Every agent starts as a demo. You prototype something, it works on your laptop, you show it to stakeholders, they are impressed. Then comes the question that changes everything: can we ship this? The gap between demo and production is not incremental improvement - it is a different category of work. Understanding what actually changes between these phases helps you plan realistically and avoid the surprises that derail agent projects.
Why the Gap Exists
Demo agents work under controlled conditions. You run them yourself, watching the output, ready to restart if something goes wrong. They handle the happy path - the scenario you designed them for. They run for seconds or minutes, not hours. They serve one user at a time.
Production agents work under uncontrolled conditions. They run unattended, handling whatever users throw at them. They face edge cases you never anticipated. They run indefinitely, sometimes for extended periods. They serve many users simultaneously.
The code that powers the agent logic might be identical. What differs is everything around it - the infrastructure that makes execution reliable, visible, and secure.
Teams often underestimate this gap because the demo worked. If the agent can complete tasks under supervision, surely it can complete them without supervision? The answer is no - not without additional work on the surrounding infrastructure.
What Changes: Failure Handling
Demo agents can fail gracefully because you are there to catch them. Production agents must handle failures automatically.
Network connections drop. APIs timeout. External services go down. Rate limits hit. Tokens expire. Any of these can happen during any agent run. In a demo, you restart. In production, you need automated recovery.
Retry with backoff handles transient failures. A request that fails once often succeeds on retry. Exponential backoff prevents hammering failing services while giving them time to recover.
Checkpoint and resume handles longer interruptions. If a connection drops mid-task, the agent should be able to pick up where it left off rather than starting over.
Graceful degradation handles unavailable dependencies. If a non-critical tool is down, the agent should continue with reduced capability rather than failing entirely.
Timeout handling prevents indefinite waits. Operations that take too long should fail cleanly so the agent can try alternatives.
Building this resilience is infrastructure work that does not exist in most demo implementations.
What Changes: State Persistence
Demo agents keep state in memory. Production agents must persist state across restarts, deployments, and failures.
Conversation history must survive if the server restarts. Agent memory must persist across sessions. Progress on long-running tasks must not be lost to transient failures.
This requires:
Durable storage for conversation and task state. The choice of storage affects performance, reliability, and cost.
Serialization logic to convert agent state into storable form and back. Not all state types serialize cleanly.
Consistency guarantees so state updates are not lost or duplicated during failures.
Cleanup mechanisms for abandoned sessions and expired state.
The complexity here is often underestimated. Getting state persistence right is a substantial engineering project.
What Changes: Observability
Demo debugging is interactive. You watch the output, add print statements, and restart. Production debugging requires visibility into past events you did not witness.
When something goes wrong in production, you need to reconstruct what happened. Why did the agent make that decision? What information did it have? What tools did it call and what did they return?
This requires:
Comprehensive logging of agent decisions, tool calls, and state changes. Not just final outputs, but the entire reasoning chain.
Structured traces that can be queried and analyzed. Raw logs are hard to navigate; structured traces enable efficient debugging.
Correlation across events so related activities can be traced together.
Retention so historical events remain queryable when you need them.
Adding observability after problems occur means the events you need were not captured. Build observability before production deployment.
What Changes: Security
Demo agents use your credentials and run on your machine. Production agents need proper credential management and access control.
Credential storage requires encryption and access control. API keys, OAuth tokens, and other secrets must not be exposed.
Per-user authentication handles agents acting on behalf of different users. Each user's credentials must be isolated.
Token refresh keeps credentials valid over time. Expired tokens must be refreshed automatically.
Access control limits what agents can do and what data they can access. Not every agent should have access to everything.
Audit logging records what actions were taken, by which agents, on whose behalf.
Security done wrong creates liability. Security done right is substantial infrastructure work.
What Changes: Cost Control
Demo agents run occasionally under supervision. Production agents run continuously at scale, and costs can escalate quickly.
