Agentic AI control planes: Turning cloud sprawl into a governed, AI-driven fabric for cost, risk and value.

From dashboards to decisions: TBM meets autonomous multicloud

In most enterprises, Technology Business Management still runs on an “analyze, advise, escalate” cycle. TBM teams pull cost and usage data from multiple clouds, normalize it, and present dashboards that help executives decide what to do next. Meanwhile, multicloud networking teams wrestle with a different reality: constantly shifting traffic patterns, new AI workloads and security threats that evolve in minutes, not months.

By 2026, that gap will no longer be tenable. IDC expects demand for multicloud networking solutions to ramp quickly as enterprises race to connect and secure AI models and inferencing applications across multiple providers. F5, Inc.+1 Kentik describes multicloud networking as an IT strategy where organizations deliberately use two or more cloud platforms and rely on a connectivity layer to abstract and unify them. Kentik+1 As that layer becomes the backbone of AI-heavy digital businesses, the role of TBM is shifting from post-facto cost reporting to real-time policy and control.

The catalyst for this shift is agentic AI, amplified by AI-native development platforms. Agentic AI is more than a smarter chatbot. Salesforce describes it as the technology that powers AI agents so they can operate autonomously without constant human oversight, coordinating with humans and other agents to pursue complex goals. Salesforce Info-Tech Research Group has already published playbooks for building agentic AI prototypes that deliver business value quickly instead of lingering as science experiments. Infotech

For multicloud networking and TBM leaders, this is the beginning of an “autonomous operating system” for cloud finances and connectivity. The vision is simple but radical: TBM stops being a static report and becomes a living set of policies that AI agents continuously enforce across networks, cloud services and edge locations.

Agentic AI in the network: From recommendations to autonomous actions

Until now, most AI in cloud governance has been analytical. It spots anomalies in egress charges, forecasts capacity needs or flags misconfigured resources. Human teams still do the heavy lifting of deciding what to change, when and how. Agentic AI introduces a new pattern: analysis, planning and execution all handled by AI agents that can take multi-step actions using APIs, scripts and infrastructure-as-code, within guardrails.

Analysts and vendors are clear that this is not theoretical. IBM characterizes AI agents as systems that autonomously design workflows with available tools, making decisions and taking actions on behalf of users. IBM Recent coverage by Associated Press notes that agentic AI is the tech industry’s newest buzzword because it promises AI that does not just generate answers but autonomously carries out multi-step tasks, from managing emails to optimizing finances. AP News

In a multicloud networking context, this might look like a fleet of specialized agents, each with a TBM-aligned mission. One agent focuses on cross-cloud data transfer, constantly evaluating traffic flows and adjusting routes to keep latency within service-level objectives while staying inside a target cost-per-gigabyte band. Another agent monitors new edge sites coming online and ensures they attach to the right virtual networks, security policies and chargeback codes without human intervention. A third agent acts as a “risk sentry,” watching for network patterns that could indicate shadow AI usage or unsanctioned data flows between regions, then automatically applying pre-approved controls.

Gartner’s early read on agentic AI is both bullish and sobering. A June 2025 report, summarized by Reuters, predicts that more than 40 percent of agentic AI projects will be scrapped by 2027 because of high costs and unclear outcomes, but it also forecasts that such agents will autonomously handle 15 percent of daily business decisions and appear in a third of enterprise software by 2028. Reuters For TBM and multicloud leaders, the message is clear: agentic AI will not be optional, but it will punish organizations that launch projects without crisp economic and governance objectives.

AI-native development platforms: Encoding TBM and networking in the software lifecycle

Agentic AI in operations is only half the story. The other half is AI-native development platforms that encode TBM and networking constraints directly into the way software is built. These platforms augment developers with AI copilots that suggest code, generate infrastructure manifests and infer best-practice architectures from high-level intent.

