Agentic AI systems, a form of artificial intelligence that can plan, invoke tools, and complete parts of a workflow with limited human supervision, are moving from experimentation into production use across enterprises, even as governance practices struggle to keep pace.
The gap between what agents can do and what enterprise controls account for is widening. Agents that once surfaced recommendations for human review now initiate workflows, modify configurations, and pass context to other agents without waiting for approval. IT teams are increasingly inheriting those capabilities as default platform features, often before governance policies have caught up.
This guide covers the specific capability shifts, framework changes, and regulatory milestones that IT and security practitioners can evaluate against their current governance posture.
Why agentic AI is evolving faster than enterprise governance can track
Agentic AI refers to AI systems that can autonomously plan, make decisions, and take actions to achieve goals with minimal human intervention. Unlike traditional AI that responds to prompts, agentic systems break down complex objectives, use external tools, and adapt their approach based on results.
Most production deployments today are built on top of a large language model (LLM) as the reasoning core, with additional layers handling tool use, memory, and orchestration.
Understanding this architecture matters for governance: the reasoning layer, the tool layer, and the orchestration layer each carry distinct risk profiles.
A less discussed but critical factor in agent governance is the quality and timeliness of the data agents rely on at runtime.
As autonomy increases, decisions are no longer reviewed step-by-step by humans, which means inaccurate or outdated data can directly translate into incorrect actions. These frameworks typically focus on permissions and auditability, but data freshness and completeness are equally important controls.
The gap between what agentic systems can do and what most enterprise governance frameworks account for is growing quickly. Enterprise AI adoption is accelerating faster than many governance programs were built to handle.
McKinsey's November 2025 survey found that 62% of respondents said their organizations were at least experimenting with AI agents, and 23% said they were already scaling an agentic AI system somewhere in the enterprise. The same survey found that most organizations are still in the piloting phase, with nearly two-thirds not yet scaling AI enterprise-wide, which helps explain why governance programs are consistently behind the capability curve rather than ahead of it. That doesn't mean agent use is universal, but it does mean oversight teams can no longer treat agentic AI as a future-only issue.
When agents can initiate procurement workflows, modify configurations, or escalate access requests without human approval, the governance question shifts. It's no longer just about who can access what data. It's about which autonomous processes can trigger which business actions, and under what conditions.
This is a meaningful departure from earlier generations of AI assistants and even from the chatbots and conversational interfaces that preceded them, which were designed to surface recommendations and wait for human direction. The current generation acts, and governance frameworks need to reflect that distinction.
The sections that follow cover specific capability changes, framework updates, and regulatory milestones that IT and security teams can evaluate against their current governance structures. The common thread across all of them: agents are no longer operating at the edges of business processes. They're embedded in the core workflows that drive procurement, supply chain management, security operations, and infrastructure management.
Model capability updates and what expanded autonomy means for IT risk
Several platform updates in the past year have expanded what agents can do without human intervention. The underlying AI models powering these agents have also grown more capable at multi-step reasoning, which means the same governance boundaries that were adequate twelve months ago may no longer contain the range of actions an agent can now take. Model releases from Anthropic, OpenAI, and other various startups have specifically highlighted extended tool-use and multi-step planning capabilities as design goals, capabilities that translate directly into expanded autonomous action in enterprise deployments.
On the platform side, major vendors are lowering the barrier to production deployment. Microsoft positions Copilot Studio as a platform for building and managing agents, including agents that can manage tasks or business processes independently and participate in multi-agent orchestration. SAP describes Joule Agents as business-process-aware agents that automate workflows at scale and coordinate complex processes across functions. The governance implication is not that every ERP workflow is now fully autonomous by default, but that more enterprise platforms are making agent-driven task execution easier to configure and operationalize.
For IT teams, agent permission scopes now need to be set at the workflow level, not just the data-access level. Most IAM configurations are scoped to data-access permissions by default, and policies governing workflow initiation or chained action execution are rarely defined explicitly, even in environments running modern identity platforms.
Another emerging requirement is end-to-end governance across the full lifecycle of an agent-driven workflow. Risk is not introduced only at the point of decision, but also in how insights are generated, how recommendations are surfaced, and how actions are executed. Systems that separate these phases across tools can make it difficult to trace how a decision led to a specific change in the environment.
