AI Shouldn't Improve Workflows, It Should Replace Them. Here's How to Do It

The market is flooded with chatbots that summarize requirements, GenAI that drafts policies, and AI assistants that extract provisions from contracts. And these tools undoubtedly make existing workflows better. But when it comes to transformational technology, different is better than better.

These AI for GRC capabilities are the direct result of practitioners and vendors alike asking, "How can AI make our current workflows better?" What they should be asking is "Does AI make a completely new way of operating possible?"

Agentic GRC doesn't improve GRC workflows; it replaces them with agents. For something to earn the title agentic, it needs to take an entire workflow, including the decision-making between each step, and execute it from start to finish. Whether teams are ready for the future or not (and they should be), they need to start thinking about their workflows in an entirely new way. Here's a framework for them to do so.

Why the Distinction Between AI for GRC and Agentic GRC Matters

The most critical difference between AI for GRC and agentic GRC is that the former doesn't really move the needle when you manage a program at enterprise scale. If your "agent" helps you draft a policy but you still need to review it, approve it, route it for signatures, track completion, and update your records, you haven't eliminated the workflow; you've just automated one step.

Agents make decisions at each step, produce outcomes, and move to the next task without waiting for you. It is these abilities that make agentic GRC so valuable, especially for enterprise GRC teams.

However, building agentic GRC is no small feat. It requires access to deep context, advanced production capabilities that need to be baked into the solution rather than tacked on, and a rigorous methodology to ensure the agents perform what they are required to and that they do so accurately.

But before any of that, it requires a fundamental shift in how GRC teams think about their work. Throwing AI at an existing workflow doesn't result in an agent. You need to reimagine those workflows through the lens of autonomous execution. What needs to happen? What decisions need to be made? What outcomes matter? This mental model shift is just as important as the technology.

Below is the methodology we have developed with both GRC and AI experts to build and deploy agents that replace workflows, and how it would apply to a CCM agent.

A Framework for Building GRC Agents

Step 1: Workflow Classification and Scope Definition

Begin by mapping the complete workflow structure from initiation through completion. This classification determines what the agent needs to accomplish and the boundaries within which it operates.

Key questions to answer:

• What is the workflow's objective, and what information is required to achieve it?
• Is this a single-step or multi-step process with conditional branching?
• At which points, if any, does the workflow require external authorization to proceed?

Application to Continuous Controls Monitoring (CCM):

Traditional CCM workflows involve scheduling evidence collection, gathering evidence manually, reviewing against control requirements, making compliance determinations, identifying gaps when controls fail, opening remediation tickets, and tracking completion. Each of these steps requires human intervention. An agentic approach executes the entire workflow autonomously: the agent identifies required evidence, collects it from relevant systems, evaluates compliance against defined criteria, determines status, identifies gaps when controls fail, opens remediation workflows with contextual details, and validates completion once remediation is finished.

This classification establishes the agent's complete scope and informs architectural decisions for the remaining steps.

Step 2: Trigger Architecture Design

Define the complete set of conditions that should initiate the agent's execution. Unlike traditional automation that relies on temporal scheduling, agentic systems require multi-dimensional trigger architectures.

Establish triggers across three categories:

• Temporal: Periodic execution based on time intervals or specific dates
• Event-driven: Real-time response to system state changes or external events
• On-demand: Manual invocation for ad-hoc requirements

Application to CCM:

A production CCM agent operates with temporal triggers (weekly execution for high-risk controls), event-driven triggers (automatic execution when new cloud resources are provisioned), and on-demand triggers (immediate execution when auditors request current evidence).

Triggers invoke the agent to go and execute the relevant tasks autonomously. This eliminates dependency on human memory or manual scheduling.

Step 3: Decision Logic Specification

Document the complete decision-making framework the agent will apply throughout workflow execution. This is where many implementations fail because they optimize for conversational interfaces rather than deterministic workflow execution. The prerequisite for any agent to be able to follow the framework and execute it accurately is deep context.