IDC’s cloud market analysis shows that buyers increasingly expect cloud providers and tool vendors to reduce the complexity of multicloud by offering higher-level abstractions and guidance. IDC Blog Kentik, for example, emphasizes that multi-cloud visibility is a major pain point and has built tooling that unifies telemetry across AWS, Azure, Google Cloud and Oracle into a single view, giving IT teams the ability to monitor application performance and traffic across both owned and unowned networks. Kentik

In 2026, TBM- and network-aware AI-native platforms will take this further. A product team building a new AI-driven customer service backend might describe its priorities in plain language: low latency to users in North America and Europe, data residency limited to specific jurisdictions, an egress budget that cannot exceed five percent of monthly recurring revenue and a requirement to use at least two cloud providers for resiliency. The platform’s AI will generate:

Network topologies that route traffic through the most cost-effective interconnects and regions while meeting latency and residency constraints.

Security policies that apply preemptive controls to critical data paths, aligned with corporate risk appetite.

Telemetry and tagging standards that ensure every resource and link created can be traced back to a TBM cost center, product and business capability.

Developers do not need to memorize the intricate pricing grids or networking constructs of each cloud; the AI-native platform embeds that expertise and constantly refines it based on real-world TBM outcomes. This dramatically shortens feedback loops between architectural choices and financial results, turning each deployment into a learning opportunity.

Preemptive cybersecurity baked into the TBM playbook

The rise of agentic AI and AI-native platforms in multicloud environments is happening against a backdrop of escalating cyber risk. Gartner estimates that by 2030, preemptive cybersecurity technologies will account for more than 50 percent of IT security spending, up from under five percent in 2024, as organizations shift away from pure detection-and-response models. Gartner+2Gartner+2

Preemptive cybersecurity in a multicloud network means constantly mapping attack surfaces, predicting which paths attackers will likely use and shutting them down before they are exploited. It also means using deception – decoy endpoints, fake credentials, synthetic data streams – to slow down attackers and give defenders more time. When woven into multicloud networking, this becomes a living shield that adapts as topology and traffic change.

For TBM, the crucial step is to treat preemptive controls as financial products with measurable risk-adjusted returns. A sophisticated preemptive module that relies heavily on deep packet inspection or edge AI may increase network processing costs and introduce some latency. TBM leaders will need to quantify whether those costs are justified by the reduction in incident probability and impact. That requires integrating preemptive cybersecurity metrics – such as blocked kill chains or reduced exposure windows – into the same dashboards that track cloud spend and business KPIs.

Agentic AI can help reconcile these dimensions. Imagine a “security-finance mediator” agent whose job is to propose configurations where incremental spending on preemptive controls leads to the largest reduction in modeled risk, and to reverse or right-size controls that deliver poor returns. In this way, TBM and security stop competing for budget and instead co-design the multicloud defense posture.

Edge, IoT and the new frontiers of multicloud TBM

As enterprises push AI and analytics closer to the edge, multicloud networking’s footprint extends far beyond core cloud regions. StartUs Insights reports that edge computing trends such as Edge AI, 5G and industrial IoT are enabling local, low-latency processing that reduces backhaul traffic and speeds decision-making. StartUs Insights A European report on edge AI notes that executing AI algorithms locally can significantly reduce latency and improve responsiveness by bypassing remote data centers. DATEurope Google Cloud’s 2024 State of Edge Computing survey similarly finds that enterprises are embracing edge to balance performance, data governance and security. Google Cloud

For TBM, this creates both challenges and opportunities. On one hand, cost structures become more complex, with spending spread across local compute, private 5G, sensor networks and cloud aggregation points. On the other, there are new levers for optimization: deciding where exactly to run AI inference, how much data to ship to central clouds, and which vendor’s edge offerings create the best balance of reliability, latency and price.

Agentic AI is well-suited to this distributed world. A cluster of agents can monitor the health, utilization and cost of hundreds or thousands of edge locations, automatically shifting workloads between edge, regional and central clouds as conditions change. AI-native development platforms can generate “edge-aware” deployment manifests that understand limitations like intermittent connectivity and local regulatory constraints. Preemptive cybersecurity technologies can deploy traps and tight segmentation at the edge, containing attacks before they spread via multicloud networks.

