The Production-Ready AI Agent Checklist (Updated For 2026)

A developer compiled a production-readiness checklist for AI agents based on an 'Ask HN' thread, focusing on specific behaviors rather than infrastructure. The checklist includes observable logging, graceful fallbacks across providers, explicit tool permission boundaries, and persistent state management, with concrete examples using the OpenClaw framework.

The most useful HN thread this week wasn't a product launch. It was a question: "Ask HN: What makes an AI agent framework production-ready vs. a toy?" The answers were more practical than I expected. Not "uses Kubernetes" or "has enterprise support." The community pointed at specific, buildable behaviors. I went through the thread and turned it into a checklist you can run against your OpenClaw setup today — with the specific OpenClaw primitives that implement each item. Toy agents: You ask "what happened?" and the agent tells you a story. Production agents: You open a log and see exactly what ran, in what order, with what inputs, and what came back. In OpenClaw, this means: Check your gateway logs openclaw logs --tail 100 Check a specific session openclaw session history <session-key --limit 50 Enable verbose logging in your config openclaw config get logging.level should be debug or trace The specific things you should be able to answer from logs alone: If you can't answer those five questions from your logs, you're running a toy. Toy agents: One model failure cascades into everything failing. Production agents: Each failure is contained, logged, and recovered from without losing work. In OpenClaw, this is the fallback chain: { "payload": { "fallbacks": "nvidia/qwen3.5-122b-a10b", "ollama/qwen3.5:27b-q4 K M", "nvidia/nemotron-nano-12b-v2-vl", "ollama/qwen3.5:9b", "minimax-portal/MiniMax-M2.7", "minimax-portal/MiniMax-M3" } } Three cross-provider fallbacks before your primary. When MiniMax is overloaded, the agent doesn't die — it tries Ollama, then Nvidia's endpoint, then another MiniMax model. The work continues. The circuit breaker pattern: if a tool fails 3 times in a row, stop trying it and tell the user. Add this to your cron job payloads: { "payload": { "timeoutSeconds": 120, "lightContext": true } } Timeout is the circuit breaker. If a call hasn't returned in 120 seconds, it counts as a failure and the agent moves to the next fallback. Toy agents: The agent can do anything, including things you didn't intend. Production agents: Each tool has a explicit permission boundary that the agent cannot exceed. In OpenClaw, this is the tool policy in skills. The deny list is the whole point: name: safe-exec description: Exec tool with hard limits — no rm -rf, no curl|bash, no cred exfil system prompt addendum: | You have exec access. You may not: - Run any command containing 'rm -rf' without explicit user approval - Run any command containing 'curl | sh' or 'wget | bash' - Access environment variables containing secrets OPENAI KEY, ANTHROPIC KEY, etc - Write to any path outside /home/themachine/.openclaw/workspace/ If a request matches any of these patterns, refuse and explain why. tool policy: allow: exec, read file deny: write file, http request, browser The agent can read and execute, but not write arbitrary files or make outbound HTTP calls. The deny list is the security surface. Toy agents: Every session starts from scratch. The agent has no memory. Production agents: State persists across sessions, survives restarts, and has explicit recovery logic. In OpenClaw, this is the 3-level memory system: memory/YYYY-MM-DD.md → Daily log raw events, what happened MEMORY.md → Curated knowledge decisions, context, patterns ~/self-improving/ → Execution memory what worked, what didn't The daily log is the source of truth. MEMORY.md is what survives compaction. The self-improving directory is where patterns compound. For state that must survive a restart cron job counters, pending tasks, error states : { "name": "cron-health-check", "payload": { "kind": "agentTurn", "message": "Check all cron jobs. If any are in error state for 2 hours, run openclaw cron run --id <jobId . Write results to logs/cron-health-$ date +%Y%m%d .json" } } The health state is written to a file, not stored in memory. When the agent restarts, it reads the file and knows where it left off. Toy agents: You have to watch them to know they're working. Production agents: They send you a message when something goes wrong. In OpenClaw, this is the failureAlert on every cron job: { "failureAlert": { "after": 1, "channel": "telegram", "to": "749348Tracker", "cooldownMs": 3600000, "mode": "announce" } } After 1 failure, Telegram alert. 1-hour cooldown so you're not spammed if the job is retrying. You don't have to watch the agent — it watches itself and tells you when something breaks. The health check cron runs every 30 minutes: python openclaw cron list --json | python3 -c " import sys, json jobs = json.load sys.stdin for job in jobs: if job.get 'consecutiveErrors', 0 = 2: print f'Job {job \"id\" } has {job \"consecutiveErrors\" } consecutive errors' " If any job has 2+ consecutive errors, auto-retrigger it. You don't find out about failures at 9am — you find out within an hour and the job tries to recover automatically. Go through each item: openclaw logs --tail 20 . Can you follow a single request through the log?If you answered no to any of these, that's your next hour of work. The thread's conclusion was: production-ready agents aren't defined by their models or their benchmarks. They're defined by what happens when something goes wrong. The checklist above is a map of "what goes wrong" for OpenClaw operators — and the specific primitives that handle each case. Ship the one that's broken first. Then the next. Then you have a production agent.