The governance question for any database-adjacent AI capability is consistent: how current is the data behind an autonomous decision, what caching or replication layers sit in the path, and is that latency acceptable for the workflow being automated? Those questions apply regardless of vendor.
The pattern across platform updates is consistent. Agents are moving from advisory roles (suggesting actions for humans to approve) to real-world execution roles (taking actions within defined parameters). This shift is partly driven by advances in machine learning that have improved agents' ability to generalize from observed patterns. The same capability that makes them more useful also makes their behavior harder to predict at the edges of their training distribution.
| Capability shift | Example | Governance gap created |
|---|---|---|
| Workflow initiation | SAP procurement agents | IAM policies scoped to data access, not workflow permissions |
| Real-time data queries | Oracle AI Database | Agent decisions based on data freshness assumptions that may not hold |
| Chained actions | Multi-step remediation agents | Audit trails that capture individual actions but miss the decision chain |
Enterprise implication: Review whether your IAM policies distinguish between data-access permissions and workflow-initiation permissions. Agents operating within "approved" access scopes may still be triggering business actions outside intended governance boundaries. It's also worth confirming with your platform providers, whether ERP, cloud, or endpoint vendors, which agent capabilities have been enabled by default in recent updates, since not all capability expansions are communicated through standard change notification channels.
With model capabilities expanding, the next question becomes how agents coordinate with each other in production environments.
Agent framework and orchestration changes affecting enterprise deployments
Multi-agent systems, where specialized agents coordinate to handle different aspects of complex workflows, are moving from research environments into production IT environments. These aren't simple task chains. They involve branching decision logic, conditional escalations, and cross-system context passing that can span security operations, IT service management, and business application layers simultaneously.
Many early enterprise deployments inserted human checkpoints between detection, analysis, and action. What is changing now is not the existence of automation itself, but the ease with which platforms let teams coordinate multiple specialized agents inside one workflow. An agent that detects an issue can now hand off findings to another agent or tool for follow-on analysis or action with less custom orchestration than before.
This changes the blast radius of a misconfigured agent. Errors no longer stop at one task boundary. A detection agent that misclassifies a legitimate process can trigger a remediation agent that quarantines a production system, all before a human sees the alert.
Many major cloud platforms are already making coordinated, multi-agent workflows significantly more accessible. AWS made multi-agent collaboration generally available for Amazon Bedrock in March 2025, and followed with Amazon Bedrock AgentCore, a modular, enterprise-grade platform for deploying and managing production agents, reaching general availability in October 2025.
The pattern across providers is consistent: the infrastructure overhead for production multi-agent deployment is decreasing rapidly. As those capabilities become easier to deploy, governance tooling needs to keep up with delegation paths, supervisor logic, observability, and failure handling.
Human oversight in agentic systems is also becoming more granular. Rather than a simple approve-or-deny model at the start of a workflow, organizations are increasingly defining control points within a sequence of actions. This allows teams to review, validate, or override specific steps without blocking the entire workflow, balancing speed with accountability.
The frameworks enabling these asynchronous handoffs, including open-source orchestration tools like LangGraph and AutoGen, are also lowering the barrier to deploying multi-agent systems, which means IT teams may encounter agent deployments built outside of sanctioned enterprise platforms. Governance policies need to account for both vendor-managed and internally built agent systems.
Vertical specialization is also accelerating. For example, Visa applies AI models for fraud detection and risk scoring across payment transactions, while purpose-built healthcare platforms use AI for workflows such as controlled substance monitoring and clinical compliance, contexts where the domain-specific accuracy requirements are far stricter than a general-purpose agent could reliably meet.
For IT procurement decisions, general-purpose agents may offer flexibility, but specialized systems can deliver higher accuracy and relevance within well-defined use cases. This shift toward domain-specific optimization also affects how teams should evaluate agent effectiveness. Performance within a defined task boundary is a more meaningful benchmark than general capability scores when assessing production readiness.
The same pattern is emerging beyond software. Robotics platforms are now integrating agentic AI layers to handle dynamic task sequencing, moving beyond pre-programmed routines into adaptive decision-making. Infrastructure providers like Nvidia, whose NIM microservices and AI Enterprise platform are increasingly used to deploy and run agent workloads at scale, are making it easier to run these systems in production environments.
