The Agent Stack - Part 1: A Systems Map of Modern Agent Infrastructure

2026-02-17 · Source: The Agent Stack · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

The article proposes a "systems map" for modern agent infrastructure, defining a v1 stack to clarify the components involved in turning model output into bounded action. It argues that the term "agent" has become too broad, encompassing provider APIs, workflow runtimes, memory systems, and evaluation platforms, thereby obscuring critical distinctions in state ownership, orchestration, capability exposure, and execution. The author introduces a ten-layer stack, conceptualized as a request path wrapped by trust and operator layers, with approvals for side effects, all built upon shared infrastructure. This framework aims to make complex systems easier to reason about, debug, and operate by clearly delineating responsibilities across layers like Interfaces, Control Plane, Runtime, Model Engine, Context, Tools, Execution Surfaces, Identity/Policy, Observability, and Infrastructure Substrate.

Key takeaway

For engineering leaders designing or implementing AI agent systems, you should adopt a layered stack perspective rather than relying on the ambiguous "agent" concept. Clearly defining ownership and boundaries for session management, context handling, capability exposure, and execution authority across distinct layers will improve system debuggability, operational safety, and overall architectural clarity, preventing common misdiagnoses of failures.

Key insights

Deconstructing the overloaded "agent" concept into a defined stack clarifies system responsibilities and failure points.

Principles

• Layers matter, even if products span them.
• Ownership defines system boundaries.
• Distinguish capability from execution.

Method

Map agent functionality to a ten-layer stack: Interfaces, Control Plane, Runtime, Model Engine, Context, Tools, Execution Surfaces, Identity/Policy, Observability, and Infrastructure Substrate, focusing on ownership at each layer.

In practice

• Separate transcript, context, and memory.
• Place approvals where side effects occur.
• Trace runs, then evaluate with criteria.

Topics

• Agent Stack Architecture
• Control Plane
• Context Management
• Capability Exposure
• Execution Surfaces

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Architect, AI Engineer, MLOps Engineer

Related on AIssential

• The Agent Stack - Part 2: Foundation Infrastructure, Models, and Inference — Agent system robustness depends on understanding and explicitly managing underlying infrastructure and model engine semantics.
• The Agent Stack - Part 3: Control Planes, Sessions, and State Ownership — Agent systems require a clear control plane to manage session state and ensure coherent execution across interruptions.
• The Practitioner’s Guide to AgentOps — AgentOps provides the operational rigor needed to manage autonomous AI agents, ensuring explainable, measurable, and compliant behavior.
• Most AI Agents Fail in Production Because They’re Built Backwards — Production AI agents require a bottom-up, distributed systems architecture, not a top-down, model-centric approach.
• The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE — Agentic AI expands software engineering's scope to "semi-executable artifacts" beyond traditional code.
• Decomposing the Agent Orchestration System: Lessons Learned — Agents can reliably recover from infrastructure failures if given the right context and capabilities.

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Agent Stack.