Table of Contents
Executive Summary
Our Agentic AI Control Design Package gives you the safeguards you will need for agents that can create outcomes in your name: an agent tiering framework with practical thresholds, a Statement of Applicability that identifies which agentic risks apply and why, and a Control Log specifying each control, the evidence it produces, and its owner.
If the technology for building agents already sits inside your own architecture, you will need these governance tools to ensure your agent estate does not grow faster than your capacity to control it.
We will draft all three, or refine the versions you already have, before workshops that will show your colleagues and you how to apply them to your own use cases and agree next steps.
If you want to adopt different types of autonomous agents safely and with confidence, contact us to customise your Agentic AI Control Design Package.
Agentic AI Introduces a New Category of Risk
Whereas generative AI produces outputs for our review, agentic AI’s ability to plan, invoke tools, and take actions can create outcomes in our name.
Because of this, your risk exposure relates to the behaviours you authorise (or fail to prohibit), e.g. data reach, action space, reversibility, autonomy.
Platform providers are making the tools to build agents easily available, meaning this leap in risk exposure can feel like just another technology upgrade, raising the prospect that your agent estate expands faster than your capacity to control it.
Your Capacity to Build Can Outrun Your Capacity to Control
If this happens to you, without, for example, a complete view of your live and retired agents, you may not be able to reconstruct decisions your agent took or even test your ability to stop them, risking post-deployment remediation costs and longer-than-necessary build cycles.
Meanwhile, other firms will get ahead of these problems: they will tier their agents, identify the risks that apply, define the controls they need, prove they work, and name their owners. Achieving this from the outset, they will also side-step the issue of shadow agents and the questions it raises moving, instead, to more advanced types of agentic workflows.
An Agentic AI Control Design Package Gives You the Detail You Will Need
Our Agentic AI Control Design Package gives you precisely those benefits: a customised agentic AI control environment your engineers can configure, your procurement colleagues can licence, your owners can operate, your auditors can test, and for which your HR team can train future talent.
Its key components include:
- An agent tiering framework where an agent can be assigned to a tier based on its authorised behaviours, each tier carrying a written threshold.
- A unique ‘Statement of Applicability’ for agentic risks that identifies which of our 32 agentic risk flags apply to your stage of agentic development. Exclusions are justified, each with a review trigger in case risk exposure presents itself as your agentic capabilities become more advanced.
- A bespoke Control Log of specific agentic AI risk controls, the technical or procedural mechanisms that deliver them, the evidence they produce, what makes that evidence satisfactory, and named owners – with no unagreed blanks.
A control without a threshold is toothless, evidence without a standard is untested, and an exclusion without a review trigger becomes a blind spot. So, these details ARE the point.
We design the controls for your control owners’ approval and advise on their implementation. Your engineers or delivery partner will then configure and implement them, or ask us for an introduction to our delivery partner.
We Will Help You Overcome the Obstacles
Obstacle 1: “I don’t know where to start!” Your Agentic Risks consultants will draft (or refine) your tiering framework, Statement of Applicability, and Control Log so they are tailor-made for your organisation, before your workshops. You and your colleagues can then focus on practising the controls via different use cases and developing any necessary implementation plans. We will agree the number and length of workshops with you at scoping.
Obstacle 2: “Generic controls feel too abstract to act on.” We will decompose your own use cases, working them through as practical examples in the workshops – mainstream ones as well as edge cases that test the boundaries and show the importance of the controls.
Obstacle 3: “Help! We’re deploying before we’re fully ready.” While you develop your agentic capabilities, controls can have interim substitutes. Still, we will make sure any interim control carries a review trigger and the condition under which you should retire it and switch to a permanent solution.
Obstacle 4: “Platforms are spraying us with agents, and we’re worried about drowning in them!” We will help you build your capacity phase by phase, each phase ending in a go/no-go decision, demonstrating progress to your business partners while maintaining control of the situation and avoiding the need to commit beyond the phase in front of you.
Obstacle 5: “We’re afraid of getting locked-in.” Agentic Risks and our agentic AI governance and control frameworks are platform-independent, so your agentic controls environment will outlive a platform migration, needing only an impact assessment.
Obstacle 6: “We’ve already invested in governance and don’t want to start again.” That does not make non-agentic governance sufficient for AI agents, but we understand the sentiment, so we start from your existing policies, tiering framework and controls, identifying which controls to reuse, which to strengthen, and where agentic-specific controls are needed.
Agentic Risks Can Make This Story Come True For You
With Agentic Risks’ senior consultants and engineers, you will be in safe hands.
- We specialise in agentic risk assessment and control selection, drawing on a library of more than 250 agentic AI controls; naming artefacts that prove controls work and knowing where to locate each one.
- Our Risk-Based Agentic AI Adoption Strategy gives us deep knowledge of agent tiering; our independence means we will always propose a solution that best fits your needs; and our seniority allows us to facilitate decisions that are both executive in tone and technical in substance.
So, if you want to keep pace with agentic AI or would like some help to move faster, contact us to discuss how our Agentic AI Control Design Package will help you adopt autonomous agents safely and with confidence.
Frequently Asked Questions
Agentic AI control design turns the behaviours an agent is authorised to perform into explicit thresholds, controls, evidence requirements, and ownership. In the approach described here, that means an agent tiering framework, a Statement of Applicability for agentic risks, and a Control Log.
Generative AI produces outputs for review, whereas agentic AI can plan, invoke tools, and take actions that create outcomes in your name. That makes risk exposure depend on authorised behaviours such as data reach, action space, reversibility, and autonomy, so the controls need to address those behaviours.
The design described here has three core components: an agent tiering framework with written thresholds; a Statement of Applicability identifying which agentic risks apply and why; and a Control Log recording the mechanisms, evidence, evidence standards, and named owners for each control.
A Statement of Applicability identifies which of the 32 agentic risk flags apply at your current stage of agentic development. It also justifies exclusions and records a review trigger for each, in case your risk exposure changes.
For each control, define the technical or procedural mechanism, the evidence it produces, and what makes that evidence satisfactory, then assign a named owner. A control without a threshold is toothless, and evidence without a standard is untested.
Use interim controls only with a review trigger and explicit conditions for when they should be retired and replaced by a permanent solution. Our Agentic AI Control Design Package will help you develop capability in a managed way, phase by phase, with each phase ending in a go/no-go decision.
