the gap between demo and production

Building an AI agent that works in a demo takes an afternoon. Building one that survives production takes months of infrastructure work. The gap between these two realities is where agent runtimes come in.

When you first build an agent, everything feels straightforward. You pick a large language model, define a few tools, write a prompt, and watch as the agent reasons through problems on your laptop. The excitement is real. Your agent searches the web, summarizes documents, and answers questions with remarkable intelligence.

Then you try to deploy it.

The first user closes their browser midway through a task. Your agent's state vanishes. Another user hits your API rate limits during peak hours, and the agent crashes without any way to resume. Someone asks a question that sends the agent into an infinite loop, burning through tokens until you manually kill the process.

None of these problems existed in your demo environment. Production is a different category of problem entirely.

what an agent runtime actually does

Think of a runtime as the environment that executes your agent code with specific guarantees about durability, visibility, and scaling. It handles the operational concerns so you can focus on what your agent should do, not how to keep it running.

A framework gives you building blocks. You import a library, define your agent logic, and figure out where to deploy it yourself. A runtime is infrastructure that executes your agent with built-in solutions for the hard operational problems.

Agent workloads have unique characteristics that general-purpose infrastructure handles poorly:

• Agents make long sequences of decisions punctuated by external calls
• They need to maintain conversation context across many interactions
• They call tools that might fail, timeout, or return unexpected results
• They run for unpredictable durations; sometimes seconds, sometimes hours
• They need human oversight for sensitive actions

Standard web frameworks assume request-response cycles measured in milliseconds. Container orchestration assumes stateless workloads that can be killed and restarted freely. Agent workloads fit none of these patterns well.

the infrastructure you will eventually build

If you deploy agents using a framework and general infrastructure, you will inevitably end up building these components yourself:

state persistence becomes necessary the first time a long-running task fails. You need somewhere to store conversation history, intermediate results, and agent memory so that failures do not mean starting over.

retry and recovery logic becomes essential when you realize that tool calls fail more often than expected. APIs timeout, rate limits hit, authentication expires.

observability tooling becomes urgent after your first production incident where you cannot figure out why the agent made a particular decision.

authentication management becomes a project unto itself when your agent needs to act on behalf of users. OAuth flows, token storage, refresh handling, and per-user credential isolation each require careful implementation.

Each of these components is a substantial engineering effort. Together, they represent months of work before you can focus on improving your actual agent capabilities.

the runtime approach

An agent runtime provides the execution infrastructure as a managed service. You write agent logic, configure tools, and deploy to the runtime. The operational concerns are handled by the platform.

durable execution means your agent's state checkpoints after each step. If a connection drops, a process restarts, or a tool times out, execution resumes from the last checkpoint instead of starting over.

managed authentication means OAuth flows, token storage, refresh handling, and credential injection are handled by the platform. You connect integrations once, and your agents can act on behalf of users without custom auth code.

built-in observability means every decision, tool call, and state change is captured automatically. You can trace any user issue back through the complete reasoning chain without adding instrumentation code.

pay-per-execution pricing means you pay for agent work, not idle time. Traditional deployment costs money whether agents are active or waiting.

start building

The path from demo to production does not have to take months. With the right runtime foundation, you can focus on what makes your agents valuable rather than what makes them merely operational.

start building with inference.sh →