Agent Runtime
The execution environment that runs AI agents, manages their state, handles tool invocations, enforces policies, and provides observability. Modern agent runtimes (Anthropic's Claude Agent SDK, LangGraph, AutoGen, OpenAI's Agents SDK, Crew AI) provide common infrastructure—prompt management, memory persistence, error handling, retry logic, audit logging, and integration with monitoring systems—so developers can focus on agent capabilities rather than infrastructure plumbing.
Written by Max Zeshut
Founder at Agentmelt · Last updated Oct 26, 2026
Example
A team building a multi-agent customer support system uses LangGraph as the agent runtime. The runtime handles the orchestration logic, persists conversation state across turns, automatically retries failed tool calls with exponential backoff, logs every step for debugging, and integrates with the team's existing observability stack—saving the team 6+ months of infrastructure work.
Frequently asked questions
- Should I build my own agent runtime or use an existing one?
- Use an existing runtime unless you have specific requirements that no available runtime addresses. The infrastructure work to build a robust runtime—state management, error recovery, observability, security—is substantial and not a competitive differentiator. Existing runtimes have hardened these capabilities through real-world use. Differentiate on agent design, prompts, and integrations, not on infrastructure.