Stay updated

Unstructured agent-building is a costly and risky choice, increasing the chance of overlooked risks, security incidents, and scrambled remediation when external stakeholders ask questions.This is because agentic workflows create new risks that you will need to control and monitor in novel ways.In this webinar, we demonstrate the specific ways the risk assessment process needs to evolve for the agentic era, and how to design and operationalise effective agentic KRIs.These are important topics because an agent is only low risk when both its task risk is low and its risk controls are strong.Adopt these techniques to give structure to your agentic transformation, prevent risk, and ensure trustworthy monitoring.

Where generative AI produces outputs for our review, agentic AI creates outcomes in our name, concentrating risk management into the design and oversight phases of a process. Autonomy itself is a “freedom coin” and, depending on how you manage both sides, a low-risk task does not necessarily ensure a low-risk agent. However, centuries of delegating autonomy to non-humans show the importance of proven controls, training, and accountability, and point to four sources of agentic risk. Our five-step process for agentic risk appetite and adoption strategy begins with use cases, efficient scaling, and adoption posture, then identifies prerequisites, assesses readiness, and constructs an achievable roadmap. To finish, our Agentic AI Readiness Assessment determines the nature and extent of any gaps an organisation faces that its implementation plan should address.

AI agents can plan, act and adapt across end-to-end workflows, creating opportunities for productivity, consistency and scale. Because they can create outcomes in an organisation’s name, they also introduce a new class of risk that increases the importance of design-phase controls and machine-speed oversight. Existing regulations, standards and model risk frameworks provide useful foundations but leave operational gaps around runtime control, evolving risk and accountability. Effective agentic AI governance combines established disciplines with enforceable controls, continuous lifecycle oversight and governance for combined human-agent operations. Our Agentic AI Control Design Package turns the Framework into implementable controls, while the Agentic AI Readiness Assessment identifies strategic, technical and operational, and organisational gaps.

One year ago, ‘agentic risk management’ wasn’t a thing, so when a few of us concluded this would change, we were able to get our hands on a stack of related web addresses because no one else seemed to want them.Roll forward 12 months, and we now run a popular webinar series for the Institute of Risk Management; we are The Investment Association’s approved training provider on agentic AI; we blog regularly about the ‘human-agent organisation’; we have been asked to speak at industry events; and we have completed some major client engagements on agentic risks and controls.It goes without saying, therefore, that these experiences have taught me a huge amount. So, on our first birthday, here are the top 10 agentic AI lessons I have learned over this period.If you are delegating autonomy to agents at your firm (or soon will be), then this is for you.For centuries, the organisation has been a human-only structure. Agents bring the first non-human delegated workforce, introducing a new paradigm: the human-agent organisation. Enter it with confidence.

Our Agentic AI Control Design Package turns your governance design into controls you can implement, own and evidence: 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 requires, and its owner. We build on your existing policies and controls to support repeatable, controlled adoption at pace, staff confidence and stakeholder trust. We draft or refine the control design before workshops where your colleagues agree owners, evidence requirements and implementation actions. You receive an agreed design and implementation plan, with guidance and handover to your implementation team. 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 assurance needs to keep pace with the deployment of autonomous AI agents. While AI products can be certified and management systems audited, a specific AI agent deployment – a particular agent, with its permissions, data, and human oversight – receives guidance and self-assessment but little independent, evidence-based assurance. This gap creates three problems: institutional over-trust, an inability to demonstrate control adequacy when an agent causes harm, and unpriceable risk that slows adoption. Drawing on regulation, established assurance practice and the agentic risk surface (broadly defined), this paper proposes twelve criteria any solution should meet. The next step is to compare existing standards, frameworks and assurance schemes against these criteria and engage standards bodies to close the gap.

Agentic AI governance is the discipline of controlling agentic AI systems whose risks emerge through actions and decisions over time, not just individual outputs. In April 2026, Szpruch, et al (University of Edinburgh) published a paper that demonstrates how traditional model risk management (MRM) and AI governance designed for static models have a structural agentic AI governance gap. The paper argues that effective governance should shift to runtime governance – continuous monitoring, enforcement, and intervention while the agent is operating – and that relying on traditional MRM for agentic governance will become hard to defend as supervisory expectations develop. For use cases that demand precision (like many in financial services), the question is no longer whether agentic AI requires a different governance model, but how quickly firms can build one that scales safely.

With a focus on which use agentic AI case, which vendor, and which oversight committee, it is easy to overlook a key question – whether the data their agents will rely on is fit for autonomous use. Agents do not read data; they act on it, and small defects compound at every step. Over 24 months, we believe two cohorts of firms will emerge: those whose data is Structured, Current, Authoritative, Rich, Verifiable, and Symmetrical – the six SCARVeS© characteristics of good agentic AI training data – and that will absorb the consequences of deploying agents on unchanged data estates. The obstacles between are not technical but governance ones and, as yet, not every firm has the capability to overcome them.

