The 7 Layers of Agentic AI Stack

The rise of agentic AI has introduced a technology stack that extends well beyond simple calls to foundation-model APIs.

Unlike traditional software stacks, where value often concentrates at the application tier, the agentic AI stack distributes value more unevenly. Some layers offer strong opportunities for differentiation and moat building, while others are rapidly becoming commoditized.

Here is my 7-layer Agentic AI Stack, which breaks down the ecosystem into distinct layers, highlights where value is likely to accrue:

Strategic implications by layer

Highest moat potential

Layer 5: Cognition & reasoning
Layer 7: Observability & governance
Layer 5: Tooling & enrichment

Why high moat:

• These layers require deep technical expertise, long development cycles, and complex orchestration.
  
• Reasoning and planning architectures are hard to replicate and become differentiators.
  
• Governance, safety, and compliance create enterprise trust moats.
  
• Rich tool/plugin ecosystems can develop platform lock-in.
  

Focus: Advanced reasoning, trust building, system reliability, ecosystem orchestration.
Timeline: 2–5 years to build, extremely hard to replicate.

Medium moat potential

Layer 2: Agent runtime & infrastructure
Layer 4: Orchestration

Why medium moat:

• Useful and specialized, but runtime environments and orchestration are increasingly standardized.
  
• Differentiation comes from performance optimization, state handling, and domain specialization.
  
• Moderately defensible if tightly coupled to specific enterprise workflows.
  

Focus: Specialized runtime skills, multi-agent workflows, memory, and state management.
Timeline: 6–18 months to build, moderately defensible.

Lowest moat potential or commoditized)

Layer 1: Foundation model infrastructure (commoditized)
Layer 3: Protocols & interoperability (commoditized)
Layer 6: Applications (low moat)

Why low moat potential or commoditized:

• Foundation model infra is dominated by hyperscalers; difficult for new entrants to differentiate.
  
• Protocols tend to standardize and commoditize quickly, offering little defensibility.
  
• Applications (especially horizontal copilots) are already crowded and interchangeable. Only vertical, data-rich applications offer some differentiation.
  

Focus: Cost efficiency, speed of execution, ecosystem participation.
Timeline: Weeks to implement, easily commoditized.

The 7 Layers of the agentic AI stack

Layer 1: Foundation model infrastructure

The foundation model infrastructure provides the models, compute, and data infrastructure needed to train, fine-tune, and serve large-scale AI systems at scale.

Models from providers like OpenAI deliver language understanding, reasoning, and multimodal capabilities that higher layers build upon.

Compute resources such as CPUs, GPUs, and TPUs power the heavy lifting behind model training and inference.

Data management and storage systems loke S3 support both large-scale training and real-time access to embeddings or contextual payloads.

APIs and runtime actors provide the interfaces and execution environments for connecting models to external systems.

• Standards such as REST APIs, HTTP, and WebSockets allow for integration.
• Runtimes like AKKA and DBOS coordinate execution flows.

Workflow engines such as Apache Airflow manage model training schedules, inference tasks, and data flows.

Layer 2: Agent runtime & infrastructure (Where agents live)

The agent runtime & infrastructure layer provides the operational environment where agents are deployed, executed, and scaled.

Execution environments such as Docker, Kubernetes, E2B, Replicate, Modal, and RunPod provide the sandboxes in which agents run.

Agent memory systems like Zep give agents the ability to store dialogue history, track goals, and preserve long-term context. This enables persistent agent identity across complex tasks and workflows.

Embedding stores such as Pinecone enable agents to retrieve context-rich knowledge and ground their reasoning in relevant information.

State and messaging protocols play a critical role in coordination.

• APIs such as OpenAI Assistant provide standardized ways to manage interaction.
• Interoperability standards like The Agent Protocol ensure consistency.
• Communication protocols such as gRPC and MQTT enable agents to exchange structured messages across distributed systems and networks.

Layer 3: Protocol & interoperability

The protocol & interoperability layer provides the standards and coordination mechanisms.

Agent interaction and coordination protocols such as Google’s A2A, Cisco’s ANP, and IBM’s ACP define how agents exchange structured messages within distributed environments.

Context and tool standards like the Model Context Protocol help agents represent capabilities consistently and pass contextual information in a structured way.

Bridging mechanisms such as the Agent Gateway Protocol (AGP) connect otherwise siloed agents and platforms, enabling cross-system communication and interoperability at scale.

