Foundation models, AI agents, and MLOps are reshaping enterprise AI architecture—clarity is key to investment decisions.
Enterprise AI is entering a new phase. The shift from narrow models to foundation models, from static scripts to autonomous agents, and from ad hoc deployment to mature MLOps is changing how organizations build, scale, and govern AI. These aren’t technical upgrades—they’re architectural shifts that affect cost, risk, and ROI.
Yet many enterprises still treat AI as a collection of disconnected tools. Without a clear view of the emerging stack, investments become fragmented, governance weakens, and performance plateaus. Understanding the new AI stack is essential—not just for alignment, but for measurable outcomes.
1. Foundation Models Change the Build-vs-Buy Equation
Foundation models—large-scale pretrained models that can be adapted across use cases—are redefining how enterprises approach AI development. Instead of building models from scratch, teams fine-tune or prompt existing models to fit domain-specific needs. This reduces time-to-value but introduces new dependencies.
The build-vs-buy decision now includes model access, licensing, customization, and control. Enterprises must weigh the benefits of speed against the risks of vendor lock-in, opaque training data, and limited explainability. In financial services, where model transparency is critical, foundation models may require additional validation layers before deployment.
Evaluate foundation models based on adaptability, transparency, and long-term control—not just performance.
2. AI Agents Introduce New Complexity in Workflow Automation
AI agents are autonomous systems that perform tasks by chaining models, tools, and APIs. Unlike single-purpose models, agents operate across workflows—retrieving data, making decisions, and triggering actions. This unlocks new automation potential but adds architectural complexity.
Agents require orchestration, error handling, and guardrails to ensure reliability. Without these, agents can produce inconsistent outputs or trigger unintended actions. In retail and CPG, where agents are used for dynamic pricing and inventory updates, weak orchestration can lead to pricing errors or stockouts.
Design agent workflows with robust orchestration and clear boundaries to avoid cascading failures.
3. MLOps Is No Longer Optional
MLOps—the discipline of managing the lifecycle of machine learning models—is now essential for scaling AI. It includes versioning, monitoring, retraining, and deployment pipelines. Without MLOps, models drift, performance degrades, and compliance risks grow.
Many enterprises still rely on manual processes or fragmented tooling. This slows down deployment and increases operational overhead. MLOps must be treated as a core capability, integrated with DevOps, data engineering, and governance. In healthcare, where models must be revalidated regularly, MLOps ensures traceability and auditability.
Invest in MLOps as a foundational capability—not a supporting function.
4. Data Infrastructure Must Support Model Interoperability
The new AI stack depends on seamless data access across systems. Foundation models and agents require structured, unstructured, and real-time data—often from multiple sources. Without unified access layers, models become brittle and agents fail to generalize.
Data interoperability includes schema alignment, semantic consistency, and latency management. Enterprises must move beyond siloed pipelines and build shared data platforms that support AI workloads. In manufacturing, where sensor data feeds predictive models, inconsistent formats can block model reuse across plants.
Build shared data infrastructure that supports real-time, multi-format access for AI workloads.
5. Governance Must Expand to Model Behavior and Agent Actions
Traditional AI governance focuses on data quality and model bias. The new stack requires broader oversight—covering model behavior, agent decisions, and system-level outcomes. This includes monitoring for hallucinations, unintended actions, and drift across agents and models.
Governance must be embedded in the stack—not bolted on. This means real-time monitoring, explainability tooling, and policy enforcement across model and agent layers. In financial services, where agents may interact with customer data or execute transactions, governance gaps can lead to compliance exposure.
Extend governance to cover model behavior and agent actions—not just data inputs.
6. Cost Models Must Reflect Stack Complexity
AI costs are no longer limited to compute and storage. Foundation model licensing, agent orchestration, MLOps tooling, and data infrastructure all contribute to spend. Without clear cost attribution, budgets balloon and ROI becomes difficult to measure.
Enterprises must track cost per model, per agent, and per workflow. This includes inference costs, retraining cycles, and orchestration overhead. In large organizations, where multiple teams deploy agents independently, lack of cost visibility leads to duplication and inefficiency.
Track AI costs at the model, agent, and workflow level to ensure financial accountability.
The new AI stack is more powerful—but also more complex. Foundation models, agents, and MLOps offer scale and flexibility, but only if supported by clear architecture, governance, and cost discipline. Enterprises that treat AI as infrastructure—not experimentation—will be better positioned to deliver ROI.
What’s one architectural capability you believe will be critical for aligning foundation models and agents with enterprise-wide governance and scale? Examples: unified orchestration layer, shared model registry, real-time policy enforcement.