Agentic AI GRC Platform Evaluation Guide

The GRC software market has moved fast to attach 'agentic AI' to existing products. Some of those claims are substantive. Many represent automation with a more sophisticated interface. The commercial incentive to use the term is high, and the confusion it generates has settled into the market.

This guide uses 'agentic AI' to mean systems that receive a goal and autonomously determine the sequence of actions needed to achieve it, using tools to query systems, call APIs, and retrieve documents without explicit step-by-step instruction, adapting their approach based on intermediate results. A system that executes a pre-defined workflow when triggered is automation. Both have value in a GRC stack. They serve different purposes and carry different accountability implications.

The evaluation framework below draws on accountability requirements of ISO 42001:2023 (the international standard for AI management systems), EU AI Act Annex III obligations for high-risk AI systems (applicable from August 2026), and operational expectations set by the Financial Conduct Authority (FCA) and European Banking Authority (EBA) for AI used in regulated functions.  

Task autonomy depth measures how independently the system can pursue a compliance objective: how many steps it takes without human instruction, how many tools it uses, and how it handles branching decisions when initial approaches fail.

What Genuine Agentic Capability Looks Like

A genuinely agentic system should be able to receive a goal ('identify controls due for testing this quarter and collect current evidence'), determine which systems to query based on its understanding of the control framework, retrieve and assess evidence without per-step instruction, and flag exceptions with full context attached. The system plans and reasons; execution follows from that planning. A complete workflow runs even when some evidence sources are unavailable, with documented handling of the gap.

What Automation Marketed as AI Looks Like

Automation marketed as agentic executes a defined sequence of tasks triggered by an event or schedule. Each step is pre-programmed. The system errors, halts, or falls back to a default when a step fails, with no capacity to adapt.

Ask vendors specifically what happens when a data source is unavailable, and what the system does when evidence does not match the expected format. A genuinely agentic system has a documented approach to these cases. An automation tool will have a fallback script or an error state.

Vendor Questions for Task Autonomy Depth

1. Can you demonstrate the system handling a multi-step compliance workflow where at least one data source returns an error or unexpected output? Show us the actual system behaviour, not a diagram.
2. How does the system determine which tools or data sources to use for a given goal? Is this logic hard-coded per workflow, or does the agent determine it at runtime?
3. What is the maximum number of sequential autonomous steps the system has taken in a live production deployment, and in what compliance context?

Human-override design evaluates how the platform structures the boundary between autonomous action and human decision-making. For regulated use cases, this is the most important safety and accountability dimension.

What Good Override Design Looks Like

A well-designed platform treats human override as a core governance feature. Override points should be configurable by decision type and consequence tier; the system should pause at a defined decision point, present its reasoning and supporting data, and wait for human input before proceeding.

Override events should be logged with the same completeness as autonomous actions, including who overrode, when, and what the alternative decision was. Override should also be accessible without engineering intervention: a compliance officer should be able to adjust the decision boundary for a specific workflow without a code change.

The Compliance Test for Override Design

EU AI Act Article 14 requires that high-risk AI systems be designed to enable effective human oversight, including the ability to intervene in or interrupt the system. ISO 42001:2023 Clause 6.1 requires that organisations assess the consequences of AI decisions and implement controls proportionate to those consequences. The requirements for high-risk AI deployments are specific: override controls must be configurable by decision tier, logged with full completeness, and accessible to compliance officers without engineering support.

Vendor Questions for Human-Override Design

1. Show us how a compliance officer, with no engineering support, configures which decision types require human approval before execution. What does that configuration interface look like?
2. When a human overrides a system recommendation, what is captured in the audit log? Provide a real production log entry from a live deployment.
3. Can the system be paused mid-workflow for human review without losing the workflow state? How is that pause triggered and resolved?

Audit trail quality is a pass/fail dimension for regulated environments. Every compliance context in which autonomous agent actions face regulatory scrutiny requires a complete, immutable, and queryable record of those actions. A platform lacking that record is unsuitable for regulated deployment.

The Minimum Standard

A compliant audit trail for agentic actions must capture, at minimum: the action taken and the agent component that initiated it; the data inputs and their sources at the time of decision; the model version or decision logic version applied; the timestamp and sequence in the workflow; whether human review was required and the outcome; any override and the reason recorded; and the final output sent to downstream systems or users. Every field here is required to reconstruct any autonomous decision for regulatory review.

