Agent Infra: curated resources for production AI agent infrastructure

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A curated collection of infrastructure resources for building production AI agents: runtimes, workspaces, sandboxes, tool protocols, context systems, security, observability, and evaluation.

Last reviewed: 2026-07

This list focuses on the systems layer below agent products and above foundation models.

It is not a general "awesome AI agents" list. It is focused on the infrastructure needed to run agents safely and reliably in production.

Organized around one question, asked of every component: what state is this, and where does it belong?

Contents

Scope

Core Concepts

Foundational Articles

Runtime and Control Plane

Tool Protocols and Context Interfaces

Workspace and Runtime State

Sandbox and Execution

Security, Policy, and Governance

Observability and Evaluation

Coding-Agent Workflows

Data Layer and Storage

Books

Videos and Talks

Related Awesome Lists

Scope

Included:

Agent runtimes and control planes

Durable execution

Workspace and runtime state

Sandbox and execution environments

Tool protocols such as MCP

Context files and repo-level guidance

Security, permissions, and policy

Observability, tracing, and evaluation

Coding-agent workflows

Relevant systems books and videos

Not included:

Prompt collections

General AI app directories

Chatbot UI tools

Generic RAG tools unless they are directly relevant to agent infrastructure

End-user agent products without clear infrastructure lessons

Core Concepts

Concept Meaning

Agent runtime The execution layer that runs agent loops, manages state, retries, tools, and human interaction.

Harness The model-facing loop and scaffolding that turns model outputs into actions.

Workspace The durable working state an agent reads and writes during a task. Not just a folder.

Sandbox The isolated compute environment where actions execute.

Session / run The ordered record of an agent execution attempt.

Tool protocol The interface by which agents discover and call external tools or resources.

Policy Runtime constraints over tools, data, paths, permissions, and publish actions.

Publish lifecycle The process of promoting candidate agent output into source-of-truth systems.

Observability Traces, logs, metrics, costs, evals, and replay data for agent behavior.

Storage concern The persistence needs around agent metadata, conversations, run events, workspaces, artifacts, memory, and traces.

Foundational Articles

Resource Layer Why it matters

Anthropic: Building Effective Agents Runtime Practical guide arguing for simple composable agent patterns instead of over-engineered abstractions.

Anthropic: Effective Context Engineering for AI Agents Context / Runtime Good framing for context as runtime design rather than prompt stuffing.

Anthropic: Writing Effective Tools for AI Agents Tooling Useful for designing tool APIs agents can actually use.

Martin Fowler: Context Engineering for Coding Agents Coding agents / Context Strong developer-facing explanation of workspace files and repo context for coding agents.

Temporal: What Is Durable Execution? Durable execution Core concept for long-running, crash-resilient workflows.

DBOS: Durable Execution for Crashproof AI Agents Durable execution Applies durable execution ideas directly to AI agents.

GitHub Blog: A practical guide on how to use the GitHub MCP server Tool protocol / GitHub workflow Practical reference for MCP + GitHub integration.

Anthropic: Building with Claude Managed Agents Runtime Defines the brain/hand/session split, vault-brokered credentials, and session-derived memory in a first-party production agent runtime note.

OpenAI: Sandbox Agents Runtime / sandbox Draws the harness/control-plane vs. compute/sandbox-plane line, with credentials treated as runtime configuration rather than prompt content.

OpenAI: The next evolution of the Agents SDK Runtime / SDK Official launch context for Sandbox Agents and the Agents SDK direction.

Vercel: Agentic Infrastructure Runtime Reports that over 30% of Vercel deployments are agent-triggered and maps sandboxes, workflows, gateway, and observability into shipped infrastructure.

HumanLayer: 12-Factor Agents Runtime The stateless-reducer framing: agent execution as a function over an append-only event log.

Manus: Context Engineering for AI Agents Context / workspace Production note arguing for filesystem-backed context and stable append-only traces.

Microsoft Agent Framework at Build 2026 Runtime / hosted agents First-party description of Agent Harness, hosted agents, per-session VM isolation, filesystem persistence, and OpenTelemetry.

OpenRath: Session-Centered Runtime State for Agent Systems Runtime state Makes the case for session as a first-class runtime value rather than incidental chat history.

Runtime and Control Plane

Projects

Project Type Why it matters

LangGraph Runtime framework Stateful graph runtime for long-running agents, persistence, human-in-the-loop, and durable execution.

OpenAI Agents SDK Python SDK Lightweight SDK for building agentic apps. Useful baseline for minimal abstractions.

OpenAI Agents SDK TypeScript SDK TypeScript version, useful for JS/TS infrastructure builders.

Omnigent Meta-harness / control plane Common orchestration layer over coding agents and custom agents, with policies, sandboxing, shared sessions, and multi-device collaboration.

LiteLLM Agent Platform Agent platform Self-hostable control plane for calling and managing coding agents with per-session isolation and shared proxy infrastructure.

LiteLLM Agent Runtime Runtime Coding-agent daemon designed to run inside per-session VMs, with customization layered through repo, team, and session config.

Microsoft Foundry Agent Service Hosted runtime Framework-agnostic hosted agent service with session isolation, durable state, tracing, evaluation, and production deployment hooks.

