Everyone wants “AI Agents” right now. It’s the buzzword of 2025-2026. But after shipping multiple agentic systems to production, here’s the cold hard truth: Making a demo is easy. Making it reliable is hell.
The Demo Trap
It’s easy to make a video where an agent plans a trip, books a flight, and sends an email. You run it 10 times, pick the best recording, and post it on Twitter.
In production, users don’t follow your happy path. They ask ambiguous questions. APIs fail. The LLM decides to hallucinate a parameter that doesn’t exist.
Lesson 1: Deterministic Code > AI
We learned this the hard way. Early on, we tried to let the LLM handle everything.
- Bad: Asking the LLM to format a date.
- Good: Asking the LLM to extract the date, then using a regex/library to format it.
Keep the AI “box” as small as possible. Use it for reasoning and extraction, not for execution.
Lesson 2: LangGraph is a Lifesaver
We started with simple chains (LangChain). They turned into spaghetti code instantly.
State machines are the answer. We use LangGraph to define clear states:
- Input Analysis
- Tool Selection
- Execution
- Validation (Critical!)
- Response
If the “Validation” step fails, we loop back. The agent can retry. A linear chain just crashes or outputs garbage.
Lesson 3: The “Human in the Loop” isn’t optional
For high-stakes actions (sending money, deleting data, emailing clients), you cannot trust the model 100%.
We built an “approval mode” where the agent drafts the action, sends a notification to a Slack channel, and waits for a human to click “Approve.”
The 80/20 Rule
Building an AI product is 20% prompt engineering and 80% traditional software engineering:
- Rate limiting
- Caching responses
- Handling API timeouts
- Sanitizing inputs/outputs
- Observability
Don’t let the “AI” label fool you. It’s still software. It still needs tests.
What This Means for Your AI Project
If you’re thinking about building an AI agent, here’s what we’d tell you over coffee:
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Start with the boring stuff. Get your infrastructure, error handling, and observability in place before you touch a single prompt.
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Demo ≠ Production. Budget 3x more time than your demo suggests. The happy path is 10% of the work.
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Build escape hatches. Human-in-the-loop isn’t a crutch—it’s a feature. Your users will thank you.
We’ve shipped AI agents for customer support automation, data processing pipelines, and internal operations tools. If you’re evaluating whether AI agents are right for your business, let’s talk—we’ll give you an honest assessment, not a sales pitch.
Curious about project pricing for AI work? Check out our guide on fixed price vs hourly—the stakes are even higher with AI projects.