Introduction

Agentic AI is transitioning from buzzword to boardroom reality. In the past year, pilot programs have evolved into full-scale rollouts, vendors have revamped their product strategies, and risk management teams are developing new protocols. This inflection point is due to a perfect storm of factors: decreasing model costs, richer ecosystems of tools, emerging standards for interoperability, and early successes proving that autonomous software agents can achieve results in complex, regulated environments.

This article demystifies this shift for an informed yet non-technical audience. We’ll explore the implications for enterprises, the evolving nature of SaaS business models, the urgent need for risk management and governance, and a practical playbook for leaders to harness value without relinquishing control.

Key Takeaways

• Modernization is now more than a cloud migration goal; it’s crucial for safely operating autonomous agents at scale.
• SaaS models are evolving from traditional user-based pricing to a focus on outcomes and orchestration, impacting pricing, distribution, and product design.
• Identity and governance have become central to AI strategy, making enterprise controls for agent sprawl and access necessity.
• Security requirements are increasing rapidly as autonomous tools face real-world vulnerabilities.
• Market developments indicate that autonomous agents represent a rapidly growing multi-billion-dollar sector.

Why Now: From Copilots to Autonomous Workflows

For the last 18 months, organizations have primarily experimented with copilots that aid in drafting, summarizing, and suggesting content. In contrast, agentic AI takes a step further, enabling agents to execute multi-step tasks, operate tools, initiate actions, and liaise with other agents. Several key trends are driving this acceleration:

• Refocused enterprise modernization: Companies like Microsoft are reorienting their modernization efforts as pathways for agents to integrate seamlessly within existing systems, exemplified by Azure Accelerate pairing expert services with agent capabilities.
• Creation of agent-centric frameworks: Organizations such as Citi have launched Stylus Workspaces to streamline complex multi-step tasks by integrating agent capabilities seamlessly across their systems.
• Emerging standards: New protocols like the Model Context Protocol (MCP) and Agent-to-Agent (A2A) interfaces are gaining traction, complemented by tools such as Okta’s Cross App Access for enhanced governance of agent apps.

Enterprises: Modernization as a Platform for Autonomy

Many organizations find that simply adding agents to outdated, fragile workflows yields unreliable results. The root problem often lies in technical debt that hampers performance. Thus, modernization is now reframed to prioritize agent readiness.

• Integrate autonomy within migration: Initiatives like Azure Accelerate ensure that modernization fosters environments where agents can function effectively, including necessary observability and guardrails.
• Focus on controlled, high-impact workflows: Citi’s initial deployment of agents targets knowledge-intensive tasks that span systems yet remain within robust identity and data protections.
• Prioritize identity-first security: As concerns rise about agentic browsers and credential management, leaders emphasize the need for controls embedded around identity and access rights from the outset.
• Establish governance over agent interactions: Tools like Okta’s Cross App Access centralize authorization management, mitigating issues related to agent proliferation and integration complexity.

What Success Looks Like

• A robust cloud and data platform enabling agents to operate safely with well-defined records, event management, and straightforward APIs.
• An approved catalog of agents and tools, constrained by policies of identity and access.
• Visible tracking of agent actions, including comprehensive audit trails.
• A governance framework capable of halting risky behaviors and managing non-compliance in real time.

SaaS: From Interfaces to Orchestration

Agentic AI transforms the traditional SaaS paradigm, moving beyond mere user interfaces to orchestrations that call APIs and provide finished results. Bain highlights that the key to success will include owning systems of record and adopting standards while focusing on outcome-based pricing rather than volume-based metrics.

Early Indicators:

• Outcome-oriented pricing models: For instance, Moody’s has developed agentic solutions that drastically reduce time spent on tasks like credit memo creation, heralding a shift toward performance-based pricing.
• Focus on orchestration: Walmart’s deployment of multi-agent systems across various operational facets illustrates the effective coordination of tasks via ‘super-agents.’
• Adaptation of legacy systems: The narrative of “software’s demise by AI” is overstated; instead, many systems will continue to evolve by integrating agents to enhance, not replace, existing workflows.

Implications for SaaS Leaders

• Product development: Prioritize creating secure, well-documented APIs for agent utilization.
• Pricing strategies: Experiment with outcome-oriented metrics and hybrid pricing models to better capture value.
• Distribution methods: Transition to selling orchestration instead of exclusively screen access, ensuring seamless integration of agents within overall enterprise protocols.
• Maintaining a competitive edge: Safeguard your systems of record with stringent domain regulations, approval processes, and compliance checks.

Risk, Security, and Governance: Non-Negotiables

Security teams are actively testing agentic browsers, tool invocation protocols, and multi-agent operations against enterprise vulnerabilities. Findings are revealing urgent considerations.