Token tracking monitors model usage per conversation and in aggregate. Unexplained cost spikes indicate problems.
Loop detection catches agents that get stuck repeating actions. Infinite loops burn unlimited tokens.
Appropriate model selection matches model capability to task requirements. Not every task needs the largest model.
Resource limits prevent runaway costs from individual requests or users.
Alerting notifies operators when costs exceed expected bounds.
Cost control is not just about money - unexplained cost spikes often indicate behavioral problems that affect users too.
What Changes: Human Oversight
Demo agents run with implicit trust - you are watching and can intervene. Production agents need explicit oversight mechanisms for consequential actions.
Approval gates require human confirmation for sensitive operations. Sending emails, modifying data, making purchases - these should not happen without review.
Audit trails record who approved what and when. Accountability requires documentation.
Escalation paths handle situations the agent cannot resolve alone. Some decisions should always involve humans.
Human oversight is not about distrusting agents - it is about maintaining appropriate control over real-world actions.
The Production Readiness Checklist
Before deploying an agent to production, verify:
Reliability: The agent handles failures gracefully. Retries work. State persists. Long tasks survive interruptions. Timeouts prevent hangs.
Observability: You can trace any issue. Tool calls are logged with inputs and outputs. Agent reasoning is visible. Performance data is captured.
Security: Credentials are properly managed. Per-user auth works. Access control is enforced. Audit trails are complete.
Cost control: Token usage is tracked. Loop detection is in place. Resource limits are configured. Alerting works.
User experience: Streaming shows progress. Errors are user-friendly. Long tasks provide status. Recovery from failures is smooth.
This checklist represents substantial work. Skipping items means accepting gaps that will surface as incidents.
The Path Forward
Two paths exist for closing the demo-to-production gap.
Build it yourself: Implement state persistence, failure handling, observability, security, and cost control using general infrastructure. This takes months of engineering time and requires ongoing maintenance.
Use a runtime: Adopt a platform that provides these capabilities built-in. This trades some customization for dramatically faster time to production and reduced operational burden.
Most teams find the second path more practical. The infrastructure required for production agents is well-understood and does not differentiate most products. Building commodity infrastructure consumes time that could be spent on unique value.
For teams ready to move agents from demo to production, inference.sh provides the runtime layer that handles operational concerns. State persistence, failure recovery, observability, credential management, and cost controls are built in. You bring the agent logic; the runtime handles production readiness.
The gap between demo and production is real and substantial. Recognizing it early, planning for it explicitly, and choosing your path through it deliberately makes the difference between successful agent deployments and projects that stall at the demo stage.
FAQ
How long should I expect the demo-to-production transition to take?
If building infrastructure yourself, expect two to three months for a reasonably complete implementation. This includes state persistence, failure handling, basic observability, credential management, and deployment infrastructure. Add time for testing, iteration, and unexpected complications. Ongoing maintenance adds three to four weeks annually. Using a runtime platform reduces this to days or weeks depending on how much customization you need. The difference is substantial enough that timeline should influence your build-versus-use decision.
What are the most common issues teams encounter going to production?
State persistence problems are extremely common - state that seemed to work in testing fails under real failure modes. Authentication edge cases cause issues when tokens expire in unexpected situations. Cost control becomes urgent when real usage reveals patterns different from testing. Observability gaps appear during the first real incidents when you discover you cannot diagnose problems. Each of these is addressable, but teams consistently underestimate how much work they represent. Building in production mode from early development - with persistence, auth, and logging even during prototyping - reduces surprise.
Can I incrementally move from demo to production rather than all at once?
Yes, and this is often the right approach. Start by adding observability - the ability to see what is happening is valuable at every stage and helps diagnose other issues. Then add state persistence for critical state. Then add failure handling for common failure modes. Then add security controls. Each increment moves toward production readiness while delivering value. The danger is stopping partway and declaring something production-ready when it is not. Define what production means for your use case and track progress against that definition.