Shipping Agents to Production
From prototype to product
Architect, deploy, monitor, and scale agents for real users — and turn what you build into a product people pay for.
- Lessons
- 5
- Reading time
- 52 min
- Level
- Advanced
- Access
- Full course
Lessons in this module
- 10.111 min
Architecture for Production Agents
Queues, durable execution, state, and sessions: the architecture that keeps long-running agents reliable.
- 10.210 min
Controlling Cost and Latency
Prompt caching, model routing, batching, and budgets — the levers that cut agent costs by an order of magnitude.
- 10.313 min
Deploying Your Agent
Ship your agent as an API and a web app: environment config, secrets, background jobs, and a deploy you can repeat.
- 10.49 min
Monitoring and Improving in Production
Dashboards, alerts, feedback loops, and turning production failures into new evals.
- 10.59 min
Turning Agents into Products
Find problems worth solving, price agent products, and design UX that earns user trust.
About this module
Running an agent in production is different from running a demo. Agent tasks can take minutes rather than milliseconds, call slow external tools, and fail partway through. Costs vary from task to task, users need to see progress, and a crashed server shouldn't mean a lost or duplicated job. These are architecture problems, and they have well-understood solutions borrowed from conventional backend engineering.
A typical production setup separates an API that accepts work from background workers that run the agent, with a job queue between them. Each step is checkpointed so a run can resume after a crash, tools are designed to be safe to run twice, and session state lives outside any single process. Around that core sit cost controls, monitoring, and a feedback loop that turns real failures into improvements.
This part covers that path from prototype to product. You will build the core architecture in TypeScript with job queues, durable execution, idempotent tools, sessions, streaming progress, and kill switches. You will then cut cost and latency with prompt caching, model routing, batch APIs, and hard budgets, measured against a baseline so you know the savings are real.
Next you will deploy an agent as an API service and a worker from one codebase, with validated configuration, a single Dockerfile, Docker Compose for local runs, and a repeatable CI pipeline. You will set up monitoring with dashboards, alerts, and user feedback, and turn production failures into new eval cases. The final lesson covers turning agents into products: finding problems worth solving, validating demand, pricing around variable costs, and designing UX that earns trust.
Questions about Shipping Agents to Production
How do you deploy an AI agent to production?
Run the agent in a background worker fed by a job queue, with a separate API that accepts requests and reports progress. Package both as containers, keep secrets in environment variables or a secret manager, and deploy through a CI pipeline so every release is repeatable.
How do you reduce the cost of running AI agents?
The biggest levers are prompt caching for repeated input, routing simpler steps to cheaper models, batch APIs for work that can wait, and hard token or spending budgets enforced outside the model. Measure cost per completed task before and after each change.
What is durable execution for AI agents?
Durable execution means saving the state of a run after each step so it can resume where it left off if a process crashes or restarts. For long-running agents, it prevents lost work and avoids repeating actions that already happened.
How do you monitor an AI agent in production?
Track system health such as errors and latency, cost per run, task outcomes, and user feedback. Record a summary event and a trace for every run, alert on real problems such as spikes in cost or failure rate, and feed failed cases back into your eval set.
How should you price an AI agent product?
Because each task can cost a different amount to run, pricing needs to account for variable usage, for example through usage limits, tiers, or pricing per unit of work or outcome. Estimate your cost per task before setting prices so heavy users don't make the product unprofitable.