Grading SaaS Resilience for the Agentic Era

Feb 8, 2026
ai saas enterprise sap agentic-ai

Adam Khoo’s February 2026 video “Software Stocks Going to Zero?” asks a simple question: which software companies survive when AI agents can do what dashboards do?

His answer: AI agents replicate interfaces. They don’t replace legally binding records, deep workflows, or institutional memory.

He built a framework around this. Six criteria, scored 0-100, rolled into a composite 1-10 resilience score. I watched it, applied it to SAP (which he mentioned but didn’t grade), and found the framework missing four dimensions that matter for the agentic era.

Khoo’s Framework

Six criteria. Each measures a different layer of defensibility.

Criterion	What It Measures	Disruption Logic
System of Record (SoR)	Central data bank — audit trails, legal compliance, master data	Hardest to disrupt. “Heart transplant on a marathon runner.”
System of Engagement (SoE)	Dashboards, forms, UI/UX layer	Most vulnerable. AI agents replace click-through interfaces.
System of Intelligence (SoI)	Agentic/operator layer — autonomous task execution	Emerging battleground. Who provides the “brain”?
Data Model Depth	Domain specificity, regulatory knowledge, proprietary schemas	Deep models = high switching costs.
Agent Monetization	Ability to pivot from seat-based to consumption/outcome pricing	Revenue resilience under headcount reduction.
AI Infrastructure	Ownership of compute, cloud, foundation models	Captures value at the base of the AI stack.

The key insight is the three-layer value stack. Companies spanning SoR + SoE + SoI are harder to dislodge than those relying on one layer. Microsoft (9.5) dominates all three layers plus infrastructure. Adobe (5.5) is “overwhelmingly a System of Engagement” — creative tools being democratised by Canva, Midjourney, and Runway.

Applying It to SAP

SAP was mentioned but not graded. Here’s my assessment using Khoo’s criteria.

Criterion	Score	Rationale
System of Record	95	The definitive ERP system of record. Financials, inventory, procurement, HR, supply chain. Legally mandated audit trails across 400,000+ customers. Khoo’s “heart transplant” metaphor applies maximally.
System of Engagement	70	SAP Fiori provides a modern UI. Joule is becoming the conversational front-end. But SAP has never been a UX benchmark.
System of Intelligence	75	400+ AI use cases across applications. Joule Agents handle accounts receivable, maintenance, procurement. Multi-agent collaboration across departments. Agentic orchestration planned for H1 2026.
Data Model Depth	95	Among the deepest in enterprise software. Spans financials, supply chain, manufacturing, HR, and industry-specific schemas. Datasphere and Business Data Cloud unify this.
Agent Monetization	65	Investing in Joule-based consumption, but hasn’t shifted pricing as aggressively as Salesforce (Agentforce credits) or ServiceNow (Pro Plus SKU). Still on traditional licensing.
AI Infrastructure	45	Runs on hyperscaler clouds (AWS, Azure, GCP). Uses 40+ external AI engines via Joule. Partners rather than competes at the infrastructure layer.

Composite: ~8.5-9.0/10. SAP’s SoR and data model depth make it among the most resilient enterprise software companies against agentic disruption. Weak spots: AI infrastructure ownership and pricing model transition speed.

What the Framework Gets Right

Three strengths worth noting.

The three-layer decomposition works. SoR / SoE / SoI maps cleanly to how enterprise software creates value. It correctly identifies the engagement layer as the most disruption-prone.

Data model depth captures switching costs. Deep, domain-specific data models create lock-in that transcends the software. A company’s institutional memory lives in these schemas.

Agent monetization addresses revenue risk. The pivot from seat-based to consumption pricing is the critical commercial question for SaaS survival.

What It Misses

The framework was designed for stock-picking. For evaluating platforms in the agentic enterprise, four dimensions are missing: human-AI interaction quality, governance readiness, workflow complexity, and ecosystem orchestration.

⚖️

These aren’t theoretical gaps. Gartner predicts over 40% of agentic AI initiatives will be cancelled by 2027 due to governance failures, not technology failures.

Enhanced Framework: Four New Dimensions

Dimension 7: Human-AI Interaction Design

What it measures: How well the platform supports graduated autonomy — from full human control to autonomous operation with post-hoc audit.

Khoo treats human interaction implicitly. SoE captures the old interface. SoI captures the agent layer. But neither evaluates the quality of the handoff between human and machine.

Singapore’s Model AI Governance Framework defines four levels of human involvement:

Agent proposes, human operates
Collaboration
Agent operates, human approves
Agent operates, human observes

A platform’s ability to support all four levels — dynamically, within a single workflow — is a critical resilience factor.

Key sub-criteria:

Autonomy spectrum: Can the platform adjust human oversight based on task risk, confidence, and context?
Agentic UX patterns: Progressive disclosure, confidence visualisation, decision traceability, supervisor-worker dashboards?
Cognitive load management: Does it prevent automation bias by surfacing agent reasoning at the right abstraction level?

💡

The EDPS warns that poorly designed hybrid systems “foster the worst of both worlds.” The handoff matters as much as the automation.

SAP score: 80/100. Joule provides contextual, natural-language interaction across business processes. Multi-agent collaboration with human approval gates exists for high-impact decisions. Agentic orchestration still rolling out in H1 2026.

Dimension 8: Governance & Responsible AI Readiness

What it measures: Built-in governance infrastructure — policy-as-code enforcement, audit trails, kill switches, explainability, and alignment with EU AI Act and Singapore MGF.

