Your agent works beautifully in development. It demos perfectly. Then you deploy it.
And it:
Books the same flight twice when the API times out
Loses all progress when a user closes their browser
Burns through your monthly API budget in 3 hours
Sends 47 follow-up emails because it didn’t know it was waiting
Does the wrong thing without crashing — and you don’t find out until a customer complains
You’re not alone. Only 2% of organizations have successfully deployed agentic AI at scale. Gartner predicts 40%+ of agentic AI projects will be canceled by 2027 due to cost overruns and inadequate risk controls.
The problem isn’t your agent’s reasoning. It’s everything around the reasoning that tutorials don’t teach.
So I wrote the series I wished existed when I started shipping agents.
What This Series Covers
9 parts covering what actually breaks in production:
┌─────────────────────────────────────────────────────────┐│TUTORIAL VIEW││││┌──────────┐││┌─▶│OBSERVE│◀── environment │││└────┬─────┘│││││││▼│││┌──────────┐││││THINK│◀── reasoning │││└────┬─────┘│││││││▼│││┌──────────┐││││ACT│──▶ execute │││└────┬─────┘││││││└──────┘││ (repeat) ││││ "Just implement the loop and you are done!" │└─────────────────────────────────────────────────────────┘
What Tutorials Teach vs What Production Needs
What Tutorials Teach vs What Production Needs
┌─────────────────────────────────────────────────────────┐│PRODUCTION VIEW││││┌──────────┐│││OBSERVE│──▶ What if API times out? ││└────┬─────┘ What if data is stale? │││││▼││┌──────────┐│││THINK│──▶ What if reasoning costs $50? ││└────┬─────┘ What if it loops forever? │││││▼││┌──────────┐│││ACT│──▶ What if action is irreversible? ││└────┬─────┘ What if we crash mid-action? ││ What if it needs approval? ││││ Required: Idempotency, Checkpointing, Cost limits, ││Observability, Human gates, Security│└─────────────────────────────────────────────────────────┘
Why This Structure?
Each part follows a pattern:
What can go wrong — real production failures
Why it happens — the underlying cause
How to prevent it — patterns that work
Implementation — code you can use
Trade-offs — nothing is free
No hand-waving. Just mechanics.
Who This Is For
You should read this if:
You’ve built agents that work in demos but fail in production
You’re about to deploy your first agent and want to avoid the pitfalls
You’re debugging production agent issues and need a framework
You’re evaluating whether to build vs buy agent infrastructure
You probably don’t need this if:
You’re building simple single-turn LLM applications
You’re doing research, not production systems
The Cost of Getting It Wrong
Production Failure Costs
Production Failure Costs
┌─────────────────────────────────────────────────────────┐│ PRODUCTION FAILURE COSTS ││││Failure Mode│Business Impact││──────────────────────┼──────────────────────────────││Double booking│ Refunds, angry customers ││Lost progress│ Users abandon, re-do work ││Cost overrun│ $10K+ surprise bills ││Silent failure│ Wrong results shipped ││Security breach│ Data exposure, compliance ││││ 68% of teams hit budget overruns in first deployment ││ 50% cite "runaway loops" as the cause ││ API downtime surged 60% between Q1 2024 and Q1 2025 │└─────────────────────────────────────────────────────────┘