Build a production AI agent (from one API call up)
A model API is a function from text to text — it remembers nothing, does nothing and fails without saying so. Everything an agent actually does lives in the program around it: the loop, the tools, the window, the budgets, the gates, the sandbox, the run log. Build that program, one mechanism at a time, until it can resolve a support ticket and issue a refund exactly once.Enter to send · Shift+Enter for a new line
About Build a production AI agent (from one API call up)
A model API is a function from text to text — it remembers nothing, does nothing and fails without saying so. Everything an agent actually does lives in the program around it: the loop, the tools, the window, the budgets, the gates, the sandbox, the run log. Build that program, one mechanism at a time, until it can resolve a support ticket and issue a refund exactly once.
- Difficulty
- intermediate
- Time
- about 156 minutes
- Stages
- 9
- Topic
- Consensus, Coordination & Durable Execution
How this problem is worked
Nine stages, from what the thing is for to how it compares with the real implementations. Each asks one question, and the simulator runs the architecture you draw against the requirements you wrote.
- 01Purpose & invariantsWhat is this for, and what must always be true of it?
- 02Workload characterizationWho writes, who reads, and in what shapes?
- 03Data model & on-disk formatWhat does the data look like at rest?
- 04Core algorithmsHow do the write path and the read path actually work?
- 05Distribution & replicationHow does this scale out and survive losing a machine?
- 06Consistency & correctnessUnder concurrency and failure, what is guaranteed?
- 07Failure modes & recoveryWhat actually happens when each part fails?
- 08Operational characteristicsCan a human run this at three in the morning?
- 09Trade-offs & comparisonWhere does this sit against the alternatives?
More in Consensus, Coordination & Durable Execution
Getting N machines to agree, and getting one job to happen exactly once: Raft, coordination services, CRDTs, locks, leader election, schedulers and durable workflows.
- Build Build Raft — consensus you can defendReplicate a deterministic state machine across N servers with safety as a theorem and liveness under partial synchrony. Build the protocol from term to commit to safety proof to reads, and feel why etcd, Cockroach, and TiKV ship slightly different Rafts.
- Build Build a workflow engine (Temporal / Airflow / Cadence style)A function that survives crashes, restarts, and re-deploys — and still finishes. Build a durable execution engine where workflow code is replayed deterministically from an event history, activities retry with exponential backoff, sagas compensate on failure, and the same workflow definition runs identically a year later. Internalize why 'just retry the cron job' breaks at the second step.
- Distributed LockRedlock controversy, fencing tokens, lease vs lock.
- AI Agent PlatformLong-running multi-step LLM agents — durable workflow + sandboxed execution + LLM gateway. Brain / hands / state are independently replaceable. The agent run is a workflow, not a request.
Browse the full problem catalog, or see what the simulator does and does not model.