Ravi Tummalapenta, Executive Director at JPMorgan Chase, focused on enterprise AI platforms & Engineering and agentic system infrastructure.
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Building an enterprise AI platform is not about picking the right model. It is about building the right foundation.
Most organizations make the same costly mistake: They connect an LLM directly into an application and call it an AI platform. Months later, they face reliability failures, runaway costs and employees who do not trust the system. The organizations succeeding at enterprise AI do the opposite. They start with the stack.
After years of building enterprise AI deployments in regulated environments, I have found that every successful platform shares seven foundational layers. Miss any one, and the system becomes fragile.
Governance is not a feature you add later. It is the foundation on which everything else is built.
In regulated environments, this layer is nonnegotiable. Financial institutions, for example, must comply with model risk management guidelines requiring documentation, validation and monitoring of AI systems in production. But the same principle applies everywhere. Every enterprise AI platform needs clear policies for what AI can do, who can access it and how activity is audited.
In practice, governance works best when built into the platform from the beginning through access controls, role-based permissions, guardrails, audit logging, model validation and incident response procedures.
A model is only as good as the data it can access. The data layer determines whether your AI platform delivers generic responses or useful context-aware answers.
This layer connects your AI platform to the information employees need: structured databases, document repositories, real-time feeds and knowledge bases. The critical challenge is data lineage and quality. AI systems that access poor-quality or outdated data produce confidently wrong answers with serious consequences.
The method that works best is treating the data layer as an engineering product, with secure connectors, data quality validation, permission-aware retrieval and documentation of what data the AI can and cannot access.
Most organizations think this is the only layer that matters. It is important, but it is just one of seven.
The model layer is where you select, configure and manage the large language models powering your platform. The key principle is multi-provider architecture. Relying on a single provider creates dependency risk. Models are updated, APIs go down and pricing changes.
Leading enterprise AI teams build model-agnostic infrastructure from the start, integrating multiple providers and routing requests to the right model based on task type, cost, latency and performance. This approach keeps applications flexible as models evolve.
If the model layer is the engine, the gateway layer is the transmission system that makes everything work reliably at scale.
The AI gateway sits between applications and LLM providers. It handles routing, failover, load balancing, rate limiting, cost controls and observability. Without it, every team builds its own integrations, creating a fragmented, ungoverned mess.
In large deployments, this layer becomes a control point for improving reliability, managing provider failures and enforcing cost and usage limits without requiring every application team to rebuild its integration.
This is the layer most organizations overlook, and the one that will define the next generation of enterprise AI.
Today's AI systems are largely stateless. Each conversation starts from scratch. For simple one-turn queries this is acceptable. For complex multi-turn workflows, statelessness is a fundamental limitation.
Research confirms this gap. A 2025 study found that memory in LLM agents is significantly under-evaluated, with existing systems failing to reflect the interactive multi-turn nature of real-world deployments. Separately, researchers found that many text-to-SQL systems are evaluated only in single-turn settings.
Memory-augmented AI agents solve this by retaining context across conversation turns, including short-term memory for within-session context, long-term memory for cross-session continuity and semantic memory for domain knowledge retrieval. The hard part is deciding what should be remembered, forgotten, accessed and measured.
Single-model responses are no longer sufficient for complex enterprise workflows. The orchestration layer coordinates multiple AI agents, tools and workflow steps to complete multistep tasks.
Frameworks like LangGraph enable graph-based agent orchestration with branching logic, parallel execution and state management. This represents a shift from AI as a question-answering tool to AI as an active participant in workflows.
The orchestration layer includes multi-agent coordination, workflow state management, tool use, error handling and human-in-the-loop intervention points. In practice, this layer should be added carefully; not every use case needs a multi-agent system, but every production workflow needs reliability.
The application layer is where your employees actually experience AI. Everything below it exists to make this layer work reliably, securely and at scale.
Different user groups need different interfaces. A data analyst needs natural language access to databases without writing SQL. A software engineer needs a coding assistant that understands your codebase. A knowledge worker needs a research agent that synthesizes information from multiple sources.
The key principle is decoupling. Applications should be able to swap models, adjust memory configurations and modify orchestration logic without rebuilding from scratch. This adaptability is what future-proofs your AI platform.
Not every organization needs all seven layers on day one. Build from the bottom up.
Start with governance. Add the data layer next. Build the gateway layer early as it becomes more valuable with every new use case. Implement memory when use cases become multi-turn. Add orchestration when single-agent responses are no longer sufficient.
The organizations treating AI as a stack, rather than a single model integration, are building durable competitive advantages.
The views expressed in this article are the author's own and do not represent the views of JPMorgan Chase & Co. or any of its affiliates. All information contained herein is based on the author's general industry experience.
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