Changing AI models sounds like it should be rather easy. If a new model performs better, costs less or has better capabilities, why wouldn't you just link your existing AI agent to it and move on? Well, you may be able to. But there's a difference between getting an agent to talk to another model and actually getting it to do its job well once you've switched it.
The choices are getting larger, too. Google Cloud announced plans to make OpenAI models available in its Model Garden in October 2026. The Linux Foundation, a day later, said it was launching new projects to bring more interoperability to sections of AI agent development. This is good news for companies who want to avoid being dependent on a single AI vendor.
But portability of AI models is not as simple as flipping a switch. The amount of effort involved depends on your agent, how it has been created, and what portions of it depend on the original supplier.
Can You Change an AI Model Without Rebuilding the Agent?
Yes, you can change AI models without rebuilding the whole agent. In some circumstances, replacing the model is simply a matter of configuration. In others, you may have to adapt the application, tweak its workflows or change out components that only operate with the original provider. Knowing what it is you're actually changing helps.
The AI agent utilises a model to understand instructions, make judgements, and decide when to use tools. But the program that sits around that model deals with things like data access, permissions, memory and the processes required to perform a task. If your organisation owns those components and uses different models, then moving may not be too difficult.
If they’re locked in to a single provider’s technology, it gets more problematic. And even if a technical connection succeeds, there's no assurance that the agent will act the same way. In September 2026 research, VentureBeat Intelligence found that 59 per cent of surveyed organisations who changed models in a production AI application needed to rework the program once performance degraded, or revert to the previous model.
Only 19 per cent said the extra engineering effort was almost negligible. The results are from a relatively small poll of 68 respondents reporting on switches completed or attempted. But they point to a problem worth testing for. Model compatibility does not imply application portability.
What Actually Transfers When You Switch AI Models?
Imagine you have an AI bot that can handle customer service queries. It reads messages, checks account information, looks up corporate policies and either responds to the consumer or escalates the issue to a person. Now you find another model that seems to do the same job for less money.
You might be able to maintain most of the application. But before you move over you need to know what parts may be used with the replacement model and what parts are dependent on how the original model works.
Model APIs and tool calling
An API (application programming interface) is what your application uses to talk to an AI model. It handles requests and responses so the agent doesn't need to know how the model works internally to use it. Some suppliers have compatible APIs or shared interfaces which make the transfer easy.
But just because they provide the same interface doesn’t mean they support exactly the same features. The documentation for tool use in Amazon Bedrock, for example, states that different models allow different choices for regulating when tools are invoked. Some also do tool calling and streamed responses differently.
Your customer care agent might still be able to connect to the new model but might have difficulty with a function that it used to use without any problems. That involves looking past whether the requests succeeded.
You’ll need to determine whether the replacement model can work with the essential tools, deliver the appropriate information, and handle answers effectively.
Instructions, skills and agent workflows
An agent’s instructions inform it what to do, and how to do a task. Its workflow tells it what actions to take, when to gather information, when to use a tool, and when to seek support. These components can often be reused especially when they are stored outside of the model provider’s platform.
But different models don’t necessarily read the same instructions the same way. One might reliably follow one sequence, another might omit a step, or use a different tool. That’s a problem if the workflow depends on those phases happening in a certain order.
Reusable Agent Skills allow businesses to share task-specific instructions and supporting resources across compatible platforms. However, when a skill is reused, the replacement model does not necessarily execute it correctly.
Context, memory and enterprise integrations
Agents need information to accomplish their tasks. Customer records, internal documents, past conversations or details pulled from company systems, maybe. If your organisation keeps this information separately it may not need to shift when you change models. But the agent’s technique of retrieving, recalling and using it may still need to be adjusted.
For example, different models have distinct constraints on context. This is the quantity of information that they can deal with in an interaction. In addition, the provider’s chat history can create additional dependencies. And for integrations as well.
Your agent may still be using the same customer database after switching models but you'll still need to inspect how it connects, authenticates and manages the information it receives.
Governance, monitoring and commercial dependencies
And then there is all the stuff that goes into running the agent as part of a business. The replacement model needs to comply with the organisation's security policies, access controls, monitoring and audit requirements. If the original provider does any of those responsibilities, you may need substitute services or additional integration effort to move away.
Commercial arrangements can sometimes make the decision a difficult one. Providers may have different pricing structures, contractual commitments, data-processing conditions, and support agreements. An agent that performs flawlessly in testing may nevertheless be inappropriate if the new arrangement does not satisfy the organisation’s operational or compliance needs.
Do Open Standards Make AI Agents Portable?
Some of these dependencies are being reduced through open standards. They develop standardised methods for various systems to communicate or exchange data, rather than needing developers to construct individual integrations for each provider. On 22 October 2026, the Linux Foundation’s Agentic AI Foundation announced four new initiatives, including Open Responses and Agent Skills.
They tackle distinct difficulties but are particularly relevant to AI agent interoperability. Open Responses aims to provide a consistent interface for requests, responses, streaming and tool interactions across model providers. Agent Skills provides a reusable structure to package the instructions, scripts and other resources that an agent requires to carry out certain activities.