Layer 4: Orchestration  (Coordinating agent behavior)

Orchestration frameworks like assist with prompt engineering and managing data flow to and from LLMs.

In ither words, these ensure responses are structured, predictable, and routed to the right tool, API, or document.

Without these frameworks, you’d need to manually design prompts, parse outputs, and trigger the correct API calls. Orchestration frameworks streamline this through:

• Multi-agent coordination: Managing how agents collaborate or delegate tasks
• Prompt orchestration: Building, managing, and routing complex prompts
• Tool integration: Allowing agents to call APIs, databases, or code functions
• Memory: Preserving context across turns or sessions (short- and long-term)
• RAG integration: Enabling knowledge retrieval from external sources

Layer 5: Tooling & enrichment (Agents as a service)

This layer expands the range of tasks agents can perform by connecting them to external tools, data sources, and environments.

It enables agents to retrieve knowledge, call APIs, automate workflows, and interact with real-world systems.

Retrieval & knowledge access includes frameworks enable Retrieval-Augmented Generation (RAG).

Agents can ground their outputs in context-rich knowledge from vector databases such as Pinecone and Weaviate, or from enterprise knowledge bases like Confluence and Wikis.

Data extraction tools such as Bright Data enable agents to collect structured and unstructured information from the web.

Tool invocation frameworks like n8n, Zapier enables agents to trigger external APIs, orchestrate multi-step workflows, and integrate into broader enterprise processes.

Search capabilities from providers such as SerpApi give agents access to live web knowledge, ensuring responses are current and fact-aware.

UI automation platforms like Browser Use enable agents to simulate user interactions, automate repetitive tasks within browser-based environments.

Layer 6: Applications (User-facing intelligence)

This is the layer where agentic systems interact directly with end users.

Co-pilots such as GitHub Copilot enhance human workflows by making recommendations, generating content, and accelerating tasks within familiar interfaces.

Agent teammates like Tidio Lyro collaborate with users, handle delegated tasks, and manage ongoing workflows, offering more independence than co-pilots.

Layer 7: Observability & Governance (The operational backbone)

This layer provides the monitoring, evaluation, and guardrails necessary to deploy agents safely and reliably at scale.

Observability platforms such as Langfuse deliver real-time visibility into agent performance.

Reliability and safety frameworks like Lakera check that the AI’s answers follow the rules, make sure the information looks correct, and help prevent risky or harmful responses.

Deployment and operational tools extend this layer further by enabling safe, scalable adoption of agentic systems. This includes:

• Deployment pipelines to automate testing, rollout, and lifecycle management of agents.
  Examples: Kubeflow Pipelines, MLflow, Vertex AI Pipelines
  
• No-code/low-code builders for configuring and deploying agents without deep technical expertise.
  Examples: Vertex AI Builder, Beam AI
  
• Governance and policy engines to enforce organizational rules, permissions, and compliance standards.
  Examples: Immuta, Open Policy Agent (OPA)
  
• Data privacy enforcement and resource management (quotas, budgets) to ensure responsible use of compute and sensitive data.
  Examples: BigID, OneTrust
  
• Agent registries and discovery for cataloging, versioning, and tracking agent capabilities.
  Examples: Hugging Face Hub, Model Catalog in Vertex AI, Databricks Model Registry
  
• Logging and auditing for accountability, cost management, and regulatory compliance.
  Examples: Elastic Stack (ELK), Splunk, Datadog 

Current implementation challenges 

In practice, agentic AI implementation remains complex. 

Supporting true agentic capabilities, with planning, foresight, self-reactiveness, and self-reflection, requires more than isolated functionality.

Each layer must be integrated with consistent data flows, coordinated execution, and aligned governance to ensure that agents operate reliably.

Here are some of the common challenges you may face when deploying agentic AI systems:

Technical complexity increases with the addition of each layer. Effective implementation requires cross-functional teams with expertise.

Integration challenges emerge from the need to connect a wide range of systems, protocols, and data sources. However, many components within the agentic ecosystem are still evolving.

Scalability concerns arise as system usage and task complexity grow. For example, a customer support chatbot might work fine for 1,000 users but crash or slow down when 1 million people use it at once.

Governance and compliance: Companies should ensure their AI systems follow legal and ethical rules. For example, a healthcare AI must protect patient privacy (HIPAA in the U.S.),