Ask ten different engineers what’s in their AI agent stack and you’ll get ten different answers — often with the same tools stacked in completely different orders for completely different reasons. O’Reilly Radar’s Paolo Perrone has done something genuinely useful with the 2026 edition of “The AI Agents Stack”: he’s cut through the noise with a six-layer framework that maps every component from raw LLM inference to production-ready deployment.
Published June 8, 2026, the piece is aimed squarely at engineers who are past the demo stage and wrestling with what it actually takes to run agents reliably. The six-layer framework is both a diagnostic tool and an architectural guide — and it comes with an unusually blunt warning: most teams over-engineer early, and the biggest source of demo-to-production failure isn’t picking the wrong model. It’s adding complexity you don’t yet need.
Here’s a breakdown of all six layers, what’s changed since 2024, and how to use this framework when you’re making real stack decisions.
The Six Layers (Bottom to Top)
Perrone organizes the stack from most stable to least mature — a useful ordering that tells you where to expect reliability and where to expect churn.
Layer 1: Models & Inference
How you run the underlying model. This includes calling commercial APIs (OpenAI, Anthropic, Google), using managed open-weight providers, or self-hosting models like Llama, DeepSeek, or Qwen.
What changed in 2026: Reasoning models (o1/o3-style) have shifted the defaults. They can handle more autonomous single-call behavior because they do more implicit planning within a single inference. Meanwhile, strong open-weight models have made the “prototype on closed, deploy on open” pattern increasingly viable — you get the simplicity of GPT-4 or Claude during development and the cost control of open-weight at production scale.
Demo-to-production gap: Low. Cloud APIs are mature, and the patterns for calling them are well understood.
Lock-in risk: Moderate. Switching models often requires prompt adjustments, but the API interfaces have converged enough that migration is tractable.
Layer 2: Protocols & Tools
How your agent calls external tools, APIs, and other agents. This is now dominated by MCP (Model Context Protocol), which was donated to the Linux Foundation and has been adopted by major model providers as the standard for tool use.
Before MCP, each framework defined its own schema for tool definitions. You’d write a tool for LangGraph in LangGraph’s format, rewrite it for CrewAI in CrewAI’s format, and so on. MCP standardizes this: write the tool once, expose it as an MCP server, and any MCP-compatible framework can call it.
What changed in 2026: MCP has effectively won. Browser automation tools and emerging agent-to-agent protocols (ACP, A2A) are building on top of it or alongside it, but MCP is the stable center of this layer.
Demo-to-production gap: Low to medium. The main open issue is security and governance — an agent with broad tool access and a subtle prompt injection vulnerability can cause real damage. This is an active area of tooling development.
Layer 3: Memory & Knowledge
How your agent stores and retrieves information. The 2026 framework identifies three sub-modes:
- In-context state/memory blocks: information held in the model’s context window, reset each session
- Vector search / RAG: retrieval-augmented generation using pgvector, Pinecone, or similar
- Persistent cross-session memory: genuine long-term memory that accumulates across sessions
Perrone makes a point that “context engineering” has largely replaced “prompt engineering” as the relevant skill at this layer. The prompt matters less than what’s in the context window and how it was assembled.
What changed in 2026: Memory has become a first-class architectural concern rather than an afterthought. In 2024, most agent demos used in-context memory only; production agents in 2026 need persistent, retrievable memory to function across multi-session or multi-agent workflows.
Demo-to-production gap: High — the highest in the stack. Getting memory right for production workloads (retrieval quality, staleness, context window budget management) is legitimately hard.
Perrone’s advice: Start with Postgres and structured prompts. Add vector search only when you have a specific retrieval problem you can’t solve with structured queries.
Layer 4: Frameworks & SDKs
How you wire model calls, tools, memory, and control flow together. This is the most crowded layer in the stack, and also where Perrone is most direct about the tradeoffs:
Provider-native SDKs (OpenAI Agents SDK, Google ADK, Microsoft AutoGen/Semantic Kernel, Hugging Face smolagents): Fast to get started, but highest vendor lock-in. Migrating between them often means essentially rewriting your agent.
