What is the difference between an agent harness and an agent runtime?

A harness is the complete product-specific shell around a model for a job, including prompts, tools, context strategy, and interface, such as a coding agent. A runtime is the execution layer that runs the loop, tools, state, and limits. A harness contains or depends on a runtime.

What is the difference between an agent framework and a runtime?

A framework is a library for defining agents: prompts, tools, step graphs, and memory abstractions. A runtime executes and operates those agents with sessions, persistence, scaling, security boundaries, and observability. Some frameworks include a basic runtime, while production deployments often use a separate one.

Do cloud providers offer agent runtimes?

Yes. Major cloud providers offer managed agent runtime services that host agent code, manage sessions and memory, integrate identity and tools, and provide observability. They reduce operational effort in exchange for provider dependence, while self-hosted runtimes offer more control and portability.

What Is an AI Agent Runtime?

An AI agent runtime is the execution environment that runs an agent's loop. It sends context to the model, executes requested tools, stores state and memory, manages sessions and long-running work, enforces permissions and limits, and records traces. Frameworks define agents; the runtime executes and operates them.

What a runtime does

Runs the loop. Assembles context, calls the model, parses its response, executes any requested tool calls, adds results to state, and repeats until the task ends or a limit is reached.

Executes tools. Calls functions, APIs, and MCP servers, often inside sandboxes, with timeouts and error handling.

Manages state. Keeps conversation history, task progress, and working files for each session, and persists them so work can resume.

Provides memory. Connects the agent to short-term session memory and longer-term stores such as vector indexes or knowledge bases.

Handles long and concurrent work. Runs many sessions at once, queues work, and supports tasks that wait on external jobs.

Enforces boundaries. Applies identity, permissions, network restrictions, spending limits, and approval steps for sensitive actions.

Observes. Records traces of model calls, tool calls, latency, errors, and token usage for debugging and cost control.

Scales and recovers. Retries failed calls, restarts crashed sessions from saved state, and allocates compute.

Local runtimes

Coding agents and desktop assistants include a local runtime that runs tools on your machine within configured permissions. The same responsibilities apply at small scale: which tools can run, what they can access, how sessions persist, and what is logged.

Runtime, framework, harness, and model

The model generates the next action: text, a tool call, or a decision to stop. It has no persistent state or ability to execute anything itself.

A framework is a library for defining agents: prompts, tools, graphs of steps, and memory abstractions. It shapes how you write the agent.

A runtime is where the defined agent executes, with the operational concerns above. Some frameworks include a local runtime; many teams deploy framework-defined agents onto a separate managed runtime.

A harness is the complete product-specific shell around a model for a particular job, such as a coding agent, combining system prompts, tool set, context strategy, user interface, and a runtime loop. A coding assistant is a harness; the loop executing its tool calls is its runtime.

An agent engine is a term some platforms use for their managed runtime service.

Workflow platforms run predefined sequences with AI steps. They overlap with agent runtimes, but agents choose their next steps dynamically while workflows follow designed paths.

The terms blur in marketing. The useful question is which layer handles execution, state, security, and scaling in your system.

Managed runtimes

Major cloud providers offer managed agent runtime services that host agent code, manage sessions and memory, integrate identity and tool access, and provide observability. They reduce operational work in exchange for tying agents to that provider's infrastructure. Self-hosted runtimes give more control and portability with more operational effort.

Where MCP fits

MCP standardises how tools and data sources are exposed to agents. The runtime is what connects to MCP servers, decides which tools a session may use, executes the calls, and handles their failures. MCP defines the interface; the runtime operates it.

Choosing or building one

Session and state model. How sessions are isolated, how state persists, and whether runs can resume after failure.

Tool execution and sandboxing. Where code and tools run, how they are isolated, and how credentials are handled without exposing them to the model.

Protocol support. Support for MCP and other tool or agent protocols reduces custom integration.

Long-running work. Support for asynchronous jobs, timeouts, and background tasks.

Limits and approvals. Budget controls, rate limits, and human approval steps for sensitive actions.

Observability. Traces, token and cost reporting, and replay of failed runs.

Scaling and cost. Concurrency limits, cold starts, and pricing model.

Portability. How hard it is to move agents to another runtime or model provider.

Memory and retrieval integration. How easily agents can query vector stores and knowledge bases before calling a model. Retrieval at the runtime level lets every agent reuse indexed knowledge instead of regenerating reference material on each run.

Build versus adopt

A minimal loop is easy to build. Production concerns such as sandboxing, credential isolation, resumable sessions, concurrency, and tracing are where custom runtimes take sustained effort. Adopt an existing runtime unless those concerns are central to what you build.

Scaling tool servers

As agent traffic grows, the tool and MCP servers a runtime calls often become the bottleneck before the model does. Stateless servers, bounded payloads, and a small, focused tool surface keep a runtime responsive under load.

Frequently asked questions

What is an AI agent runtime?: The execution environment that runs an agent's loop: sending context to the model, executing requested tools, managing state and memory, handling sessions and long-running tasks, enforcing permissions and limits, and recording traces. It turns an agent definition into something that operates reliably.
What is the difference between an agent harness and an agent runtime?: A harness is the complete product-specific shell around a model for a job, including prompts, tools, context strategy, and interface, such as a coding agent. A runtime is the execution layer that runs the loop, tools, state, and limits. A harness contains or depends on a runtime.
What is the difference between an agent framework and a runtime?: A framework is a library for defining agents: prompts, tools, step graphs, and memory abstractions. A runtime executes and operates those agents with sessions, persistence, scaling, security boundaries, and observability. Some frameworks include a basic runtime, while production deployments often use a separate one.
Do cloud providers offer agent runtimes?: Yes. Major cloud providers offer managed agent runtime services that host agent code, manage sessions and memory, integrate identity and tools, and provide observability. They reduce operational effort in exchange for provider dependence, while self-hosted runtimes offer more control and portability.