The AI development community is increasingly recognizing that successful production-ready LLM applications often require a more modular, controlled approach rather than relying on all-encompassing agent frameworks. This insight emerges from discussions around the 12 Factor Agents principles, which outline best practices for building reliable LLM applications.
The Framework vs. Library Debate
Developers are finding that while agent frameworks promise simplicity and quick deployment, they often lead to limitations when scaling to production. The community is converging on the view that LLMs work best when implemented as libraries rather than frameworks, giving engineers greater control over critical components. This approach allows for better customization, more predictable behavior, and easier debugging when issues inevitably arise.
Most 'AI Agents' that make it to production aren't actually that agentic. The best ones are mostly just well-engineered software with LLMs sprinkled in at key points.
This sentiment reflects the practical reality many developers face when moving beyond demos to production systems that need to be reliable and cost-effective. The excitement around fully autonomous agents often gives way to more pragmatic implementations where LLM capabilities enhance existing software architecture rather than replacing it entirely.
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| Structured outputs facilitate greater control over LLM applications by leveraging libraries instead of frameworks |
Control Flow and State Management
One of the key principles emphasized in community discussions is the importance of maintaining control over execution flow and state management. Rather than surrendering control to black-box frameworks, successful implementations tend to keep engineers in charge of how and when LLM components are invoked.
Developers note that unifying execution state with business state (Factor 6) and owning control flow (Factor 8) are crucial for building systems that can be properly debugged, monitored, and optimized. This approach allows teams to handle edge cases and failure modes that might not have been anticipated by framework designers.
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| Managing control flow is crucial for reliable LLM applications, ensuring predictable behavior and easier debugging |
Cost Considerations at Scale
An important practical consideration raised by the community is the cost of running LLM applications at scale. Token consumption can quickly become expensive, especially when agents operate in loops making multiple LLM calls. Developers recommend using deterministic components whenever possible before resorting to LLM calls.
This cost-conscious approach not only improves the bottom line but often results in more reliable systems with lower latency. By carefully choosing where to apply LLM capabilities, teams can maximize value while minimizing unnecessary expenditure.
Testing and Reliability Challenges
The community has highlighted the reliability challenges inherent in agent-based systems. Even when an agent works correctly 99% of the time, that 1% failure rate can be problematic in production environments. Adding more LLM-based guardrails doesn't necessarily solve the problem, as it relies on the same potentially fallible technology.
This has led many teams to adopt more structured workflows rather than fully autonomous agents, particularly for enterprise applications where predictability and reliability are paramount. The ability to test, validate, and ensure consistent behavior becomes increasingly important as these systems take on more critical tasks.
In conclusion, while the promise of autonomous AI agents continues to capture imagination and drive innovation, the practical reality of building production-ready LLM applications often leads developers toward more controlled, modular approaches. By understanding these principles and trade-offs, teams can build more reliable, cost-effective, and maintainable AI-enhanced systems that deliver real value to users.
Reference: 12 Factor Agents - Principles for building reliable LLM applications