Once you deploy autonomous agents that can fail in ways that non-agentic alerting was not designed to detect, only incident management for agentic AI will protect you if something goes wrong. Of the 18 steps needed to manage an agentic incident, only 2 survive intact – 3 have no non-agentic equivalent at all, and 13 require material changes – creating operational and regulatory exposure for firms that have already deployed agents. The most effective response plan is to diagnose your gaps, define bespoke upgrades that match your agentic use cases, and sequence implementation to ensure they support other in-flight initiatives. Our three-stage Agentic Incident Management Upgrade service is designed to achieve exactly this for you.

A risk-based agentic AI adoption strategy classifies AI agents into risk tiers and applies stronger controls where the risk is highest. In comparison, more blunt approaches have inherent flaws: permissive access with post-event monitoring sacrifices control, and full pre-approval constrains scalability. Instead, the risk-based model assigns low-risk agents a register-and-attest process, medium-risk agents a proportionate review, and high-risk agents full governance, making it productive, enforceable, and defensible at scale. To implement this, add four deliverables to your agentic roadmap: tier criteria, technical enforcement, shadow agent detection, and risk manager dashboards. If you are at the start of an agentic transformation, the Agentic AI Readiness Assessment evaluates your firm’s readiness across all the prerequisites for agentic AI – including those needed to implement this model – in 90 minutes.

A risk-based agentic AI adoption strategy classifies AI agents into risk tiers and applies stronger controls where the risk is highest. In comparison, more blunt approaches have inherent flaws: permissive access with post-event monitoring sacrifices control, and full pre-approval constrains scalability. Instead, the risk-based model assigns low-risk agents a register-and-attest process, medium-risk agents a proportionate review, and high-risk agents full governance, making it productive, enforceable, and defensible at scale. To implement this, add four deliverables to your agentic roadmap: tier criteria, technical enforcement, shadow agent detection, and risk manager dashboards. If you are at the start of an agentic transformation, the Agentic AI Readiness Assessment evaluates your firm’s readiness across all the prerequisites for agentic AI – including those needed to implement this model – in 90 minutes.

An April 2026 academic paper has confirmed what we advised IRM delegates in January: a low-risk use case does not mean a low-risk agent. The paper establishes that agentic AI compliance under EU law is not limited to the AI Act – it spans GDPR, DORA, NIS2, and sector-specific regulation simultaneously. Critically, behavioural drift is now a live legal obligation, not just a governance preference: firms must trace it, record it, and treat threshold changes as regulatory events. Three standards gaps remain unresolved. Our frameworks already address all three.

A frontier lab has just told the US government what Agentic Risks has been saying since July 2025: agentic AI risk is a distinct category of harm that existing frameworks do not describe. For risk managers, compliance officers and CROs at regulated firms, Anthropic’s 9 March 2026 submission to NIST’s CAISI validates a discipline that standards bodies have yet to catch up with. This post walks through six ways the submission strengthens the approach Agentic Risks has taken, where we believe more is needed, and why we publish our IP and methodologies freely.

The Agentic AI Governance Framework is a structured guide to governing autonomous AI systems – what to keep from traditional AI governance, and what new controls you need. It’s essential reading for risk, compliance, and technology leaders whose organisations are deploying, or planning to deploy, agentic AI. It tells you exactly which foundations still hold, which new components you need to add, and how to navigate the areas where the debates remain inconclusive. With it, you can build a governance model that is defensible to regulators, auditors, and boards.

Firms whose adoption strategies succeed are those whose roadmaps are achievable from their current state of readiness. The Agentic AI Readiness Assessment ensures your transformation is evidence-based, achievable, and customised to your situation. It does this by establishing whether each prerequisite is in place (strategic, technical and operational, and organisational), its maturity level, and the extent of work needed to support your target risk tier. Triage-style 90-minute session. Output is a complete and systematic view – strengths, weaknesses, and prioritised next steps – ready within 48 hours for you to discuss with your colleagues.

Across most enterprise platforms, an agentic workflow comprises three layers – model, orchestration, and application – and each layer can see risk signals that the others cannot. For your agentic workflow monitoring to be viable, a KRI should reside where its risk signal lives; those that do not risk noise, blind spots, or a false sense of security. We mapped a sample of KRIs across the three layers and found that half reside primarily at the application layer, while the model layer was the primary source for only one. Monitoring built on functional layers is therefore less likely to have coverage gaps than a framework tied to a single platform. We conclude by advising firms to understand their risk requirements before selecting a platform – and embed their risk controls into their workflow designs.