• Increased expectations for agentic browsers: Early iterations display vulnerabilities, emphasizing the need for stringent identity-bound controls before widespread adoption.
• Strengthening protocols: The Model Context Protocol (MCP) is gaining traction but requires additional hardening to address maintainability and security concerns in existing ecosystems.
• Runtime governance is essential: Research suggests proactive monitoring and detection approaches are necessary for managing agent behavior and compliance dynamically.
• Identity management is becoming critical in commerce and payments through innovations like the Know Your Agent (KYA) framework.
• Anticipate challenges during scaling: According to Gartner, a significant percentage of agentic AI projects may be abandoned due to cost and unclear return on value. Prioritizing governance from the start can help mitigate these risks.

Where Value is Emerging Now

• Knowledge-to-Outcome Automation: Automating research and drafting from trusted sources while maintaining audit trails.
• Finance and Risk Operations: Utilizing agents for tasks like reconciliations and anomaly detection, incorporating proof of their actions.
• Retail and Supply Chain: Orchestrating multiple agents to improve operational efficiencies linked to robotics and digital twin technologies.

A Leadership Playbook for 2025-2026

1. Focus on a handful of controlled workflows: Select 3-5 workflows that deliver clear, measurable outcomes.
2. Integrate identity and authorization into architecture: Assign every agent a human owner, linking their capabilities to specific roles and permissions.
3. Monitor agent behavior proactively: Log actions and decisions made by agents to support audits and investigations.
4. Establish clear processes for agent lifecycle: Implement routines for registration, threat assessments, approvals, and decommissioning.
5. Optimize for cost efficiency: Tailor models and operations to maintain performance while reducing costs.
6. Align pricing with the value delivered: Shift toward outcome-based pricing as agent productivity increases.
7. Prepare teams for the new environments: Train staff to manage agent interactions and ensure swift responses to issues.

Case Snapshots for Inspiration

• Microsoft’s Azure Accelerate: A transformative approach to facilitate agent-ready environments through expert guidance and funding.
• Citi’s Agentic Initiatives: Demonstrates how to effectively integrate agents within secure, identity-focused frameworks.
• Walmart’s Multi-Agent Strategy: Showcases orchestration across customer and operational touchpoints, highlighting effective multi-agent management.
• SaaS Strategy Overhaul by Bain: Advocates for ownership of systems while adapting pricing models to reflect agent efficiencies.
• Worldpay and Trulioo’s KYA framework: Ensures transactions involving agents are secure and compliant by verifying agent identity.

Frequently Asked Questions

Q1) What distinguishes automation, a copilot, and agentic AI?
– Automation generally follows fixed protocols. Copilots assist with suggestions but do not take autonomous actions. Agentic AI, on the other hand, can plan, call tools, and act independently within defined policies.

Q2) Will agents replace existing software?
– Rarely. Most gains arise from enhanced orchestration across existing systems rather than complete replacement. Complex, regulated workflows remain vital.

Q3) Which standards are currently important?
– The Model Context Protocol (MCP) is emerging for linking models to tools and data, while A2A and Cross App Access (XAA) simplify agent interactions with enterprise-scale governance.

Q4) How should return on investment be evaluated?
– Link agent performance metrics to trusted financial indicators like cycle times and error rates, similar to successes observed at Moody’s.

Q5) Are agentic browsers safe enough for enterprises?
– Currently, no. Many early versions display vulnerabilities requiring stringent controls and monitoring.

The Bottom Line

Agentic AI is not just another feature — it represents a transformative model for software and workflows. Companies that position their modernization efforts as platforms for autonomy, integrate identity and governance into their agent strategies, and focus on measurable outcomes will be better equipped to navigate this new landscape. SaaS providers that pivot toward orchestration and outcome-based pricing will also remain competitive, while those who hesitate may face the consequences outlined by Gartner: the costs associated with late-stage adjustments often exceed the price of doing things right from the beginning. The opportunities are ripe, the risks are manageable, and time is of the essence.

Sources and Further Reading

• Microsoft Azure Accelerate and Cloud Accelerate Factory for agent-driven modernization.
• Citi Stylus Workspaces agentic deployment insights.
• Okta Cross App Access and identity-first governance strategies for agents.
• Bain’s insights on the implications of agentic AI in SaaS pricing and operations.
• Moody’s case study on modular agents and their time-to-outcome efficiencies.
• Walmart’s innovations in multi-agent orchestration.
• Wall Street Journal commentary on the resilience of enterprise software amidst AI advancements.
• Research on Model Context Protocol security and ecosystem vulnerabilities.
• Guidelines for runtime governance frameworks for AI agents.
• Worldpay + Trulioo’s KYA and Digital Agent Passport functionalities.
• Gartner’s predictions regarding the sustainability of agentic AI initiatives.
• Insights on the security measures necessary for agentic browsers.