Graph-based frameworks led by LangGraph (v1.0 shipped in 2025, now in production at companies like Uber and JPMorgan): Explicit state management, portable abstractions, more setup overhead. LangGraph is the leading production orchestration engine by adoption.
CrewAI: Particularly strong for multi-agent, role-based orchestration patterns. Often compared with LangGraph for team-of-agents use cases.
Build your own: Thin wrappers over provider APIs plus MCP tools. Works well for simple single-agent cases and gives you maximum flexibility, but you end up rebuilding framework functionality as your agents grow more complex.
Demo-to-production gap: Medium. The frameworks are maturing, but the right choice depends heavily on your state management needs and lock-in tolerance.
Lock-in warning: Perrone explicitly calls this out as the highest-lock-in layer in the stack. Rewriting a LangGraph agent for CrewAI (or vice versa) is not a refactor — it’s a rewrite. Choose deliberately.
Layer 5: Evaluation (Eval)
How you assess whether your agent is actually doing what you want. This layer was barely present in 2024 stack diagrams; it’s now a distinct first-class category.
Evaluation for agents is harder than for traditional software because agent behavior is non-deterministic and the outputs are often long-form or multi-step. The emerging patterns include: LLM-as-judge (using another model to evaluate outputs), test suites as objective oracles (the success criterion for Dynamic Workflows), and human-in-the-loop sampling for high-stakes domains.
Demo-to-production gap: High. Most teams underinvest here and discover the gap when something goes wrong in production that they would have caught with better evals.
Layer 6: Guardrails
Real-time constraints on agent behavior: safety filters, compliance checks, budget limits, action blocklists, and output validators. This is the least mature layer in the stack and has the largest gap between what teams need and what’s currently available.
For agentic AI specifically, guardrails need to operate at the tool-call level (can this agent take this action?) and the output level (is this response safe to return?), not just the input level (is this prompt allowed?). That’s a significantly harder problem than content filtering for chat interfaces.
Demo-to-production gap: Highest in the stack. Production agents operating autonomously in consequential domains (finance, healthcare, legal) require guardrail sophistication that most teams are still building from scratch.
How to Use This Framework
Perrone’s key advice is explicitly anti-complexity: evaluate each layer against three questions before adding anything to it.
- State management needs: Does this layer require tracking state across turns, sessions, or agents? Higher state complexity justifies more sophisticated tooling.
- Vendor lock-in tolerance: How much are you willing to pay for portability? Start with less lock-in tolerance than you think you need.
- Production readiness: Has this specific combination of tools been validated in production, or is it demo-grade? The gap between demo and production widens rapidly as you add layers.
The canonical mistake Perrone identifies: teams add LangGraph + vector memory + a full guardrails stack on day one because it’s what they’ve seen in architecture diagrams, then spend months debugging complexity that didn’t serve any actual user need.
Start with Layer 1 (a model API) and Layer 2 (MCP tools). Add each subsequent layer only when you have a specific production problem it solves. The agents that get to production fastest are usually the ones that stayed simple longest.
The 2026 Edition vs. 2024
The framework updates from previous versions reflect what changed in AI infrastructure over eighteen months:
- MCP standardized Layer 2, dramatically simplifying tool integration across frameworks
- Reasoning models changed the calculus at Layer 1, enabling more autonomous single-call behaviors
- Memory became a first-class concern at Layer 3, not an afterthought
- Evaluation (Layer 5) was added as a new distinct category — it didn’t exist as a recognized layer in 2024 diagrams
- The frameworks layer consolidated around LangGraph and CrewAI as the leading production-grade options
For anyone who built their mental model of AI agent stacks from 2024 resources, the 2026 edition is a meaningful update. The shape of the problem is the same; the answers have gotten more specific.
Sources
Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260608-2000
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