Under DORA Article 10, in-scope firms must implement detection mechanisms for ICT-related incidents. DORA's ICT risk management framework requires firms to develop comprehensive logging procedures covering event identification, retention periods, and tamper protection. Where an agentic system operates on ICT-relevant functions, its action logs must meet the same standard. ISO 42001:2023 Clause 9.1 requires monitoring and measurement of AI system performance, which depends on access to complete decision logs over time.

Vendor Questions for Audit Trail Quality

1. Show us a complete audit log entry for a single autonomous agent action. What fields are present, and are any fields optional or truncated?
2. Is the audit log tamper-evident? What prevents post-hoc modification of log entries?
3. How is the audit log stored and for how long? Is the retention period configurable to meet your regulatory record-keeping obligations?
4. Can the audit log be queried by an external auditor or regulator without requiring vendor access to the platform?

Agentic value in GRC depends on the system's ability to act across the data sources and systems that compliance work actually requires. A system that can only access data within its own database has no meaningful autonomy in operational terms.

What Deep Integration Looks Like

Genuine workflow integration means the agentic system can query live data from identity and access management systems to validate access control evidence, pull configuration state from infrastructure management tools to assess control compliance, and read from and write to the GRC platform's own risk register and control library. It should also interact with third-party management platforms to update supplier risk scores and trigger notifications or escalations in the organisation's communication tools without requiring manual data transfer.

Integration depth is measured by the read-write capability of each connection. A read-only connection lets the agent use data but prevents it from acting on that data. For workflows that require closing a control as tested or updating a risk rating, read-write capability is required. Integration count in a vendor brochure measures connectivity; operational depth requires a separate assessment.

Vendor Questions for Workflow Integration Depth

1. For each integration listed in your platform, confirm whether the agent has read-only or read-write access. Can you provide this as a documented matrix?
2. How does the platform handle integrations that require authentication credentials? Where are those credentials stored, and how is access to them controlled?
3. What is the process for adding an integration the platform does not currently support? Does this require vendor engineering, or can it be configured by the customer?

Regulatory coverage is the breadth and accuracy of the framework content the agentic system uses to map controls, identify gaps, and assess compliance. A system with weak or outdated regulatory content will produce unreliable outputs regardless of its agentic capability.

What Adequate Regulatory Coverage Looks Like

For UK and EU regulated organisations in 2026, adequate coverage should include current versions of: DORA (in force 17 January 2025), with specific coverage of ICT risk management, incident reporting, operational resilience testing, and third-party risk requirements; ISO 27001:2022 (the current version of the international information security management standard); NIS2 (the EU Network and Information Security Directive 2, with a transposition deadline of 17 October 2024; enforcement is advancing in member states that have completed transposition); and NIST Cybersecurity Framework 2.0 (released February 2024, replacing NIST CSF 1.1). Coverage should include specific article or clause references for control-to-framework mapping, not generic category mappings.

The Accuracy Test

Ask vendors to demonstrate that their framework content is updated when regulations change, and what the update process is. Ask specifically: when DORA implementing technical standards (ITS) are finalised and published by the EBA, how quickly does the platform content reflect them? A vendor unable to answer this specifically has likely outsourced or ignored their content maintenance.

Vendor Questions for Regulatory Coverage

1. What is your framework content update process? Who is responsible for maintaining the accuracy of regulatory content, and what is the maximum lag between a regulatory update and its reflection in the platform?
2. For DORA, do you map controls to specific article numbers, or to category-level requirements? Show us a sample mapping for DORA Article 9 (ICT security policies) and Article 10 (ICT-related incident detection).
3. How does the platform handle regulatory requirements subject to ongoing technical standard development, such as DORA ITS on incident classification?

Score each dimension out of five based on your evaluation evidence, multiply by the weight, and sum for a weighted total out of 100. A minimum threshold of 60 weighted points is a reasonable baseline for regulated deployment; no dimension should score below 2.

Score each dimension 1 to 5 using evidence from vendor demonstrations, documentation, and reference calls. Multiply score by weight. Sum weighted scores for total. Require vendors to provide documentation supporting any score of 4 or 5; verbal assurances are insufficient for high-stakes dimensions.

The following vendor behaviours are disqualifying signals. If you encounter them, the platform either cannot deliver genuine agentic capability or will not satisfy regulatory accountability requirements.