For governance teams, both trends mean the scope of what needs to be accounted for extends beyond cloud-hosted agents to hardware-accelerated and physically embodied deployments as well.
Enterprise implication: If you're running or evaluating multi-agent workflows, check whether your monitoring tools can observe inter-agent handoffs. Many observability platforms are designed for user-initiated request flows, not agent-to-agent signal passing.
Orchestration changes affect how agents work together. The next section covers what's changing at the endpoint and security operations layer specifically.
Endpoint-specific developments and new attack surfaces
As AI-powered automation moves deeper into endpoint management and security operations, the attack surface expands in ways that traditional security tooling wasn't designed to monitor. At the endpoint and response layer, recent developments are creating both new capabilities and new risk patterns simultaneously.
Agent drift in production environments
Agent drift occurs when cumulative micro-decisions compound into outcomes no one intended or explicitly approved, distinct from model hallucination, which refers to the model generating incorrect outputs. Drift risk is often less about incorrect model outputs and more about workflows running inside permissions that were too broad or insufficiently monitored over time. A model can behave exactly as designed while still drifting outside intended scope if the guardrails around workflow scope aren't enforced. For practitioners, that makes approval points, step-level logging, rollback plans, and periodic review of agent scopes core operational controls.
The detection gap: review whether your logging and detections distinguish between human-initiated changes, service-account activity, and agent-triggered actions. If they don't, agent activity may be harder to trace during an incident and easier to miss during routine monitoring.
Credential sprawl from persistent agent access
These systems increasingly require persistent credential access across multiple systems to perform their functions. A remediation agent that can patch endpoints, update configurations, and restart services requires credentials for each action.
Agent credentials often have broader scope than any individual human user would, and they're active continuously rather than during defined work hours. Compounding this, agents frequently authenticate across APIs connecting endpoint management, identity, and business application systems, meaning a single compromised service account can traverse multiple integration layers simultaneously. Most privileged access management (PAM) frameworks weren't designed to handle this pattern.
These risks are often amplified in fragmented environments. When agents rely on different data sources, control planes, and execution systems, it becomes difficult to maintain consistent oversight and policy enforcement. A more unified data and execution model can reduce these blind spots by ensuring that decisions, actions, and monitoring all operate from the same source of truth.
Data access across system boundaries
Agents that query data from multiple data sources to inform decisions are traversing data environments in ways that create visibility challenges. A single agent query might touch endpoint telemetry, identity systems, business intelligence platforms, and business application data.
Because agents can chain queries across these sources autonomously, the full scope of what was accessed to reach a decision may not be visible in any single system's access log. Traditional data access logging, designed for application-by-application visibility, may not capture the full picture of what an agent accessed to reach a decision.
AI tool sprawl on the endpoint
A governance gap that receives less attention than credential sprawl or data access is simpler and more immediate: most organizations don't have an accurate inventory of what AI tools are already running across their endpoint environment. Local large language models, MCP servers, AI model files, and third-party AI applications can be installed and running on endpoints before IT or security teams have any visibility into them. Without that inventory, governance policies have no surface to enforce against.
This isn't a future risk. As AI tooling proliferates at the developer and power-user layer, the AI attack surface on the endpoint is expanding independently of sanctioned deployments. Governance programs that focus exclusively on approved agent pipelines will miss the exposure introduced by unsanctioned AI running alongside them.
Enterprise implication: AI tool sprawl and credential sprawl compound each other. Audit whether your SIEM detection rules cover agent service accounts with the same rigor as user accounts, and whether your endpoint inventory now captures AI-specific artifacts alongside traditional software. If agent-initiated configuration changes aren't generating alerts, you have a detection gap.
Regulatory and standards updates shaping agentic AI deployment
Regulatory frameworks are catching up to agentic AI capabilities, and the enforcement phase is beginning.
E.U. AI Act enforcement milestones
The E.U. AI Act's risk-based regime can apply to AI systems used in covered contexts such as critical infrastructure and employment, but the timing matters. The Act entered into force in 2024, and some provisions began applying in 2025. For high-risk AI systems, the deployer and logging obligations most relevant to enterprise governance are currently scheduled to apply on August 2, 2026.
On May 7, 2026, E.U. lawmakers reached political agreement on the Digital Omnibus, which includes a 16-month postponement for new or substantially modified high-risk AI systems listed in Annex III. Formal adoption is still pending. Until the legislation is enacted, organizations should treat August 2026 as the operative planning deadline and monitor the Digital Omnibus process for updates.
