The Demo-to-Production Gap
Every AI agent demo looks impressive. The agent handles complex queries, chains tools together, and delivers results that seem like magic. Then you deploy to production and everything breaks.
This isn't a failure of AI—it's a failure of expectations. Production environments are messier than demos. Edge cases multiply. Users do unexpected things. Systems fail in unpredictable ways.
At OriginLines, we've shipped AI agents to production across industries. Here's what we've learned about building systems that actually work.
The Four Pillars of Production AI Agents
1. Reliability Engineering
Expect Failure, Design for Recovery
LLM APIs have downtime. Rate limits hit at inconvenient times. Model outputs occasionally make no sense. Production agents need to handle all of this gracefully.
Our approach:
- Circuit breakers that prevent cascade failures when upstream services degrade
- Retry logic with exponential backoff and jitter
- Fallback behaviors that provide value even when the AI is unavailable
- Graceful degradation that maintains partial functionality during outages
Not everything needs an LLM. Use traditional code for tasks with known rules. Reserve LLM calls for genuinely ambiguous situations that require reasoning.
Example: Parsing dates from user input. An LLM can handle "next Tuesday" or "the day after tomorrow." But for ISO 8601 strings, use a date parser. It's faster, cheaper, and more reliable.
2. Human-in-the-Loop Design
Confidence-Based Routing
Not all agent decisions are equal. Some are routine, others are high-stakes. Production agents need to distinguish between them.
We implement confidence scoring that routes decisions appropriately:
- High confidence, low stakes: Execute automatically
- High confidence, high stakes: Execute with notification
- Low confidence: Escalate to human review
- Novel situations: Always escalate
For consequential actions (financial transactions, data modifications, external communications), build approval workflows. The agent prepares the action; a human confirms it.
This isn't a limitation—it's a feature. Customers trust systems that include human oversight for important decisions.
Override Mechanisms
Sometimes agents make mistakes. Sometimes circumstances change. Production systems need clear ways for humans to intervene, correct, and redirect agent behavior.
3. Observability & Debugging
Comprehensive Logging
When an agent makes a bad decision, you need to understand why. This requires logging:
- Input context and conversation history
- Reasoning steps and intermediate outputs
- Tool calls and their results
- Decision points and confidence scores
- Final outputs and user feedback
Traditional logging isn't enough. Agent behavior spans multiple steps, tools, and decision points. We use structured traces that capture the full execution path.
Tools like Langfuse, Phoenix, or custom solutions help here. The investment in observability pays off quickly when debugging production issues.
Feedback Loops
The best agents learn from mistakes. Build systems to capture user corrections, flag problematic outputs, and feed this back into prompts or fine-tuning.
4. Testing & Evaluation
Unit Tests for Tools
Every tool an agent can call should have comprehensive unit tests. Tool failures cascade into agent failures. Reliable tools are the foundation of reliable agents.
Integration Tests for Workflows
Test complete workflows end-to-end. Mock external services when necessary, but test the full chain of agent reasoning, tool use, and output generation.
Evaluation Datasets
Maintain curated datasets of inputs and expected outputs. Run these regularly to catch regressions. Expand the dataset as you encounter new edge cases.
Human Evaluation
Some things are hard to test automatically. For subjective quality (tone, helpfulness, accuracy), periodic human evaluation is essential.
Common Pitfalls & How to Avoid Them
Pitfall: Over-Reliance on Prompts
Prompts are powerful but fragile. Small changes have unpredictable effects. Model updates break carefully-tuned prompts.
Solution: Complement prompts with structured outputs, validation logic, and explicit constraints. Don't rely on the LLM to do something that code can do reliably.
Pitfall: Insufficient Error Handling
Demo agents assume everything works. Production agents must handle:
- Malformed outputs from the LLM
- Tool failures and timeouts
- Unexpected user inputs
- Rate limits and quota exhaustion
Pitfall: Ignoring Cost
LLM calls cost money. Agents that call APIs hundreds of times per request burn through budgets fast.
Solution: Monitor costs carefully. Cache repeated calls. Use smaller models for simple tasks. Set per-request budgets.
Pitfall: Poor Context Management
Context windows are finite. Agents that stuff everything into context hit limits and degrade.
Solution: Selective context loading. Summarize long histories. Retrieve relevant information rather than including everything.
The OriginLines Approach
When we build AI agents for clients, we follow a consistent methodology:
1. Define success criteria before building anything 2. Start with the simplest possible implementation that could work 3. Add complexity only when needed to meet reliability requirements 4. Instrument everything from day one 5. Plan for human oversight as a core feature, not an afterthought 6. Test with real users early and often 7. Iterate based on production data, not assumptions
This approach has helped us ship agents that handle millions of interactions reliably. The agents aren't perfect—no system is—but they're robust enough for enterprise use.
Getting Started
If you're building AI agents for production, start with these questions:
1. What decisions should the agent make autonomously? 2. What decisions require human approval? 3. How will you know when the agent makes a mistake? 4. What happens when the AI is unavailable? 5. How will you measure success?
The answers will shape your architecture and set realistic expectations.
Building production AI agents is challenging but achievable. The teams that succeed are rigorous about reliability, thoughtful about human oversight, and obsessive about observability.
We've helped companies across industries deploy AI agents that deliver real value. If you're ready to build, let's talk.