Define four critical components:

• Objective statement: Precise definition of what the agent must accomplish
• Context: All information and criteria the agent needs to make decisions
• Execution steps: Sequential or conditional logic that the agent follows to make a decision based on the accessible context
• Decision criteria: Explicit rules for each decision point in the workflow

Application to CCM:

The agent's decision logic specifies: objective ("Evaluate SOC 2 access control CC6.1 compliance status"), context (control requirements from your SOC 2 scope, evidence types that satisfy the control, testing procedure for pass/fail determination), execution steps (authenticate to AWS, collect CloudTrail logs for the evaluation period, parse for access events, map events to control requirements, identify violations, determine compliance status), and decision criteria (what constitutes a passing control, what severity level different violations represent, what threshold triggers escalation).

Without this level of specification, you have an assistant that requires interpretation, not an agent that executes workflows.

Step 4: Outcome Definition and Integration

Specify the complete set of artifacts and actions the agent must produce and execute upon workflow completion. Real agents don't generate insights for human review. They produce structured outcomes that advance business processes.

Define outcomes across five dimensions:

• Artifact generation: What records, reports, or documentation must be created
• System integration: Which systems must be updated with workflow results
• Workflow triggering: What downstream processes should be initiated based on outcomes
• Stakeholder notification: Who needs to be informed and with what level of detail
• Audit trail creation: What evidence must be preserved for compliance verification

Application to CCM:

Upon completing control evaluation, the agent generates compliance findings with attached evidence, updates control testing status in your GRC platform, produces audit-ready documentation formatted to auditor requirements, triggers remediation workflows when controls fail (creating tickets in your issue tracking system with relevant context), notifies control owners and stakeholders with actionable information rather than generic alerts, and creates a complete audit trail documenting the evaluation process, evidence reviewed, and determination rationale.

Step 5: Validation of Implementation

Establish systematic validation to ensure workflow execution completeness and accuracy. This is not about checking the agent's work. It's about confirming the agent executed all required steps and produced expected outcomes.

Validation operates at two levels:

• Execution completeness: Verify all workflow steps were performed (evidence collected from all required sources, all controls evaluated, all outcomes produced)
• Outcome accuracy: Confirm outputs meet quality standards and business requirements

Application to CCM:

Validation confirms: evidence collection accessed all required systems and time periods, all in-scope controls received evaluation, findings documentation includes necessary evidence attachments, system updates reflect current control status, stakeholder notifications reached intended recipients, and the complete audit trail is available for review.

The critical distinction: validation confirms the agent completed its defined workflow, not whether it made the "right" decision at each step. Decision-making criteria were defined in Step 3. Validation ensures the agent followed that framework.

Start Thinking in Agents, Not Workflows

The methodology above isn't theoretical. It's how we've built every production agent we've deployed. The framework applies whether you're building CCM agents, policy management agents, or entirely new GRC workflows that haven't been automated before.

Manual workflows are dying, automated workflows aren't far behind. Management won't accept models that require constant human intervention when agentic alternatives exist.

GRC teams that want to stay ahead should start applying this methodology to their workflows now. Even if your current tools can't orchestrate full agent execution, even if you don't have the data foundation you need, the exercise of breaking down your workflows through this framework prepares you for what's coming.

The teams that start thinking in this framework today will be the ones defining how GRC operates tomorrow.

About the Author: Yair is a CEO on a mission to remove the frustration that typically accompanies enterprise GRC. Yair served as the head of the R&D department of the IDF's elite 8200 unit. After leaving the military, he led the Innovation Group at Intsight, where he successfully brought new products into a highly competitive market. In 2020, Yair co-founded Anecdotes to revolutionize enterprise GRC. His vision for a data and agentic AI powered GRC ecosystem has completely transformed the way many of the world's top organizations manage their programs today.