For teams operating in covered high-risk use cases, the more accurate near-term takeaway is this:
- Logging and retention: Ensure the system can generate logs and that deployers can retain logs under their control for the required period
- Human oversight: Assign accountable personnel and define how oversight is exercised during use
- Use-case and risk review: Confirm whether the deployment falls into a high-risk category and whether additional obligations apply before production use
NIST AI RMF updates for autonomous systems
The National Institute of Standards and Technology (NIST) AI Risk Management Framework remains the primary voluntary framework many organizations use to guide AI governance. NIST has expanded its guidance through resources like the AI RMF Playbook and generative AI profiles, with increasing focus on how AI systems behave in production environments.
For teams deploying agentic systems, the practical shift is toward continuous oversight. Governance is not just about pre-deployment testing. It requires ongoing monitoring of how agents behave, what data they rely on, and how decisions translate into actions over time.
This includes documenting the data sources and pipelines that inform agent decisions, especially in environments where agents query live systems rather than static datasets.
For organizations using NIST as a baseline, the emphasis moves from "test before deploy" to "monitor and validate after deploy."
Enterprise implication: If your deployment automates high-impact actions, document rollback procedures, approval points, and escalation paths now. Even where regulation does not explicitly require "deployment rings," controlled rollback is a core operational safeguard and an important input to auditability.
How to evaluate whether your governance framework has kept pace
Given the developments covered above, IT and security teams can assess their current governance posture against specific criteria.
| Evaluation criterion | Diagnostic question | Risk if not addressed |
|---|---|---|
| Decision data freshness | Is the data current enough for the workflow's tolerance for delay or drift? | Agents may act on conditions that no longer exist or no longer matter |
| Governance controls | Are agent approval boundaries documented and policy-enforced? | Uncontrolled autonomous action outside intended scope |
| Progressive rollout | Does your deployment support staged rollout, rollback procedures, and defined progression criteria? | Widespread failures from untested changes at scale |
| Integration | Can agents exchange context with existing SIEM, ITSM, and endpoint tools? | Siloed automation that creates blind spots |
| Auditability and traceability | Can you reconstruct what the agent did, what tools or data it used, and which approvals or checkpoints were involved? | Compliance gaps, weak incident reconstruction, and low operator trust |
As organizations evaluate these criteria, it becomes clear that governance is no longer limited to static policy definitions. It depends on how decisions are informed at runtime, how actions are executed across systems, and how outcomes are monitored and validated.
Before working through each criterion, establish baseline metrics for your current agent deployments: how many agent service accounts are active, how many workflow-level permissions have been granted, and how many agent-initiated configuration changes occurred in the last 30 days. Without these baselines, the evaluation criteria below are difficult to act on:
- Decision data freshness: The key consideration is whether the data feeding agent decisions is sufficiently current for the action being taken. In fast-moving environments, even short delays between data collection and agent execution can produce incorrect or unnecessary remediation, particularly when no human is reviewing decisions in real time. Validating the actual latency between data freshness and agent action is a concrete step most teams haven't taken.
- Governance controls and human oversight: The practical question goes beyond whether approval boundaries exist, but whether they are enforced consistently at the point of execution. Many organizations have informal or partially implemented approval models that don't fully constrain what an agent can do once it begins a workflow.
- Integration: Integration challenges are often less about connectivity and more about consistency. When multiple systems are involved in agent workflows, differences in data models, access controls, and logging can introduce gaps in visibility and make it difficult to trace how decisions translate into actions.
- Progressive rollout and staged deployment: As agents begin executing changes directly, deployment strategy becomes part of governance. Progressive rollouts, staged execution, and documented progression criteria can act as safety controls that limit the blast radius of incorrect decisions. In this model, rollout design is not only an operational concern, but also a governance mechanism.
One distinction worth evaluating in any platform is whether rollback requires human initiation or whether the system can trigger rollback automatically when execution fails validation. Automatic rollback on failure is a qualitatively different governance control. It closes the window between a failed action and human response, which matters most in environments where agents are executing changes at speed and scale.
