What it is
Agent infrastructure refers to the foundational systems that make agents possible to run at all: secure execution environments (often sandboxed containers or microVMs), network egress controls, tool registries, persistent memory stores, vector databases, LLM gateways and proxies, and the runtime orchestration that schedules and recovers agent tasks. It is the agent equivalent of "cloud infrastructure" — the boring-but-essential layer the rest of the agent stack depends on. Vendors in this space include Daytona and E2B (sandboxed runtimes), Helicone and Portkey (LLM gateways), Mem0 and Letta (memory), and the agent runtime layers inside Anthropic Console, OpenAI AgentKit, and Salesforce Agentforce.
Why it matters
Without proper agent infrastructure, every team building agents reinvents the same wheel: how to sandbox tool execution, how to enforce egress policy, how to handle long-running jobs that exceed serverless timeouts, how to persist memory across sessions. With it, agent developers focus on the skill and the workflow instead of the plumbing. Agent infrastructure is the bedrock layer; agent operations is the management layer above it. The distinction matters because they are bought by different buyers — infrastructure decisions are made by platform engineering, operations decisions by the leaders running the agents in production.
Key components
- Sandboxed runtimes — secure execution environments for agent code and tool calls
- LLM gateways and proxies — unified entry points to multiple LLM providers
- Memory stores — persistent context across sessions, often vector-indexed
- Tool registries — discoverable, governed catalogs of agent-callable functions
- Orchestration plumbing — task scheduling, retry, timeout, and recovery mechanics
Related terms
MCP (Model Context Protocol)
Anthropic's open standard for connecting AI models to external data sources and tools. Think of it as a universal adapter for AI.
Agent Orchestration
The coordination and management of multiple AI agents working together to accomplish complex workflows that no single agent could handle alone.
Agent Operations
The discipline of running AI agents in production — capturing what they do, attributing what it costs, evaluating what they produce, and intervening when something goes wrong. The operational layer above agent observability and orchestration.
LLM Gateway
A unified proxy in front of multiple LLM providers that captures every call, enforces policy, and lets a single application talk to Anthropic, OpenAI, xAI, Gemini, and local models through one interface.