10 Ways to Build Future-Proofed AI Workflows

Several Azure OpenAI model versions will be retired in March 2026, giving organisations a limited window to migrate affected workloads - a timely reminder of the operational realities of AI adoption.

For many enterprises, AI adoption has outpaced AI operations maturity. Proofs of concept have become production systems. Experiments have evolved into business-critical workflows. Yet the operational rigour applied to traditional infrastructure dependencies still has some way to go to be extended to AI systems.

As large language models become increasingly embedded in core business processes, establishing proper operational foundations becomes essential. 

The announcement surfaces an important question: if a critical business process were running on a deprecated model, would you know?

The following ten practices provide a framework for building genuine resilience into LLM workflows.

1. Implement a Model Abstraction Layer

Application code should not call specific models directly. An abstraction layer enables model changes through configuration rather than code modifications, standardising input and output formats while handling model-specific requirements. This is why we typically leverage capabilities like AWS Bedrock or Azure Foundry. 

When deprecation notices arrive, when superior models become available, or when pricing changes alter the economics of a particular choice, the migration becomes a configuration change rather than a development project. This single architectural decision substantially reduces the cost and risk of model transitions.

2. Maintain a Business Process to Model Registry

A comprehensive registry should map business processes to supporting AI workflows and their underlying models. Beyond technical documentation, this registry must capture business context: workflow ownership, criticality ratings, potential impact of disruption and escalation contacts.

When changes occur in the model landscape, organisations should be able to assess impact within minutes rather than conducting an extensive discovery exercise. Without this visibility, every external change triggers an internal audit.

3. Capture Workflow Metadata Systematically

Every AI workflow and agent requires documented metadata: model and version information, update history, ownership, dependencies and expected usage patterns. Where possible, this capture should be automated and derived from configuration rather than maintained manually.

This constitutes a software bill of materials for the AI estate. Traditional software deployments require clear visibility of libraries and dependencies. AI systems warrant the same discipline.

4. Establish Model Observability and Usage Telemetry

Instrumentation of model calls enables tracking of actual usage patterns: which workflows call which models, with what frequency, at what times and with what outcomes.

When deprecation notices arrive, telemetry data reveals whether affected model versions have been called recently. A model with no calls in 90 days represents a low-priority cleanup task. A model handling thousands of daily requests for revenue-critical processes demands immediate attention.

Beyond deprecation management, usage telemetry supports cost optimisation, performance monitoring and identification of upgrade opportunities. Solutions like Langfuse help in this space, with the hyperscalers now stepping into this void too with more mature monitoring capabilities. 

5. Identify and Address Dormant Workflows

Workflows are sometimes deployed and subsequently fall into disuse while remaining technically active. Observability practices should flag dormant workflows to ensure appropriate prioritisation during migration planning.

Regular identification of unused deployments also supports general operational hygiene. Every active deployment represents a potential migration task, a security consideration and ongoing cost. Resources not delivering value should be decommissioned. We’ve had the same issues with application inventories over time. Who owns this application? Can it be migrated? How much do we pay for it? All questions we have already answered in the traditional software domain. 

6. Build a Continuous Model Evaluation Pipeline

Proactive evaluation of new models against representative test cases removes the discovery phase from migration timelines. A suite of test cases drawn from production scenarios, run against candidate models on a regular cadence, provides ongoing readiness data.

Relevant metrics vary by context but typically include output quality against defined criteria, latency, cost per request and edge case performance. When migration becomes necessary, decisions are informed by existing data rather than a hurried assessment.

7. Version Control Prompts and Workflows

Model behaviour varies and prompts optimised for one model may underperform on another. Versioned prompts maintained alongside model versions, with documentation of which variants perform best with which models, enable smoother transitions.

Migration involves more than endpoint changes. It potentially requires deployment of prompt variants already validated for the target model. Without version control, this institutional knowledge remains undocumented and vulnerable to personnel changes.

8. Develop a Multi-Provider Strategy

Single-provider dependency creates concentration risk. Maintaining validated capability across multiple providers, whether Azure OpenAI alongside direct OpenAI access, or incorporating Anthropic, Google, or other providers, ensures options remain available.

This does not require active multi-provider deployment for all workloads. However, validated failover capability, established commercial agreements and tested connectivity provides resilience when circumstances require alternatives.

Capability mapping across providers should be established in advance, identifying which models from each provider satisfy specific requirements such as function calling, vision capabilities, or extended context windows.

9. Consider Model Ownership for Critical Workflows

Fine-tuning open source models provides complete control over the model lifecycle. No external deprecation notices, no pricing changes, no API modifications imposed by third parties.

The trade-off involves operational complexity. Hosting, scaling and maintenance become internal responsibilities. However, for critical workflows where predictability outweighs the need for cutting-edge capabilities, this approach merits consideration.

A tiered strategy often proves effective: managed API services for experimental work and commodity tasks where convenience is paramount, with owned and controlled models for stable, well-defined, business-critical processes.

10. Define Upgrade Runbooks and Clear Ownership

Clear ownership and documented processes are essential. Responsibilities should be established for monitoring deprecation announcements, evaluating alternatives, approving production changes and executing testing and rollout procedures.

A two-month migration window proves challenging when responsibilities are being determined for the first time. It becomes manageable when processes have been rehearsed and roles are clearly understood.

Rollback procedures warrant equal attention. The ability to revert if migration causes unexpected production issues should be documented and tested before it is needed.

Building Operational Maturity

The AI operations landscape continues to mature rapidly. Tooling is advancing, practices are emerging and most organisations are developing capabilities iteratively.

However, deprecation notices serve as valuable reminders that fundamental operational disciplines remain relevant. Visibility into what is running, clear ownership, understood alternatives and documented plans constitute the foundation of resilient LLM workflows.

Organisations that establish these practices will not only navigate deprecation events with confidence. They will adopt improved models more rapidly, optimise costs more effectively, and maintain greater control over their AI investments.

AI infrastructure warrants the same operational rigour as any other critical business dependency.