The role of TBM in this scenario is to set the rules of the game. Policy statements might specify that AI inference for safety-critical industrial processes must always occur within a certain radius of the physical site, that data from specific sensors cannot leave a country, or that edge connectivity costs must stay defined thresholds below as a percentage of site revenue. Agentic AI then treats those policies as constraints in a continuous optimization problem.

Governing the governors: Avoiding shadow AI and runaways

As enterprises put more power into the hands of agentic AI, a new class of risk is emerging: shadow AI at scale. Gartner recently warned that by 2030, around 40 percent of enterprises could suffer breaches tied to “shadow AI” – unapproved AI tools adopted by employees without proper oversight, leading to data leakage and compliance violations. IT Pro

The same danger applies to agentic systems managing multicloud networks and TBM. If teams quietly deploy unsanctioned AI agents that have access to credentials, APIs and financial data, the potential for damage is enormous. TBM and security leaders must therefore apply the same discipline to AI agents that they demand for any critical system: clear inventories, access controls, change management and kill switches.

Reuters’ coverage of Gartner’s agentic AI forecast notes another subtle risk: hype-driven projects that burn budget without delivering value, leading to cancellations and disillusionment. Reuters TBM is uniquely positioned to prevent this by insisting that every agentic AI initiative has explicit, measurable financial and operational goals, such as reducing cross-cloud data transfer costs by a defined percentage, shortening incident response times, or cutting the time to onboard new regions and edge sites.

In practical terms, that means extending TBM frameworks to include “AI value accounting.” This encompasses not only the direct cost of AI infrastructure and licensing, but also the downstream impacts on network charges, security posture and business outcomes. Agentic AI becomes both a cost center and a value generator whose performance must be tracked like any other asset.

Closing thoughts and looking forward

By 2026, multicloud networking will be far too dynamic and complex for humans alone to manage effectively. Agentic AI and AI-native development platforms are emerging as the control planes that will keep this fabric coherent, secure and economically viable. Yet these technologies will only fulfill their promise if TBM evolves from a rearview-mirror discipline to a forward-looking governance engine.

In this new model, TBM defines the policies and economic principles that agentic AI must obey. AI-native platforms ensure that every new service, from a simple API to a global AI inference layer, is deployed in alignment with those policies. Preemptive cybersecurity is woven into the network so that attacks are anticipated and disrupted before they can propagate through multicloud linkages. Edge and IoT sites are treated as first-class citizens in the TBM universe, with clear cost models and optimization strategies.

The journey will not be smooth. Some agentic AI initiatives will fail, and some early platforms will overpromise. But the direction is clear: TBM will increasingly be expressed as code and policies inside autonomous systems, not just as PowerPoint and spreadsheets. Organizations that move early, invest in shared data foundations and establish strong AI governance will be able to transform cloud chaos into a disciplined, AI-driven fabric that supports innovation without sacrificing control.

Over the next 18 months, the most impactful steps TBM leaders can take are to partner deeply with platform engineering and security teams, experiment with narrowly scoped agentic AI pilots in multicloud networking, and build telemetry pipelines to measure results in real time. In doing so, they will turn multicloud networking from a sprawling cost liability into a strategic, self-optimizing asset at the heart of technology business management.

Reference sites

“Market Perspective: Multicloud Networking Will Inflect in 2024” – IDC / F5 – https://www.f5.com/go/report/idc-report-multicloud-network-technology-trends-2024

“Multicloud Networking: Definitions, Benefits, and Challenges” – Kentik – https://www.kentik.com/kentipedia/multicloud-networking/

“Build Your Agentic AI Prototype” – Info-Tech Research Group – https://www.infotech.com/research/ss/build-your-agentic-ai-prototype

“Don’t Delay in Building Preemptive Cybersecurity Solutions” – Gartner – https://www.gartner.com/en/articles/preemptive-cybersecurity-solutions

“Top 10 Edge Computing Trends in 2024” – StartUs Insights – https://www.startus-insights.com/innovators-guide/edge-computing-trends/

Benoit Tremblay, Author, IT Security & Business Management, Montreal, Quebec.
Peter Jonathan Wilcheck, Co-Editor, Miami, Florida.

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