Some platforms are also beginning to introduce confidence signals tied to recommendations and automated actions. These signals can help teams determine when to allow fully automated execution versus when to require additional human review. While not yet standardized, confidence-based controls are emerging as a way to make agent behavior more predictable and auditable.
What IT and security teams can prioritize in the next 90 days
Given the developments covered in this guide, here are specific actions IT and security teams can take in the near term. These seven actions are tactical, but they should also inform a broader AI strategy review.
- Audit agent permission scopes against IAM policies. Check for workflow-level permissions now required by autonomous ERP and endpoint agents. Most IAM configurations are scoped to data access, not workflow initiation.
- Review SIEM detection rules for agent service accounts. If detection rules are scoped to user accounts, agent-initiated configuration changes may not be generating alerts.
- Document rollback procedures, approval points, and escalation paths for high-impact agent workflows. Controlled rollout and rollback are core operational safeguards regardless of regulatory requirement, and for deployments that fall under the E.U. AI Act's high-risk provisions, they directly support the auditability documentation those obligations require.
- Evaluate monitoring coverage for inter-agent handoffs. If you're running multi-agent workflows, check whether observability tools can trace context passing between agents.
- Confirm data freshness assumptions for agent decisions. For any agent making autonomous decisions based on endpoint or system state, verify how fresh that data actually is.
- Identify where insight-to-action workflows cross tool boundaries. If multiple systems are involved in discovery, decision, and execution, confirm how those transitions are governed, logged, and monitored.
- Inventory agent identities and service accounts. Document which agents can call which tools, under which credentials, and with what approval or RBAC boundaries. This is often the fastest way to surface overbroad access before it turns into a governance problem.
If your organization's AI governance documentation was written before agentic capabilities entered default platform tiers, it likely doesn't account for workflow-level permissions, multi-agent handoffs, or the E.U. AI Act's documentation requirements, all of which are now operational concerns, not future considerations.
Taken together, these gaps highlight a broader challenge: governance is no longer just a policy exercise. It depends on having accurate, current data, the ability to act on that data within controlled workflows, and the visibility to understand what autonomous systems are doing in real time across the environment.
How Tanium supports agentic AI governance
The Tanium Autonomous IT Platform delivers real-time endpoint intelligence and governed execution for IT and security operations, providing the data and control layer on which AI-assisted and agentic workflows depend.
That foundation combines real-time endpoint intelligence with Tanium's patented Linear Chain Architecture, along with confidence and risk scoring, progressive ring-based deployment, and deep integrations. On top of that foundation, Tanium adds natural-language querying, agentic workflows with human-in-the-loop controls, and centralized oversight of autonomous activity.
Tanium Atlas, the autonomous operating system, brings that foundation together into a single governed experience where intelligence, guidance, and action operate within the same workflow. As the agentic AI ecosystem continues to evolve, Tanium is expanding this foundation so organizations can maintain visibility, enforce governance, and act with confidence as systems become more autonomous.
The effectiveness of agentic AI in IT and security operations depends less on the sophistication of the model and more on how confidently it can move from insight to action within defined constraints. Systems that connect real-time data, governed execution, and observable outcomes in a single workflow are better positioned to support safe and scalable automation.
- Real-time endpoint intelligence helps teams ground AI-assisted decisions in current device state. Tanium Ask uses natural-language interactions to surface insights and support investigation. Additionally, the Tanium Ask Agent extends that into action by supporting automated, human-in-the-loop workflows for data discovery, software management, and more.
- Human-in-the-loop controls are built into sensitive actions. Tanium Ask Agent maintains human oversight throughout agentic workflows, requiring confirmation before actions that directly affect endpoints or Tanium configurations. Administrators can oversee, approve, and revert changes, keeping teams in control of what the platform does on their behalf.
- Confidence scoring supports controlled rollout of automated changes. Progressive deployment with confidence scores gives organizations a mechanism to stage changes and assess potential impact before broad execution.
- Centralized oversight of autonomous activity provides a unified governance layer. Tanium Action Oversight provides continuous system reporting and visibility into the current state of autonomous activity across the platform. Rather than relying on individual module logs or ad hoc monitoring, every system is tied into this centralized governance component, giving IT and security teams a unified view of what the platform is doing autonomously, at what scope, and under what conditions.