Agentic key risk indicators are the metrics organisations use to monitor the behaviour, risk profile, and operational performance of autonomous AI agents. Agentic systems do not operate in a steady state. As they learn and adapt, small behavioural changes can accumulate and alter their risk profile. Effective governance therefore requires organisations to monitor the direction and speed of this AI behavioural drift before it moves beyond risk appetite. Agentic key risk indicators, sometimes described more generally as AI key risk indicators, provide the operational evidence needed to monitor AI agent risk and ensure regulatory compliance. This article sets out 12 practical principles for designing effective agentic KRIs and illustrates them through 20 worked examples across 5 common agentic risks.

Agentic AI marks a shift from tools that produce outputs to systems that achieve outcomes. As a result, agentic workflows differ from traditional ones. In brief, they can plan, decide, and execute multi-step actions autonomously to achieve your goal, remembering and learning from their experiences. Because of this, their behaviour can evolve, creating both opportunities and risks. Therefore, understanding how agentic workflows differ becomes vital to effective risk identification. So, this article examines 10 dimensions that vary between agentic and traditional workflows – from decision authority and system access to failure modes and accountability – and how risk managers should respond.

Autonomous AI introduces a new class of risk – agentic risk – because it delegates execution to non-humans that interpret objectives, constraints, and context differently from people. In agentic workflows, ambiguity itself becomes a risk: unless risk appetite is expressed in behavioural and quantitative terms, agents cannot reliably understand or enforce organisational expectations. Risk managers (broadly defined) must therefore integrate agentic risk into existing risk appetite processes by defining an agentic AI risk appetite statement, applying it consistently at the workflow level, and instituting key risk indicators for agentic risks. Organisations that do this will enable safe, scalable agentic adoption; those that do not will face fragmented, compounding, and unmanaged autonomous behaviour risks. This article will give you the guidance you need to structure your agentic risk appetite statement, and a link to book a free consultation with us if you would like some specific help.

Agentic workflows are not “just another AI model” – they are operational systems that can act, spend, and escalate at speed. That means risk is shaped as much by design choices (ownership, boundaries, monitoring, and stop authority) as by the task the agent performs. If you, as a risk manager, are asked to perform an agentic AI risk assessment, you will need a fast, defensible way to determine whether the project is sufficiently controllable to go live. This article introduces you to 32 systematic, verifiable agentic AI risk flags you can test with evidence, so you can quickly translate findings into clear, proportionate risk treatment plans.

An agentic workflow is a system where AI agents autonomously plan, decide, and act across interconnected tasks, with explicit controls and human oversight embedded at each stage. The success of these initiatives rests not just on technology, but on organisations building the capability to govern autonomy before they introduce it. This article sets out a practical, audit-defensible agentic workflow design process that helps firms decide whether an AI agent should exist, what authority it may safely hold, and how that authority is constrained, tested, and monitored over time. The process is structured into three phases – foundation, build, and deployment – to move firms beyond demos to a repeatable, governable, and scalable agentic workflow. The outcome is a step-by-step guide on how to design an agentic workflow using a risk-led, auditable agentic workflow design process.

This article is for experienced risk professionals and explains why agentic risk management is now essential for retaining control of agentic AI workflows. Traditional risk management assumes humans design, execute, and oversee systems end-to-end, but agentic AI breaks that assumption by delegating execution to autonomous systems. This development fundamentally changes how risk emerges, how controls fail, and where accountability must sit. To mitigate risk in an agentic workflow, therefore, risk management must extend its role beyond post-hoc monitoring into the design phase, monitor continuously rather than periodically, and be ready to address new risks arising from emergent behaviours.

To leverage the benefits of agentic AI you must manage the broad new class of risks that it introduces. If left unmanaged, these risks can materialise across individual and multi-agent behaviours, system security, governance, policy integration, as well as organisational and human factors. To enable the new discipline of agentic AI risk management, the Enterprise-Wide Agentic AI Risk Control Framework helps you identify, assess, and control these new risks, integrate them into your existing framework, and keep pace as agentic AI evolves. To get started, set your policy on AI autonomy, map the risks and controls for a pilot agentic workflow, and prove control effectiveness through testing.

AI agents can act autonomously to perform multi-step tasks, interact across systems, and learn from experience, making them more powerful than basic prompt-driven AI tools. The key features of AI agents and their benefits for organisations include higher productivity, scalability, and better use of human judgement. This makes them especially suitable for workflow automation. Organisations should de-risk their adoption of agentic AI by defining their policy for delegating autonomy, implementing effective risk controls, and piloting an agentic workflow before scaling.