Blog

Agentic vs. Agentish AI: What it means for third-party risk management

Company size and maturity plays a pivotal role in how you deploy AI for vendor oversight.

Chris Paterson
Director, TPM Strategy

Risk teams and the current AI landscape
From automation to autonomy
Why Agentish AI fits today’s risk environment
Emerging intersection of AI and third-party risk
Lessons from early adopters
Building safe and scalable AI adoption

Agentic vs. Agentish AI — What risk teams need to know

AgenticAI is evolving rapidly, but the question for risk professionals isn’t just what’s possible, it’s what’s practical and safe today.

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The conversation around artificial intelligence in risk management has changed dramatically over the past year. Every software vendor seems to have “agentic AI” on their marketing banners, and every risk team is wondering how much of that buzz translates into something practical.

Agentic and Agentish AI both represent the next evolution in automation and analytics, but they are not the same. Understanding that difference is critical for anyone responsible for assessing or mitigating third-party and technology risk. Adopting AI is no longer a question of if, but of how intelligently and safely it is deployed.

Risk teams and the current AI landscape

Risk programs are grappling with a surge in AI use. Most organizations still focus on external AI risk, such as third-party vendors embedding machine learning into their products or large models entering the supply chain. That focus makes sense. In 2024, AI-related incidents grew more than twenty-six times over the previous three years. With that kind of escalation, many teams have concentrated their attention on protecting against AI risk rather than exploring its potential.

However, a quiet shift is underway. Risk professionals are now starting to explore how AI can strengthen the very programs designed to control it. A growing number of teams are investing in AI to augment staff capacity, improve visibility, and compress timelines for assessments, monitoring, and reporting. This is where the distinction between Agentic and Agentish AI becomes central.

From automation to autonomy

Before defining those terms, it helps to look at where enterprise AI sits on the maturity curve.

At the most basic level lies process automation. These systems operate entirely on predefined rules: “If X happens, trigger Y.” Robotic process automation has existed for decades, and while recent models may use large language models (LLMs) to enhance decision logic, they still rely on hard-coded steps.

Next comes generative AI. These models can create new text or visuals by drawing on a defined corpus of training data. In a risk management setting, this could mean scanning a vendor’s SOC 2 report or penetration test and auto-completing portions of a questionnaire. The content feels dynamic, but the model still depends on the information it is fed.

Agentish AI sits a level above. Think of it as an AI agent that can act with partial autonomy. It follows clear parameters and can initiate or complete certain tasks on its own, often blending automation and generative capabilities. For example, a supplier-onboarding agent might review documents, calculate inherent risk, and send reassessment reminders without manual prompting. It reacts to conditions and prompts but does not operate beyond its defined boundaries.

True Agentic AI is different. It has full agency. Rather than waiting for an instruction or event, it continuously monitors multiple data streams, evaluates complex conditions, and executes actions without human oversight. A fully agentic system might not only identify a supply-chain disruption but also reassign inventory and update delivery schedules automatically. That level of independence is powerful — but also risky.

Why Agentish AI fits today's risk environment

In practice, most organizations are adopting Agentish AI, and that’s a good thing. Fully autonomous AI might sound attractive, but for regulated industries and risk-averse teams, it introduces significant exposure. Machines work at machine scale, which means that a single incorrect decision can multiply thousands of times before a human notices.

Agentish AI provides a more balanced approach. It is autonomous within its guardrails, reactive rather than proactive, and transparent enough for human oversight. That combination allows risk leaders to define the parameters, control the data inputs, and maintain accountability for every decision the system makes.

It also fits neatly into existing governance frameworks. Risk programs already manage technology and automation risk; AI is an extension of those disciplines. The same controls, such as access management, audit logging, and data lineage tracking, can apply here.

What changes is the scale and speed at which those processes run.

The emerging intersection of AI and third-party risk

Vendor onboarding is one of the most immediate and impactful applications. Onboarding a new supplier often requires a chain of assessments, evidence collection, and contract validation. An AI agent can integrate across procurement tools, security scoring systems, and public trust profiles to assemble that information automatically. The result is faster onboarding and a more consistent, inherent risk calculation.

Breach monitoring is also a growing area for AI abilities. Agentish systems can scan hundreds of data feeds, correlate threat intelligence, and alert analysts when a third party experiences an incident. By understanding which vendors hold sensitive data or connect to critical systems, the agent can prioritize alerts and reduce noise.

Control assurance is also evolving. Instead of manually reviewing every document submitted as evidence, an AI agent can read, classify, and match artifacts to control requirements. It can even calculate a confidence score and highlight gaps for human review. This approach not only shortens validation cycles but also creates a defensible record of how the evidence was analyzed.

Lessons from early adopters

Despite the excitement, AI initiatives continue to fail at a high rate. A 2024 MIT study found that 95% percent of enterprise generative AI projects had not met their expected return on investment. In many cases, the issue is not technical. It is a lack of clarity. Fewer than one in five organizations surveyed by McKinsey had defined success metrics for their AI deployments.

For risk teams, that lesson is especially relevant. Success should be measured not only in efficiency gains but also in improved control accuracy, better decision support, and stronger collaboration across stakeholders. AI must serve a purpose that is clearly documented and defensible.

Regulatory complexity is another factor. The EU AI Act and a growing patchwork of state-level laws are shaping how AI can be used, evaluated, and audited. Risk leaders must track these developments and build compliance reviews into their AI lifecycle management.

Building safe and scalable AI adoption

The most successful programs approach AI as an enabler, not a replacement. They start by defining their existing workflows, then introduce AI to enhance what already works. Poor processes only produce poor results, faster. Good ones become more resilient and adaptive.

It’s also essential to communicate with employees about why these tools exist. When people understand that AI agents are designed to reduce manual workload and enhance decision quality, they are far more likely to embrace them. Transparency builds trust, both within the organization and across the vendor ecosystem.

Agentic AI may be the headline, but Agentish AI is the present reality. For third-party risk teams, it offers a practical, controlled path toward automation that delivers measurable impact without surrendering oversight. As adoption continues, the most mature programs will not be those that deploy the most advanced technology, but those that deploy it most responsibly.

Learn more about the differences and uses of Agentic and Agentish AI in this webinar.

Blog

Agentic vs. Agentish AI: What it means for third-party risk management

Table of contents

Agentic vs. Agentish AI — What risk teams need to know

Risk teams and the current AI landscape

From automation to autonomy

Why Agentish AI fits today's risk environment

The emerging intersection of AI and third-party risk

Lessons from early adopters

Building safe and scalable AI adoption

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