AI agents are spreading rapidly across organizations. They replace specific components, form a layer on top of existing systems and introduce something fundamentally new: probabilistic decision making within deterministic architectures.
The question is not whether agents should be used. What matters is whether you intentionally design how they work within your business. Without clear boundaries, complexity scales faster than value. Here’s a framework for turning that complexity into leverage.
Do you allow agents to disrupt your stack?
AI agents are popping up in enterprise stacks everywhere. Sales manages an SDR agent. Support uses a chatbot. Marketing is rolling out a content co-pilot. Operational experiments with an agentic workflow tool. Each initiative is meaningful in itself. Together they introduce new behavior into already complex architectures.
Some argue that AI will replace traditional CRM, CMS, CDP or MAP platforms, simplifying the stack in the process. Our research data shows the opposite. Only 30.1% of companies are replacing specific SaaS use cases with AI. Much more, 85.4%enhance existing use cases with AI.
Adoption looks impressive at first glance. But only 23.3% of companies have agents in full production. As of May 2025, only 6.3% had fully integrated AI into the marketing stack. Teams can experiment quickly. They struggle to connect agents end-to-end in deterministic systems. The real challenge is integration.
Without a shared architectural model, each team defines good output differently. Policy lives in slide decks. Guardrails differ per department. The context is fragmented across the tools. The result is drift, risk and fragile automation. What companies need are not more pilots. They need a clear framework for how agents operate within the stack.
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The architectural change we actually need
Most organizations treat agents as if they were just add-ons. A co-pilot is added here, an automation agent is controlled there, and they connect to existing workflows as if they were just a SaaS module. That approach worked if everything in the stack followed deterministic logic. It doesn’t work if decision-making itself becomes probabilistic.
Traditional systems are designed to protect the truth of the business. Customer data in CRM, product information in PIM, consent status, pricing logic and compliance rules. These systems define what is correct, verifiable and controlled. They are the foundation that keeps the company consistent across regions, brands and teams.
Agents introduce something fundamentally different. They do not simply execute predefined logic. They interpret signals and determine which action makes sense in the context. That shift means that the stack now contains two types of systems: systems that define the truth and systems that decide how to act on that truth at a specific moment.
The probabilistic block works as a system of context on top of registration systems. The systems of record remain responsible for data integrity and policy enforcement. The agentic layer is responsible for interpreting that data and recommending or executing actions within defined boundaries.
The boundary between these roles is crucial. When contextual agents begin modifying customer data, product features, or compliance logic without explicit restrictions, the risk increases. When deterministic foundations explicitly determine how agents can act, scale becomes possible.
The architectural shift is clear: contextual decision making must be purposefully designed to operate within the controlled business truth.
Unpacking the agentic stack framework
The framework combines deterministic SaaS and probabilistic AI into one coherent architecture. To understand it properly, it helps to go through the layers from bottom to top.
The hyperscale layer
At the base of the stack are commoditized possibilities. This is where scale, storage and performance live. It doesn’t differentiate the company, but it makes everything above possible. The hyperscale foundation consists of three core components:
- Cloud infrastructure providers such as Google Cloud, Microsoft Azure, AWS, Scaleway, Oracle Cloud and Alibaba Cloud provide computing capacity, networking and elasticity.
- Cloud data warehouse and lakehouse platforms such as Google BigQuery, Microsoft Azure Synapse, AWS Redshift, Snowflake, and Databricks centralize enterprise data and enable governance and performance at scale.
- LLM models from OpenAI, Anthropic, Mistral, Meta and Google provide general reasoning capabilities that can be adapted to business-specific use cases.
This layer is usually purchased. It scales up power and flexibility, but competitive advantage rarely arises here. The advantage lies in the way the upper layers are structured and controlled.
Record tier system
Above hyperscale is the operational backbone of the company. This is where corporate truth lives.
CRM, CMS, DAM, MAP, CDP, e-commerce and PIM systems manage customer and product data, enforce consent and identity resolution, apply pricing logic and embed governance and regulatory compliance. Together, they ensure that business data remains accurate, auditable, and aligned to policy across regions and business units.
Most of these are long-tail commercial SaaS solutions that provide reliability and control. Agents do not overwrite this layer. They would have to operate on top of it.
System of differentiation layer
Above the registration systems are the options that express the business strategy. This layer reflects how a company chooses to compete.
These are often hypertail, typically built applications created with low-code or no-code platforms or custom development. Customer portals, partner portals, dealer locators, pricing calculators, product configurators, and orchestration tools are typically present here.
While systems of registration protect the truth, systems of differentiation make brands stand out. Together, these three layers form the deterministic basis of the stack.
Intent model layer
On top of this deterministic base is the probabilistic block. The probabilistic block starts with intention. This is the layer where hyperscale LLMs are trained, fine-tuned and packaged to behave within your corporate standards.
In practice, you take general models and adapt them to corporate standards, including:
- Brand and tone.
- Product truth.
- Approved claims.
- Legal and Compliance Requirements.
- Privacy Restrictions.
- Risk appetite.
You also package the rules and decision logic that determine what an agent can do, when to escalate it, and which actions require human approval. This is also where data from systems of record is prepared for use by agents, so that agents act based on consistent definitions of customers, products and policies rather than improvising from a fragmented context, such as a brand LLM for marketing.
This layer is mainly constructed and does not in itself deliver any results to the end users. It makes each agent’s output more secure, consistent, and scalable.
Agent capabilities layer
Above the intent model layer are AI agents that provide common business capabilities in marketing, sales, customer support, and data analytics.
These agents are typically developed and sold by third-party vendors. Commercially, they resemble SaaS products, often priced based on usage, volume or outcome-based models. Organizations adopt them to accelerate specific capabilities without having to build everything themselves.
They operate probabilistically, but within the boundaries defined by the intent model layer. They act based on systems of record and differentiation systems without redefining the business truth.
This layer scales shared capabilities across the organization while remaining aligned with the company’s defined intent.
Agent differentiation layer
At the top are company-built AI agents designed around proprietary workflows, internal data, and domain expertise, using low-code, AI coding tools like n8n, Lovable, or Replit. These hypertail agents reflect how the brand chooses to compete and operate.
Unlike commercially available agents, these are built in-house or with strategic partners. They are tailored to business-specific logic, segmentation models, pricing strategies, go-to-market processes, or retention frameworks that third-party vendors cannot fully replicate.
Examples include a custom go-to-market agent aligned to internal ICP definitions, a churn prevention agent trained on proprietary behavioral signals, or a pricing intelligence agent that operates within company-defined policy constraints.
This is where differentiation comes to the fore in the probabilistic block. When intent is clearly defined and systems of record remain stable, these agents become strategic assets rather than isolated experiments.
Turning agent proliferation into strategic influence
AI agents are here to stay. The question is whether they operate within a coherent architecture or next to it.
When probabilistic systems are stacked on top of deterministic foundations without clear intentions and guardrails, complexity scales faster than value. When these boundaries are purposefully designed, agents reinforce what is already working.
The agentic stack framework turns sprawl into structure and experimentation into sustainable advantage.
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