The Underestimated Challenge of Production AI: Standardized Components
Building AI systems is moving at an exceptional pace. New agent frameworks, orchestration models, and workflow patterns appear constantly, and every team can quickly develop its own way of building around AI.
In the short term, this looks efficient. In the long term, it can become an operational nightmare.
The same pattern already appeared during the microservices wave roughly a decade ago. At first, teams built their own approaches for service discovery, logging, and circuit breakers. Within a few years, many organizations realized that nobody fully understood the overall system anymore. The response was the rise of platform teams, internal developer platforms, and standards such as OpenTelemetry. Spotify built Backstage largely because internal tooling and services had become too fragmented to manage effectively.
AI systems now seem to be entering a very similar phase, but with one important difference.
Non-determinism makes the problem harder
Traditional software is largely deterministic: the same input produces the same output.
AI systems no longer behave this way consistently.
Modern AI systems may involve:
- LLM-based reasoning
- dynamic agent decisions
- changing context
- external tools
- branching workflows
- silent model version changes
As a result, system behavior becomes much harder to predict.
A concrete example is model evolution itself. OpenAI has deprecated models multiple times, and even within the same model family, behavior changes after updates have been well documented. If every team has its own evaluation framework, fallback logic, and error handling patterns, a model upgrade can break production in ways that may remain invisible until users start reporting issues.
This is the point where standardization shifts from being useful to becoming necessary.
The hidden cost of custom AI components
AI projects naturally encourage rapid experimentation:
- custom agent runtimes
- proprietary orchestration layers
- project-specific retry mechanisms
- custom tracing implementations
- unique guardrail systems
Individually, each decision often seems reasonable. Teams move faster and avoid waiting for centralized solutions.
The problems emerge gradually.
As organizations accumulate multiple AI systems, each starts to develop:
- its own error handling logic
- its own telemetry model
- its own tracing structure
- its own interpretation of retry behavior
- its own evaluation patterns
A fix made by one team does not benefit others. Model upgrades must be validated repeatedly across different implementations. Security audits need to review multiple guardrail implementations separately. Knowledge becomes concentrated around individuals, and over time organizations end up operating systems that nobody fully understands anymore.
The LangChain ecosystem offers a public example of this phenomenon. Many teams have moved away from it because rapidly evolving abstractions and unstable APIs made long-term operations increasingly difficult. The same pattern can easily emerge internally with custom-built AI frameworks.
Standardization does not reduce agility, it enables scale
Standardization is sometimes perceived as something that slows AI development down.
In practice, well-designed shared components often do the opposite:
- accelerate new solution development
- reduce operational risk
- improve observability
- simplify governance
- reduce duplicated work
- enable centralized improvements
A shared tracing model means debugging an agent chain no longer depends on the original author. A standardized guardrail implementation means a security improvement can be implemented once and benefit every system. Shared evaluation patterns make model upgrades manageable instead of turning every upgrade into a separate risk project.
This does not mean everything should be standardized immediately. Premature abstraction can be just as harmful as complete fragmentation.
A practical rule of thumb is that something is usually worth standardizing once the same problem has already been solved multiple times in slightly different ways. At that point, shared components typically start paying for themselves very quickly.
Where to start
As the number of AI systems grows, a few areas usually deliver the highest value when standardized first.
1. Tracing and telemetry
Without unified observability, it becomes extremely difficult to understand or compare system behavior.
2. Evaluation and regression testing patterns
Model upgrades quickly become risky without standardized evaluation approaches.
3. Guardrails and policy frameworks
Governance does not scale if every system implements safety and policy logic differently.
Many other areas can usually wait.
Scaling AI is also platform engineering
Bringing AI from demos into critical production environments is not only about models.
It is also about platform engineering.
The same principles that became essential for microservices, data platforms, and internal developer platforms are becoming increasingly important for AI systems:
- consistency
- observability
- operability
- governance
- standardized components
The difference is that non-deterministic systems punish fragmentation much faster.
Ultimately, the value of AI does not come only from model intelligence.
It comes from building systems that remain understandable, reliable, governable, and operable even as they grow large and complex.