Key sub-criteria:

Policy-as-code: Are governance rules enforced programmatically at runtime?
Decision traceability: Can every agent action be traced, explained, and audited?
Technical safety: Circuit breakers, kill switches (sub-one-second termination), credential rotation, state rollback
Regulatory alignment: Built-in support for EU AI Act, Singapore MGF, industry-specific requirements
Economic governance: Cost caps, usage limits, inference monitoring to prevent runaway agent spending

SAP score: 90/100. SAP’s ERP heritage gives it best-in-class audit trails, approval chains, RBAC, and compliance documentation. That’s exactly the governance infrastructure agentic systems need. The challenge: extending this to novel agentic behaviours beyond traditional ERP workflows.

Dimension 9: Workflow Complexity & Reversibility

What it measures: Depth and interconnectedness of workflows, and reversibility of agent-initiated actions.

Khoo’s SoR criterion captures data depth. It doesn’t capture the operational complexity of workflows running on that data.

Consider procurement: supplier qualification, contract negotiation, purchase order creation, goods receipt, three-way matching, payment. Dozens of interconnected steps across multiple systems. The complexity and irreversibility of these workflows is itself a moat.

Key sub-criteria:

Cross-functional depth: How many departments and systems does a single workflow span?
Action reversibility: Can agent-initiated actions be rolled back? Financial postings and regulatory submissions can’t.
Exception handling: How well does the platform handle edge cases, cascading failures, and multi-agent coordination failures?

SAP score: 95/100. SAP manages arguably the most complex, mission-critical, cross-functional workflows in enterprise software. McKinsey notes: for every $1 spent developing an AI model in ERP, organisations need $3 in change management. That’s a measure of the workflow complexity agents must navigate.

Dimension 10: Ecosystem Orchestration & Lock-in

What it measures: The platform’s ability to act as the orchestration hub for multi-agent ecosystems — running its own agents, integrating external agents, and coordinating across enterprise boundaries.

The post-SaaS era will be defined by agent-to-agent interactions. A platform’s value depends on serving as the coordination layer — what Deloitte calls the “attention layer.”

Key sub-criteria:

Multi-vendor agent integration: Can the platform orchestrate agents from different vendors?
Protocol standardisation: Support for MCP, A2A, and emerging agent communication standards
Marketplace/composability: Agent marketplace, composable architecture, third-party skill integration
Agent identity & access: Dynamic, context-sensitive permissions for autonomous agents

SAP score: 92/100. Business Technology Platform, Microsoft 365 Copilot integration, 40+ AI engines via Joule, Datasphere as data federation layer. Joule Studio enables custom agent development. Main gap: nascent support for fully open agent-to-agent protocols.

Comparative Summary

Dimension	Origin	Microsoft	SAP	ServiceNow	Salesforce	Adobe
System of Record	Khoo	80	95	88	65	25
System of Engagement	Khoo	85	70	82	80	95
System of Intelligence	Khoo	90	75	85	72	40
Data Model Depth	Khoo	78	95	92	55	30
Agent Monetization	Khoo	95	65	90	70	35
AI Infrastructure	Khoo	98	45	40	30	15
Human-AI Interaction	New	85	80	82	75	60
Governance Readiness	New	82	90	85	70	45
Workflow Complexity	New	70	95	78	55	20
Ecosystem Orchestration	New	88	92	75	72	40
Enhanced Composite	—	~9.5	~8.8	~8.8	~6.8	~4.5

The enhanced framework shows SAP’s resilience is underrepresented in Khoo’s original model. SAP’s governance infrastructure, workflow complexity, and ecosystem position — the three dimensions most relevant to the agentic era — weren’t captured. Adobe’s vulnerability is even more pronounced when governance and workflow depth factor in.

Key Takeaways

Three principles for evaluating platforms in the agentic era.

Governance is the new moat. Platforms with decades of built-in audit trails, approval chains, and compliance infrastructure have a structural advantage in agentic deployment. Governance readiness determines which organisations scale from pilot to production.

The engagement layer is dissolving. Traditional dashboards collapse into conversational and agent-mediated interactions. Companies whose primary value was the UI layer face existential risk. Companies with strong SoR + SoI survive because the agent becomes the new engagement layer.

Human-AI interaction design is safety-critical. Enterprises should evaluate not just whether a platform has AI agents, but whether it implements graduated autonomy, meaningful human oversight, and cognitive load management.

⚖️

Khoo’s framework asks: “Can this platform survive?” The enhanced framework asks: “Can this platform be trusted to act autonomously on behalf of the enterprise?” Both questions matter. They answer different things.

Framework Weighting by Role

The 10 dimensions can be weighted based on who’s evaluating:

Investors: Weight Agent Monetization and AI Infrastructure (Khoo’s emphasis)
CIOs/CTOs: Weight Governance Readiness, Workflow Complexity, and Ecosystem Orchestration
Chief AI Officers: Weight Human-AI Interaction Design and System of Intelligence
Regulators: Weight Governance Readiness and Human-AI Interaction Design

Grading SaaS Resilience for the Agentic Era

Khoo’s Framework

Applying It to SAP

What the Framework Gets Right

What It Misses

Enhanced Framework: Four New Dimensions

Dimension 7: Human-AI Interaction Design

Dimension 8: Governance & Responsible AI Readiness

Dimension 9: Workflow Complexity & Reversibility

Dimension 10: Ecosystem Orchestration & Lock-in

Comparative Summary

Key Takeaways

Framework Weighting by Role

Sources