The Model Context Protocol (MCP) meanwhile provides a common approach for AI applications to connect to external tools and data sources. Together these standards may enable more of an application to be preserved as models or platforms evolve. There is a lot of interest already.
More than 70 agent platforms have incorporated the standard, Anthropic said in its announcement regarding transferring Agent Skills. But these are standards for individual pieces, not guarantees that whole apps would work across providers. Two models won't behave the same just because they share an API.
A reusable skill doesn't mean you get the same result. And a standard tool connection won't address variances in permissions, execution, or error handling. But while open standards can help organisations avoid AI vendor lock-in, they still have to figure out what their apps can actually do when the underlying model changes.
How Can Enterprises Test AI Model Portability Before Switching?
The best technique to test portability is to test an existing agent with a replacement model before committing to the migration. But that doesn't mean just seeing if it gives you an answer. You need to determine if it can still do the work it was built to do, within the needs of the organisation.
Map the dependencies before changing providers
First, determine all the dependencies of the agent. Know what your organisation controls vs what the existing vendor supplies or manages. Then figure out which ones will work with the proposed replacement. Questions that are worth answering include:
- Can we reuse the current prompts, tools and process definitions?
- Where does the agent store the memory, conversation history and evaluation records?
- Is the application dependent on provider-specific APIs or features?
- Will authentication, restriction of access, and audit logging continue to function?
- Are there contract or data processing limitations on transferring workloads?
This offers you a better view of the potential migration effort, before anyone starts rewriting code. It also helps to distinguish between replacing a model inside the same platform vs. migrating a complete agent to another provider’s environment. The latter can take substantially more work.
Run the existing agent against the replacement model
After you know the dependencies, test the replacement model with tasks that are representative of how the agent really works. In the case of customer assistance, this involves asking more than a handful of typical enquiries.
You’d want to evaluate if the agent can retrieve the necessary account details, follow business policies, use the right tools and recognise when something has to be handled by a human. Also, include incidents where things go awry. What happens if a tool fails, if information is missing, or if a request is beyond the agent’s permissions?
Here regression testing is really important. This involves running existing tests against the modified application to discover if something that worked before is now broken. Compare the outcome with the original agent, using identical tasks and acceptance criteria. Observe accuracy, task completion, tool usage, response times and failures.
Security and permission checks should be part of the evaluation too. A model that does well on general benchmarks could struggle with the specialised process your organisation relies on.
Measure the full cost of switching
After you know what will work and what will not, you can begin to calculate the real cost of migration. Stacklok’s State of AI Architecture 2026 survey found that 57 per cent of 520 enterprise AI leaders surveyed anticipated it would take at least four months to move their present workloads to another model provider.
The most cited impediments were technical difficulty, lack of experience or tooling, and uncertainty regarding prices. Those results don’t suggest that every migration will take months. They do say, though, businesses should budget for more than just plugging in a new model.
Include time spent updating instructions, altering integrations, running evaluations and troubleshooting unexpected failures. Think about infrastructure expenditures, worker training, operational impact and any existing contractual commitments. And you need to look at the ongoing costs.
A cheaper model may just require more queries, longer processing or more services to provide acceptable results. The goal is to determine if the predicted advantages are commensurate with the total cost of getting the replacement agent ready for production.
When Is Switching AI Models Worth It?
There are plenty of solid reasons to think about updating AI models. A replacement might perform better at jobs you care about, have reduced running expenses or have capabilities your old model doesn't offer. It may also lessen dependence on one supplier. But the choice shouldn’t come down to model performance and pricing alone.
In June 2026, IBM’s report The Calculus of AI Sovereignty found that 71 per cent of 1,000 senior executives believed it would be difficult to replace their primary AI provider or model. Another 91 per cent said they didn’t completely grasp their AI dependencies across vendors, models and infrastructure. It’s a tough place to try and make purchasing selections from.
Without visibility of the dependencies, companies cannot accurately forecast what it will take to change providers. A practical decision comes down to three possibilities. If the current model still serves you, you can keep it. If the benefits are worth the effort, you are able to change the application to utilise a replacement.
Or, if the existing infrastructure is too expensive, has unacceptable risks, or is just limiting, you can migrate at a bigger scale. There's value in testing portability even if you don't want to move right away. Knowing what would have to change puts the organisation in a stronger position to analyse contracts, plan future enhancements or consider alternatives.
Final Thoughts: AI Portability Has to Be Proven
Enterprises have more AI models to select from. But that choice is only beneficial when they understand what it would take to migrate their existing apps from one supplier to another. Open standards are making some progress in facilitating the portability of AI agents, and some applications may already be flexible enough to swap out models with relatively little effort.
The most important thing is to know what situation you're in before you decide to migrate. With AI models continuing to evolve, the greatest advantage for a company may not be having access to the latest model.
It’s having applications that allow them the flexibility of choice. And as those choices become more complicated, EM360Tech will keep examining the technologies and decisions shaping how enterprises put AI to work.
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