Traditional Automation: Reliable but Rigid

Automation has been part of enterprise operations for decades. Examples include:

• RPA (Robotic Process Automation)
• Workflow engines
• Rule-based integrations between systems

Automation excels at repeatability and consistency. When inputs and rules are known in advance, automation can run reliably at scale.

However, traditional automation struggles when:

• Inputs are unstructured (emails, documents, conversations)
• Decisions require judgment rather than fixed rules
• Processes change frequently

Automation follows predefined paths. If something unexpected happens, it either fails or hands off to a human. This limitation often leads to brittle systems that require constant maintenance.

Agentic Workflows: Goal-Driven Execution

Agentic workflows sit between chatbots and automation—but add a critical capability: agency.

An agentic workflow uses one or more AI agents that can:

• Understand a goal
• Break it into steps
• Decide which tools or systems to use
• Adapt based on intermediate results
• Know when to involve a human

Unlike chatbots, agentic workflows are not limited to responding to a single prompt. Unlike traditional automation, they are not locked into rigid decision trees.

The key distinction is orchestration. Agentic workflows coordinate multiple actions—across tools, systems, and people—to move work forward with minimal supervision.

This makes them especially useful for knowledge-heavy, semi-structured processes where full automation is unrealistic but manual execution is inefficient.

A Simple Example: Vendor Risk Assessment Intake

Consider a common enterprise scenario: onboarding a new vendor.

Traditional View

• A form is submitted
• Analysts manually review documents
• Risk questionnaires are emailed back and forth
• Findings are summarized and approved

Agentic Workflow Approach

1. Trigger: A new vendor request is submitted.
2. Interpretation: An AI agent reviews the request and identifies required risk checks (security, compliance, financial).
3. Information Gathering:
   • Pulls existing vendor data from internal systems
   • Requests missing documents automatically
4. Analysis:
   • Reviews responses and documents
   • Flags potential risks or inconsistencies
5. Human-in-the-Loop:
   • Escalates only flagged items to a risk analyst
   • Provides a structured summary and recommendation
6. Completion:
   • Updates systems
   • Notifies stakeholders

In this workflow, the agent is not replacing human judgment. It is reducing manual coordination, handling routine steps, and ensuring consistency—while humans focus on exceptions and decisions.

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A Simple Example: Vendor Risk Assessment Intake

Consider a common enterprise scenario: onboarding a new vendor.

Traditional View

• A form is submitted
• Analysts manually review documents
• Risk questionnaires are emailed back and forth
• Findings are summarized and approved

Agentic Workflow Approach

1. Trigger: A new vendor request is submitted.
2. Interpretation: An AI agent reviews the request and identifies required risk checks (security, compliance, financial).
3. Information Gathering:
   • Pulls existing vendor data from internal systems
   • Requests missing documents automatically
4. Analysis:
   • Reviews responses and documents
   • Flags potential risks or inconsistencies
5. Human-in-the-Loop:
   • Escalates only flagged items to a risk analyst
   • Provides a structured summary and recommendation
6. Completion:
   • Updates systems
   • Notifies stakeholders

In this workflow, the agent is not replacing human judgment. It is reducing manual coordination, handling routine steps, and ensuring consistency—while humans focus on exceptions and decisions.

When to Use Each Approach

Understanding where each tool fits helps avoid overengineering or underdelivering.

Chatbots work best when:

• The goal is information access or Q&A
• Users initiate interactions
• The scope is narrow and well-defined

Traditional automation works best when:

• Processes are stable and predictable
• Inputs are structured
• Decisions follow clear rules

Agentic workflows are most effective when:

• Work spans multiple systems
• Inputs are semi-structured (emails, documents, requests)
• Decisions require context and judgment
• Human oversight is still required

Agentic workflows are not “better” by default. They are simply better suited to a specific class of enterprise problems.

How to Get Started with Agentic Workflows

For organizations considering agentic workflows, the goal should not be full autonomy. A more realistic objective is progressive assistance.

Here are practical starting steps:

1. Identify Coordination Pain Points

Look for processes where:

• Humans spend time moving information between systems
• Work stalls due to handoffs
• Exceptions consume disproportionate effort

These are often better candidates than highly transactional workflows.

2. Define Clear Boundaries

Decide upfront:

• What the agent can do independently
• When it must stop and ask for human input
• What actions require approval

Clear guardrails build trust and reduce risk.

3. Start with One End-to-End Slice

Avoid designing a “platform” on day one. Start with:

• One workflow
• One team
• Measurable outcomes (time saved, errors reduced)

4. Keep Humans in the Loop

Human-in-the-loop design is not a limitation—it’s a strength. It allows learning, oversight, and gradual expansion of scope.

5. Measure Operational Impact

Focus on metrics that matter:

• Cycle time
• Rework
• Analyst capacity
• Process consistency

These indicators provide a more honest view of ROI than usage metrics alone.

Moving From Tools to Outcomes

Agentic workflows are not about replacing people or chasing autonomy. They are about improving how work moves through an organization—especially work that is too complex for traditional automation and too repetitive for skilled teams to handle manually.

Enterprises that succeed with agentic workflows tend to treat them as operational systems, not experiments. They align them to real processes, involve domain experts, and scale carefully.

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