The near-ubiquity of LLM systems in 2025 has changed the game in many ways. While Large Language Models have been around for some time, their mass adoption for modern applications and services has reshaped interactions dramatically, changing how systems and their APIs interconnect.
Instead of relying on direct human initiation and interaction, agentic LLM systems are becoming increasingly autonomous, connecting AI models to internal and external systems without human feedback. While this pattern offers huge benefits in terms of automation, scalability, and rapid evolution, it introduces some quite unique challenges throughout the software development lifecycle.
Today, we’re going to look at one slice of complexity: application and API mocking. While mocking in traditional software development typically focuses on creating stubs and unit tests to mirror service dependencies and calls to external services, the advent and wide adoption of artificial intelligence has introduced a new domain that must be mocked – agent-to-model. The complexity of these interactions is often obfuscated by the nice natural language processing typical in the frontend of these AI systems – in actuality, they are some of the most incredibly complex and difficult to mock systems in the current tech stack.
In this piece, we’ll define what agent-to-model means, explain why it changes the mocking paradigm, and offer five practical tips to improve how you test and validate these systems. We’re going to dive into some solutions offered by Speedscale, and give you some actionable steps you can take today for better agent-to-model mocking.
Understanding Mocking in Agent-to-Model Context
Agent-to-model is a paradigm where AI agents, powered by large language models, autonomously interact with structured backends via APIs.
These agents parse natural language input using the inference and intelligence systems developed through repeated integration with high-quality training data, allowing them to do a wide variety of functions, ranging from the generation of plans and workflows to actual code generation. Specifically, these large language models are built on transformer models, which enable them to understand complex relationships in human language through techniques like self-attention. The agents can then process the request and transfer it to the appropriate system or output. In essence, they don’t just execute hardcoded flows – they reason, adapt, and choose how to invoke APIs on the fly.
Since these agents are effectively creating cooperative and coordinated execution, traditional mocking tools often fall short of effectively representing these systems. You can easily mock or stub a static series of endpoints, but when it comes to agent-to-model interactions, you need to account for varied and unpredictable integrations and requests. AI is not human yet, but in many ways, mocking agent-to-model AI implementations is closer to mocking humans than it is to mocking machines.
Introduction to Agent-to-Model
Agent-to-Model is a revolutionary approach that leverages the power of large language models (LLMs) to enhance human capabilities. By combining the strengths of human agents and artificial intelligence (AI) models, Agent-to-Model enables more efficient and effective decision-making, content creation, and problem-solving. This innovative approach has the potential to transform various industries, from customer service and virtual assistants to content generation and research assistance. With the ability to generate text, answer questions, and provide insights, Agent-to-Model is poised to revolutionize the way we work and interact with technology.
Foundation of Agent-to-Model
The foundation of Agent-to-Model lies in the concept of large language models (LLMs), which are a type of machine learning model designed for natural language processing tasks. LLMs are trained on vast amounts of text data, allowing them to acquire predictive power regarding syntax, semantics, and ontologies inherent in human language corpora. By fine-tuning these models for specific tasks, developers can create powerful AI systems that can generate text, answer questions, and provide insights. The transformer model, a type of neural network architecture, is commonly used in LLMs due to its ability to handle sequential data and capture long-range dependencies.
How Agent-to-Model Works
Agent-to-Model works by leveraging the strengths of both human agents and AI models. Human agents provide context, guidance, and oversight, while AI models generate text, answer questions, and provide insights. The process involves several key components, including data preparation, model training, and prompt engineering. Data preparation involves curating and preprocessing large datasets, which are then used to train the AI model. Model training involves fine-tuning the AI model for specific tasks, such as text generation or question answering. Prompt engineering involves crafting input prompts that elicit specific responses from the AI model. By combining these components, Agent-to-Model enables humans and AI models to collaborate effectively, leading to more efficient and effective decision-making and content creation.
Why Mocking Large Language Models Needs a New Approach
Fundamentally, the problem here is that agentic patterns introduce a few specific wrinkles in processes that need to be wrangled.
