System Design: How would you build an AI agent orchestration platform? Imagine asking 3 AI agents to complete one task: → Agent 1 writes the code. → Agent 2 reviews it. → Agent 3 runs tests before merging. Sounds simple, right? The real challenge is coordinating everything reliably. 1. Orchestration Layer - A central orchestrator assigns tasks, tracks workflow state, and coordinates agents. 2. Workflow Engine - Represent dependencies as a DAG. - Code writing must finish before review starts, and tests run after approval. - Independent tasks can execute in parallel. Each task follows a state machine: "PENDING → READY → RUNNING → COMPLETED" On failure: "RUNNING → RETRY_WAIT → READY" If retries are exhausted, the task moves to "FAILED". Persist state transitions so workflows can recover after crashes. A task becomes "READY" only when its dependencies are satisfied. 3. Execution Queue - Use Kafka or a durable job queue to distribute tasks to workers. - Workers claim jobs, execute them, and report results. - Use idempotency keys to prevent duplicate processing. 4. Failure Handling Failures are inevitable. Design for them. - Transient errors: Retry with exponential backoff and jitter. - Repeated failures: Move tasks to a dead-letter queue for investigation. - Worker crashes: Use leases and heartbeats to detect abandoned tasks and safely reassign them. - Model/API timeouts: Apply timeouts, retry limits, and circuit breakers. - Partial failures: Persist completed outputs so successful agents don't need to rerun unnecessarily. - Invalid agent output: Validate results before allowing dependent tasks to proceed. Never assume a retry means the previous execution didn't happen. Use idempotency and durable checkpoints to avoid duplicate side effects. 5. Shared Context - Store task instructions, agent outputs, code diffs, logs, and conversation history in shared storage. - Pass each agent only the context it needs. 6. Conflict Prevention - Give coding agents isolated Git branches or worktrees. - Validate diffs, run tests, and merge only after conflict checks and required approvals. 7. Observability & Cost Control - Track token usage, model costs, execution time, retries, and failure rates. - Set budgets and stop runaway workflows. 8. Pause, Resume & Cancel Persist workflow state in PostgreSQL. - Pause: Stop scheduling new tasks and let active workers reach safe checkpoints. - Resume: Requeue eligible tasks. - Cancel: Stop pending tasks and signal active workers to terminate safely. Keep "FAILED" and "CANCELLED" separate: one means execution failed; the other means execution was intentionally stopped. Architecture at a glance: Developer → API → Orchestrator → Workflow Engine → Durable Queue → Agent Workers Supporting services: - PostgreSQL - workflow state and checkpoints - Redis - caching and short-lived coordination - Object storage - logs, artifacts, and code outputs - Retry/DLQ mechanism - failed task recovery - Observability - logs, metrics, traces, and cost tracking The key principle: assume every component can fail, and make recovery part of the architecture. How would you prevent two agents from modifying the same files and accidentally overwriting each other's work?

I was asked this in an Uber SDE-1 interview last year. Problem: Design the low-level system for matching a rider with the nearest available driver. My answer: I’ll store drivers in a hash map keyed by driver ID. When a rider requests a ride, I’ll scan all online drivers, calculate distance for each, and pick the closest one. Then mark that driver as busy. But then they asked: How does this work when you have 50k online drivers in a city, and 200 ride requests come in at the same second? I knew the interview was cooked right there. What’s your response to get selected?