A demo agent takes an afternoon to build. A production agent takes months of infrastructure work. The runtime layer handles durability, observability, and tool orchestration so you can focus on agent logic. Learn more about the inference.sh runtime →
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, and understanding this distinction will save you from rebuilding the same wheel that hundreds of teams have already struggled with.
The Distance Between Demo and Production
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. You record a demo, post it online, and start planning the launch.
Then you try to deploy it.
The first user closes their browser midway through a task that was taking longer than expected. 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. A tool returns an unexpected error format, and your parsing logic throws an exception that bubbles up to the user as a cryptic message.
None of these problems existed in your demo environment. The demo ran on your machine, with you watching, for 30 seconds at a time, with no real users doing unexpected things. Production is a different category of problem entirely.
The agent logic itself - the prompts, the tool definitions, the reasoning patterns - that part was actually the easy bit. What you are missing is the execution infrastructure that makes agents work reliably, repeatedly, and at scale. This infrastructure layer is what we call an agent runtime.
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.
The distinction matters because 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. Message queues assume independent jobs without complex state dependencies. Agent workloads fit none of these patterns well, which is why teams building production agents end up constructing custom infrastructure that addresses these specific needs.
The Infrastructure You Will Eventually Build
If you deploy agents using a framework and general infrastructure, you will inevitably end up building the following 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. This means designing a state schema, choosing a storage backend, handling serialization, and managing cleanup of abandoned sessions.
Retry and recovery logic becomes essential when you realize that tool calls fail more often than you expected. APIs timeout, rate limits hit, authentication expires. You need exponential backoff, circuit breakers, fallback strategies, and careful handling of partially completed operations.
Observability tooling becomes urgent after your first production incident where you cannot figure out why the agent made a particular decision. You need to trace the entire reasoning chain, see what information the agent had at each decision point, and correlate tool inputs with outputs. This typically means integrating a separate tracing product and instrumenting every step of your agent loop.
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. Most teams underestimate this work significantly.
Execution infrastructure needs to handle agents that might run for minutes or hours without blocking other requests. Serverless functions timeout too quickly. Long-running processes waste money sitting idle between LLM calls. You need something that can start and stop efficiently while maintaining state across interruptions.
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.
inference.sh handles all of this. Durable execution, managed auth, and built-in observability are part of the runtime layer. You write agent logic; the platform handles operations. See how it works →
Why Frameworks Alone Fall Short
Agent frameworks provide valuable abstractions for defining agent logic. They give you clean interfaces for tool definitions, patterns for structuring multi-step reasoning, and integrations with various model providers. This is genuinely useful work that simplifies agent development.
However, frameworks operate at the application layer. They help you express what your agent should do, but deploying that logic reliably remains your responsibility. The framework does not know where your code runs, how to persist its state, or what to do when things fail.
This is not a criticism of frameworks - it reflects their design scope. A framework is a library. A runtime is a platform. They solve different problems and often work together. The question is how much infrastructure you want to own and maintain versus how much you want handled for you.
The challenge becomes acute when you realize that the infrastructure layer is not a one-time build. Security patches, scaling adjustments, integration updates, and bug fixes require ongoing attention. Every team that builds this infrastructure spends engineering cycles maintaining it instead of improving their agents.
The Runtime Approach to Agent Execution
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. Long-running tasks survive interruptions automatically.
Managed authentication means OAuth flows, token storage, refresh handling, and credential injection are handled by the platform. You connect integrations once in workspace settings, 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 or paying for a separate observability product.
Pay-per-execution pricing means you pay for agent work, not idle time. Traditional deployment costs money whether agents are active or waiting. A runtime that charges per execution aligns cost with actual usage, which matters significantly for bursty agent workloads.
This approach trades some flexibility for reliability and development speed. You work within the runtime's model rather than building everything custom. For most teams, this trade is favorable because their differentiation lies in agent capabilities, not infrastructure.
When Each Approach Makes Sense
Custom infrastructure makes sense when your requirements are genuinely unusual, when you have a platform team with spare capacity, or when you need maximum control over every aspect of execution. Some organizations have regulatory constraints that require self-hosted infrastructure. Others have existing investments in execution platforms that can be adapted for agent workloads.
A managed runtime makes sense when your goal is shipping agent features rather than building infrastructure, when your team is small or focused on product development, when you want proven solutions for reliability and observability, and when predictable costs matter more than theoretical maximum flexibility.
The honest assessment is that most teams building agents do not have unusual requirements. They need the same core capabilities: durable state, retry logic, observability, auth management, and efficient execution. Building these from scratch provides little competitive advantage while consuming significant engineering time.
Making the Transition
If you have existing agents built on a framework, the mental model shift is straightforward. Instead of thinking about where to deploy your agent code and how to keep it running, you configure the agent and let the runtime handle execution.
You still control which models power your agents, what tools they can access, how they behave through system prompts, which actions require approval, and how sub-agents are structured. You stop managing execution infrastructure, state storage, authentication lifecycles, retry logic, and observability pipelines.
The code change is often simple. Instead of instantiating a framework executor and running it on your infrastructure, you instantiate an agent object and send messages through the runtime's API. The tools you defined still work. The prompts you wrote still apply. The business logic remains yours.
What disappears is the operational burden - the late-night pages about failed jobs, the debugging sessions where you cannot reproduce issues, the scaling problems during traffic spikes, and the security reviews for credential handling.
Looking Ahead
Agent capabilities are advancing rapidly. Models get smarter, tool calling gets more reliable, and multi-step reasoning improves month over month. The teams that benefit most from these advances are those who can iterate quickly on agent behavior without being blocked by infrastructure limitations.
If your engineers spend half their time maintaining agent infrastructure instead of improving agent capabilities, you are moving at half speed in a market that is accelerating. The infrastructure work is necessary, but it does not have to be your work.
The choice between building and using a runtime is ultimately a question of where you want to invest your engineering effort. Do you want to become experts in durable execution, auth management, and distributed tracing? Or would you rather focus on the agents themselves - the prompts, tools, and behaviors that create value for your users?
For teams that want to explore the runtime approach, inference.sh provides a complete agent execution environment with durable state, managed integrations, and built-in observability. You can start building agents today and reach production without the infrastructure detour.
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.
FAQ
What is the difference between an agent framework and an agent runtime?
A framework is a library that provides abstractions for defining agent logic - tool interfaces, reasoning patterns, and model integrations. You import it into your code and deploy wherever you choose. A runtime is execution infrastructure that runs your agent code with built-in capabilities for state persistence, failure recovery, observability, and scaling. Frameworks help you write agents; runtimes help you run them reliably. Many production deployments use both: a framework for expressing agent logic and a runtime for handling operational concerns.
Can I use my existing agent code with a runtime?
Yes, in most cases. The core agent logic - system prompts, tool definitions, and reasoning patterns - typically transfers directly. What changes is the execution model. Instead of running the agent loop yourself and managing all the infrastructure, you configure an agent in the runtime and interact through its API. Your tool implementations may need minor adaptation to match the runtime's interface, but the business logic and prompts remain yours. The transition is usually straightforward because runtimes are designed to accept standard agent patterns.
When should I build my own agent infrastructure instead of using a runtime?
Build custom infrastructure when you have unusual requirements that no runtime satisfies, when regulatory constraints require complete self-hosted control, or when you have an established platform team that can maintain the infrastructure long-term. Consider the full cost: initial development (typically several months), ongoing maintenance (security patches, scaling, updates), and opportunity cost (engineers working on infrastructure instead of agent features). For most teams, the standard requirements of state persistence, auth management, and observability are not differentiating, making a managed runtime the more efficient choice.