- Unpredictable traffic patterns – agents may call APIs in different orders or frequencies depending on how they interpret a prompt. Unlike machines with set routes and patterns, these patterns may be more effective while being less predictable, creating issues with mocking and representation that are hard to resolve.
- Real-time decision making – mocking must preserve logical integrity so agents don’t generate false inferences. As these agents make decisions on the fly based on the data and interactions they’re observing, mocking without using real data or insight can result in decision-making in the mock that is wildly different than those made in the actual production environment. Agents are often fine-tuned to perform specific tasks, such as interpreting questions or generating responses, which adds another layer of complexity to the mocking process.
- Cross-layer fidelity – these complex agent-to-model interactions often span multiple APIs, meaning mocks must simulate entire workflows, not just endpoints. In such a setup, your data and the quality of that data become paramount, significantly influencing the quality of the production service at scale.
Benefits of Agent-to-Model
The benefits of Agent-to-Model are numerous and significant. One of the primary advantages is the ability to generate high-quality content quickly and efficiently. AI models can produce text, answers, and insights at a speed and scale that human agents cannot match. Additionally, Agent-to-Model enables humans to focus on higher-level tasks, such as strategy, creativity, and problem-solving, while AI models handle more routine and repetitive tasks. Another benefit is the ability to provide 24/7 customer support and virtual assistance, improving customer experience and satisfaction. Furthermore, Agent-to-Model can help reduce the risk of human error, improve accuracy, and enhance overall productivity.
Limitations of Agent-to-Model
While Agent-to-Model offers many benefits, there are also several limitations and challenges to consider. One of the primary limitations is the quality of the training data, which can affect the accuracy and reliability of the AI model. Additionally, AI models can inherit biases and inaccuracies present in the training data, which can lead to suboptimal performance. Another limitation is the need for significant computational resources and infrastructure to support the training and deployment of AI models. Furthermore, Agent-to-Model requires careful prompt engineering and human oversight to ensure that the AI model is generating relevant and accurate responses. Finally, there are also concerns around job displacement, as AI models may automate certain tasks currently performed by human agents. However, by understanding these limitations and challenges, developers and organizations can design and implement Agent-to-Model solutions that mitigate these risks and maximize the benefits of this innovative approach.
5 Tips for Agent-to-Model Mocking
With all this in mind, let’s look at five excellent tips for agent-to-model mocking.
Tip 1 – Capture Real Traffic Early
The biggest mistake in mocking agentic systems is assuming you know what to mock. Mocking is meant to be a representation of your actual API, so when your mock drifts from the real API into a projection, you can undermine your entire mocking and testing effort quite readily.
Instead, use real traffic capture to ground your mocks in reality first, setting a strong standard before launching into fine-tuning and iterations. Speedscale excels here as a solution since it can sit inline, intercept API traffic, and enable you to build deterministic mocks based on real observed patterns. Additionally, capturing documents in this process ensures that the mocks are comprehensive and reflective of real-world usage.
With a real base system in place, you can then iterate upon this baseline, such as:
- Deploying new security solutions and standards to test efficacy against real-world attack attempts and recorded real-world scenarios;
- Test the actual API against network failures and other types of external uncontrollable risks, allowing you to test a variety of “what if” scenarios effectively;
- Validate the character of the API documentation against the actual API, ensuring that what you think should happen does; and
- Test different large models, their handling of sensitive data, the key differences in their processing and output of different responses, and their response to reinforcement learning of various types.
These are just a few of the huge benefits unlocked by early data capture – in essence, early capture turns mocking from guesswork into data-backed simulation.