- Playbook-based orchestration supports governed, repeatable workflows. Tanium Automate allows teams to create playbooks that chain together actions into logical, repeatable sequences with little to no code to automate common tasks like patching, vulnerability remediation, and application deployment. With staged rollouts and user oversight built into the workflow, teams can limit the impact of errors and control how automation scales across production environments.
- Proactive vulnerability management helps reduce downstream risk in automated environments. Tanium Guardian automatically notifies teams of emerging issues, including zero-day vulnerabilities, and provides recommended operator actions. Guardian content and insights are prepared by Tanium's Vulnerability Emergency Response Team (VERT), a Tanium-led collaboration between Tanium security experts, industry partners, and customers.
- Unified IT and security operations can reduce monitoring blind spots. Tanium Threat Response monitors endpoint activity in real time, supports threat hunting, and uses Tanium Signals for continuous detection and alerting. Because investigation and response run on the same platform, teams can build and tune detections without switching contexts or tools.
This approach reflects the operational advantage of addressing governance at the data and execution layer together. An end-to-end approach spanning real-time data collection, governed workflow execution, audit trail generation, and unified detection is what separates governance frameworks that can scale with agentic AI from those that create new blind spots as agent capabilities expand.
When agents work from current endpoint state, operate within auditable workflows, and trigger alerts through unified detection coverage, the oversight gap that makes agentic AI risky becomes addressable rather than assumed.
As agentic AI expands into endpoint environments, visibility challenges shift from application lists to runtime behavior, local models, and embedded agent frameworks. This video provides a practical look at how those risks surface and how teams can begin to categorize and monitor them using Tanium.
Frequently asked questions about agentic AI progress
Still sorting through what agentic AI means and how it impacts your environment? These quick answers break down the most common questions teams are asking right now as capabilities evolve and governance expectations catch up. Use them to clarify what's changing, what it means for your risk posture, and where to focus next.
What's the latest in agentic AI?
Agentic AI is moving beyond chat-style assistance toward systems that can plan, use tools, and complete multi-step workflows. Recent vendor launches from companies such as SAP, Microsoft, AWS, and Oracle show that enterprises now have more off-the-shelf options for building and coordinating agents, but most organizations are still early in scaling them.
Why are so many agentic AI initiatives failing in production?
Many failures stem from a governance gap: teams haven't yet aligned data freshness expectations, approval boundaries, identity controls, and observability with what autonomous workflows can actually do in production. McKinsey's November 2025 survey found that nearly two-thirds of respondents said their organizations had not yet begun scaling AI across the enterprise, which helps explain why excitement around agents still outpaces operational maturity.
How do agentic AI updates impact decision-making processes?
Recent platform updates have shifted agents from advisory roles to execution roles, meaning they now initiate workflows and modify configurations without waiting for human approval. This changes decision-making from "agent suggests, human approves" to "agent acts within defined parameters." For IT teams, this means governance must now account for workflow-level permissions, not just data-access controls, and monitoring tools need to capture agent-initiated actions as rigorously as user-initiated ones.
Does agentic AI have a future?
Agentic AI adoption is accelerating, with nearly 70% of enterprises in Team8's 2025 CISO Village Survey, a community of security leaders at large enterprises, reporting AI agents already in production. That figure likely reflects leading-edge adopters rather than the median organization; McKinsey's November 2025 survey found most companies still in early stages. Taken together, the data suggests adoption is uneven but moving fast at the top of the market.
The technology's future depends on whether governance frameworks can catch up to agent capabilities. The organizations that get there will be the ones that treat governance not as a policy exercise but as an operational discipline, embedded in how agents are deployed, monitored, and constrained from the start.
While tools like ChatGPT introduced many organizations to AI-generated outputs, agentic systems represent a fundamentally different category: they don't just respond to queries, they initiate actions, and that distinction is what makes governance so critical.
Teams that address visibility gaps early will be positioned to scale agentic AI deployments with confidence. Organizations that don't will find themselves managing incidents that could have been prevented with clearer boundaries and better endpoint intelligence.
Governance is no longer just a policy exercise—it depends on having accurate, current data, the ability to act within controlled workflows, and the visibility to understand what autonomous systems are doing in real time.
Request a free, customized demo today to see how Tanium supports agentic AI governance. Listen to our podcast, and follow Tanium on LinkedIn and across social media for ongoing updates on agentic AI governance, endpoint security, and enterprise IT developments.