Agentic Workflows That Actually Work
The unglamorous parts of shipping AI agents: retry logic, audit trails, and knowing when to put a human in the loop.
Why Demos Don’t Survive Production
Building agentic systems often feels like a sequence of “aha!” moments during the prototyping phase. I recently experienced this with a personal feedback agent that synthesizes my weekly journals and task lists into actionable critiques. In a vacuum, it worked flawlessly, offering high-signal advice on my habits. However, a sudden family crisis—a stroke and dementia diagnosis for my mom—forced me into two weeks of pure survival mode. While I was managing hospital logistics and care across state lines, the agent ran on schedule, cheerfully scolding me for “missing workout streaks” and “poor time management.”
This failure highlights a massive architectural blind spot: the context gap. The agent was technically accurate based on the data provided, but it was fundamentally “wrong” because it lacked a world model that accounts for human crisis. It had no heuristic to recognize that when 100% of a user’s inputs shift from “strategic planning” to “emergency logistics,” the standard productivity metrics should be suppressed. This is the difference between a system that is data-aware and one that is context-aware.
The true challenge of moving from a flashy demo to a production-grade agent lies in handling these “black swan” edge cases. In a demo, inputs are clean and the sun is always shining; in production, you deal with silent data corruption, API timeouts, and agents that hallucinate “creative essays” when you need a risk summary. The exciting part of agentic AI is the initial build, but the most valuable engineering happens when we solve for the moments the system fails—ensuring our agents don’t just process data, but respect the human reality behind it.
The Non-Negotiables
If you’ve watched the OpenClaw explosion over the past two weeks—145,000 GitHub stars, security researchers losing sleep—you’ve seen what happens when an agent ships with ambition but without infrastructure. Schema validation (Zod, Pydantic, Pi’s TypeBox) isn’t optional—it’s the difference between “my agent returned useful JSON” and “my agent returned a philosophical meditation on the nature of JSON.” Exponential backoff on every external call, calibrated to complexity: fewer retries for atomic agents, more room for complex orchestrations. And cap your loops—an agent without iteration limits is a credit card attached to a while(true).
Beyond that, every agent needs recoverability and observability. A workflow that can resume from a checkpoint, even imperfectly, is worth ten times one that restarts from zero on every hiccup. And you need to see what your agent decided, not just what it produced—structured logging as the baseline, Sentry for workflows where failure has a blast radius. The first time your agent makes a decision you can’t explain, observability becomes the only thing that matters.
Human-in-the-Loop
It costs nothing to send your human a Telegram DM: “Yo—something’s fucked up here, can you give me a direction?”
Two minutes of their time—a bathroom break—in exchange for saving 12 hours on a weekday. If the issue lands on a Friday night? You just saved 72 hours. That’s the math.
The framework is simple: if the action is irreversible, expensive, or customer-facing, a human approves it. Everything else runs autonomously with logging. Make it async and non-blocking—the agent parks that branch, keeps working on others, and picks up when the human responds.
Due Diligence Checklist
If you’re evaluating an agentic system—whether you’re building it, buying it, or hiring someone to implement it—these are the questions that separate production-grade from demo-grade.
Does the system validate every LLM output against a schema before acting on it? If not, you’re trusting a probabilistic model to always return well-formed data. It won’t.
What happens when an external API call times out? If the answer is “the workflow crashes,” you’re not ready.
Is there a maximum iteration count on every agent loop? If someone can’t tell you the number, walk away.
Can the workflow resume from a failure, or does it restart from scratch? Partial completion with graceful degradation is the minimum bar.
What does the audit trail look like? Can you reconstruct why the agent made a specific decision three weeks ago? If the answer involves grepping through unstructured logs, it’s not enough.
Where are the human checkpoints? If the answer is “nowhere” or “everywhere,” neither is correct. The right answer names specific decision points with clear escalation criteria.
What’s the cost visibility? Can you tell, per workflow run, how many tokens were consumed, how many retries occurred, and what the total API spend was? If you can’t measure it, you can’t manage it.
Final Thoughts
The gap between a compelling agent demo and a reliable agent in production is enormous. It’s filled with retry logic, schema validation, error interpretation, checkpoint design, and structured logging—the unglamorous work that doesn’t make for good Twitter threads but makes the difference between a toy and a tool.
The agents that actually work aren’t the cleverest. They’re the ones that fail well.