Tip 2 – Emulate Latency and Variability
Agents can behave differently based on response times or unexpected edge cases, especially when handling requests such as image content creation or high-volume text generation. Handling image text in response times and edge cases is crucial for robust AI applications. A mock that always returns a perfect 200 response in 10ms isn’t useful, and certainly does not represent the variety inherent in machine learning and prompt engineering practices. Introducing controlled variability unlocks some huge mocking benefits, including:
- Randomized latencies within realistic bounds to test the resiliency of neural networks, the management of input tokens (and their fail states), and the ability of these agent-to-model systems to respond to uncontrollable variations;
- Flaky or partial responses, especially those that fail in the same way on specific tasks, suggesting a particular failure of the model or prompt request;
- 4xx and 5xx error simulation wherein the problem lies outside of your code or programming languages, and is instead localized external to the service; and
- Errors are introduced routinely by human agents, such as overly long and confusing prompts or requests with malformed or malicious entry methods.
ProxyMock supports latency injection and behavior scripting, letting you simulate these edge conditions with minimal configuration and unlocking a larger number of testable controls.
Tip 3 – Replay Workflows, Not Just Calls
Agents don’t just make single calls – they often pursue multi-step goals that require natural language and complex queries. For instance, a travel-planning agent might call search, availability, booking, and payment APIs in sequence or parallel to generate complex workflows resulting in very complex data. This is where llm applications come into play, as they can efficiently handle such intricate processes. Mocking a single endpoint won’t validate the agent’s behavior across the entire flow.
Speedscale’s scenario replay lets you capture and re-run full call chains. This is essential when testing how agents adapt to changing states or partial failures mid-execution. Virtual assistants are often created for complex variables and workflows, and such models require complexity in their mocking to represent the complexity of the request in the simulated version within the mock. This is especially true when models hybridize data sources, calling both external data sources and services, as well as internal ones such as Retrieval Augmented Generation databases or complex transformer architecture.
In short, you are trying to mock a service that is based on complex workflows, including various forms of content, so don’t try to dumb down your mocking to just the endpoints underpinning it!
Tip 4 – Validate Agent Output, Not Just Input
In agentic systems, the response isn’t just whether the mock returns 200 OK. It’s whether the agent behaves logically given the mock. Agents must also understand context to generate accurate responses. This is a major part of effective agent utilization, and as such, you need to check whether:
- The agent parsed the response correctly (and gave the data that had been requested in the format requested);
- The follow-up actions make sense based upon this data, the original request, and the original situation in which the request was made, utilizing an attention mechanism to focus on relevant parts of the data; and
- Failures are gracefully handled using a pre-defined and understood method.
In other words, your test should validate agent reasoning as much as API correctness.
Tip 5: Use Dynamic Mocks for Open-Ended Queries in Natural Language Processing
Agents may not always hit the same endpoint with the same parameters. Sometimes, they’ll ask broader questions or vary query structure in order to get a more complex question answered. For these cases, use schema-driven or dynamic mocking, where responses are generated based on request patterns rather than simple endpoint flows. Dynamic mocking can effectively handle unstructured data, ensuring that responses remain relevant and accurate.
Speedscale can auto-generate mock responses based on observed schemas or create templated responses that interpolate dynamic values – this is ideal for ensuring agents still get meaningful data even with unstructured inputs. Pre-trained models are particularly effective in these scenarios, as they are trained to handle unstructured inputs and generate contextually appropriate responses.
Closing Thoughts
Agent-to-model systems are, in many ways, the future of intelligent API consumption. Foundation models play a crucial role in this future by enhancing the capabilities of AI through extensive training on vast datasets. That being said, they demand a shift in how we approach testing and mocking. Static mocks are out, but dynamic, high-fidelity mocking and testing leveraging capture and replay are in. By grounding your testing strategy in robust and realistic data and workflows, you can ensure that your agents perform reliably, safely, and intelligently. Speedscale can help you capture real traffic, making your mocking and testing that much better – and the best part is that you can get started with Speedscale in minutes today with a powerful free trial!
Large language models (LLMs) improve their performance through techniques such as reinforcement learning with human feedback (RLHF). This method is crucial for mitigating biases, eliminating unwanted outputs, and refining the quality of the generated text, thereby ensuring that the LLMs can be used safely and effectively across various applications.
