remain with human staff while standard cases proceed straight through.
Which processes are best suited for agentic workflows?
Agentic workflows are ideal for processes where the next operational step depends on live context, new data points, or intermediate runtime results. Typical characteristics include variable inputs, multiple knowledge sources, iterative inquiries, and branching processing paths. For fully predictable routines, traditional deterministic workflows remain the simpler solution.
Do agentic workflows require direct access to internal enterprise databases?
An agentic workflow requires relevant enterprise knowledge, but not raw internal database access. In Paperfly, required business knowledge is provided securely within a separate knowledge base. Case data, knowledge repositories, and operational system integrations are kept strictly separated and controlled.
Can agentic workflows interact with existing line-of-business systems?
Yes, agentic workflows can interact with existing enterprise software when equipped with explicit APIs and permissions. An agent can retrieve records, launch downstream workflows, or return finalized results. Paperfly supports integrations with APIs, DMS platforms, CRM suites, ERP systems, and core banking or insurance applications.
What is the difference between an AI Agent and generative AI?
Generative AI typically generates text or answers based on a prompt. An AI Agent goes further by evaluating context, deploying knowledge bases and tools, executing actions, and verifying outcomes. This enables it to drive a business transaction autonomously across multiple processing steps within defined guardrails.