OpenHands Agent platform Full open-source software-agent platform with sandboxed execution and developer workflows.

OpenHands Software Agent SDK SDK Composable SDK for building software-development agents.

SWE-agent Coding-agent scaffold Simple and influential coding-agent scaffold for GitHub issue solving.

mini-SWE-agent Minimal agent Small baseline for understanding the minimal loop needed to solve coding tasks.

Goose Local agent runtime General-purpose open-source agent with CLI, desktop, API, and MCP extension ecosystem.

Semantic Kernel Enterprise SDK Enterprise-oriented orchestration and agent framework from Microsoft.

CrewAI Multi-agent framework Popular multi-agent orchestration framework.

AutoGen Multi-agent framework Influential multi-agent framework. Useful historically and architecturally.

Letta Stateful agents / memory Platform for stateful agents with advanced memory. Useful for understanding memory-first agent design.

Letta Code Coding agent / memory Memory-first coding agent. Useful contrast with workspace-first designs.

Papers

Paper Why it matters

Infrastructure for AI Agents Defines agent infrastructure as external technical systems and protocols that mediate agent interaction.

AI Runtime Infrastructure Proposes runtime infrastructure between model and application. Useful for control-plane framing.

OpenHands: An Open Platform for AI Software Developers as Generalist Agents Important system paper for software agents.

The OpenHands Software Agent SDK Architecture of a composable SDK for production software agents.

Agents Learn Their Runtime Shows that runtime persistence semantics affect agent behavior.

Tool Protocols and Context Interfaces

MCP

Resource Type Why it matters

Model Context Protocol official docs Docs Main entry point for MCP.

MCP specification Spec Protocol details.

MCP TypeScript SDK SDK Official TypeScript SDK.

MCP servers repository Repo Reference and community MCP server directory.

GitHub MCP Server MCP server Important integration for coding-agent workflows.

MCP security best practices Security docs Required reading for secure MCP host/server design.

MCP Safety Audit Paper Demonstrates real security failures enabled by MCP-style tool integration.

MCPSafetyScanner Tool Scanner for MCP server security issues.

NSA: Model Context Protocol Security Security guidance Government security guidance for MCP adoption.

Context and Repo Guidance

Resource Type Why it matters

AGENTS.md Open format Simple repo-level instruction format for coding agents.

AGENTS.md GitHub repo Repo Source for AGENTS.md examples and adoption.

Evaluating AGENTS.md Paper Important empirical warning: repo context files can reduce task success when overloaded.

Configuring Agentic AI Coding Tools Paper Analyzes configuration mechanisms across Claude Code, Copilot, Cursor, Gemini, and Codex.

OpenAI Codex AGENTS.md example Example Real-world AGENTS.md usage in a coding-agent repo.

Workspace and Runtime State

Resource Type Why it matters

Resilient Write paper Paper Durable write surface for coding agents. Very relevant to workspace design.

Resilient Write blog Blog More engineering-oriented explanation of durable write paths.

BranchFS Filesystem project FUSE-based copy-on-write branching workspace. Useful for fork/snapshot semantics.

TClone Paper Low-latency forking of live GUI workspaces for computer-use agents.

Git worktree docs Git docs Basic primitive for isolated local candidate workspaces.

Git apply docs Git docs Basis for patch-based publish.

Git format-patch docs Git docs Basis for exporting deltas and patch bundles.

GitHub Pull Requests docs Workflow docs Publish target for coding-agent output.

GitHub Actions docs CI docs Common validation stage for agent-produced changes.

Sandbox and Execution

The useful first question is not "which provider?", but "what boundary protects the host?". Agent sandboxes also need a state model, because long-running agents often need files, snapshots, or branches to survive beyond one process.

Isolation model Boundary Typical fit

Process policy sandbox Host process plus OS policy Local tools or helper processes with filesystem and network limits.

Shared-kernel container Namespaces, cgroups, capabilities, seccomp Dense Linux workloads where compatibility matters more than kernel separation.

Userspace-kernel container Container API with syscall mediation Stronger isolation than ordinary containers while preserving OCI-style workflows.

Language or WebAssembly runtime Engine-level isolate or capability grants Controlled plugins, edge functions, and short-lived extension code.

MicroVM Separate guest kernel per workload Untrusted Linux code, generated code, package installs, and agent execution.

VM-backed container runtime Container UX backed by lightweight VMs Kubernetes or OCI workflows that need a stronger tenant boundary.

Full VM or enclave Hypervisor VM or confidential-computing TEE Broad OS compatibility or highly sensitive secret-handling paths.

State model What persists or branches Why it matters for agents

Ephemeral runtime Nothing durable by default Good for one-shot code execution and tests.

Durable volume Files survive sandbox stop/start Useful for package caches, generated artifacts, and long tasks.

Filesystem snapshot Filesystem state can be cloned or restored Enables branch-mutate-evaluate workflows without rebuilding environments.

Memory/process snapshot Running state can resume from a checkpoint Useful for fast warm starts and long-lived interactive sessions.

Branchable backing services Databases or object state can fork cheaply Keeps agent experiments isolated beyond the filesystem.

Managed Agent Sandboxes

Resource Type Why

[truncated for AI cost control]