Agentic AI: Model Optionality

Background

In the rapidly evolving landscape of agent accelarators: Agent Frameworks and LLM Gateways are often seen as a layer for effectively interfacing with diverse set of model providers and models.

While there are many factors in choosing an agent framework, We specifically focus model optionality, which gives agents the ability to work with multiple LLM providers and models .

As Enterprises build more agents, one of the key components to choose is the right LLM provider and the models within those. This choice is not easy to make as it seems. There are variety of reasons this choice may be fluid and dynamic at least until we see a certain maturity

Newer Models from frontier labs

Newer and better Models at regular interfaces from the frontier providers. We have seen regular announcements from all the frontier labs and the periodicity of announcement of newer better models is only increasing.

Newer players

Plethora of new and emerging players with hosted open-source and closed source models emerging.

Competitive intensity and velocity of announcements

With the increasing competitive intensity and improved benchmarks on common tasks, the leader model often varies by task or a group of tasks. This again is constantly changing with every new model announcement.

Non functional requirements

Cost and Latency are emerging as key selection criteria while choosing the model and the provider. The cost and latency parameters variance is extremely high and is dependent on the provider, model, region, capacity and subscription tier.

For example, Anthropic Claude Sonnet works great for multi-step tasks, coding related tasks, whereas OpenAI GPT-4o works well with creative writing, report generation etc, while Deepseek R3 and Grok may offer the best cost & latency per query,

A privately tuned/hosted model may provide best outcome for a domain specific task and tasks where data privacy needs are absolutely non-negotiable.

Real world benchmarks and evolving usecase needs

As models get to real world applications, undertanding of evaluation needs is also improving. Newer Benchmarks catering to these real agentic needs are adding a new dimension of looking at the model strengths and weaknesses on newer dimensins. for example, iterative tool calling, Long context understanding and reasoning etc.

Conclusion

Model optionality is therefore crucial in exploiting from the diversie set of available provider/model options and picking the right model for a specific agentic task need.

Additionally, these choices are unlikely to be static and change driven by the above factors is going to be unavoidable during the life cycle of the agent as newer, better, faster models become available and price matrices evolve.

Enterprise agentic application planning, architecture, evaluations must therefore factor in the above driver upfront to enable them choose the most optimal choice during the agent creation as well as through its lifecycle.