In today's ecosystem of large language models (LLMs), correctly routing each request to the optimal engine has become a major technical challenge. Not all consultations deserve the same calculation or the same cost. A short question like 'fix the authentication bug' can trigger a sequence of search, edit, and verify that only a large, expensive model can sustain without failure. Conversely, a lengthy paragraph asking for a comparison of blocking strategies is likely to be solved with a lightweight, free model. The paradox is obvious: the size of the request does not correlate with the actual complexity of the work. That is why the need arises to build an agent intention detector, a system that analyzes each request and decides whether it requires an autonomous agent or simply a generative response.
At Q2BSTUDIO we understand that artificial intelligence for companies must be practical, efficient and scalable. Our experience in the development of custom applications has led us to design intelligent routing architectures where these detectors are key pieces. After all, a bad routing decision can turn a promising debugging session into a context-loss chaos. Therefore, when building an agent task detector, a yes or no is not enough; A classification ladder that assigns minimum levels of effort is needed.
Let's imagine four categories: single request (no tools), toolchain (read, edit, test), iterative (retry and debug loops), and autonomous ('solve it yourself'). Each has a minimum model floor: an iterative request should never fall into a small model because the cost of losing the debug thread is enormous. The detector therefore assigns a score based on six independent signals. The first is the number of tools attached: the more tools a customer has ready, the more likely they are to perform multi-step work. However, this signal is the most misleading, as we will see.
Another crucial signal is the nature of those tools. It is not the same to have read-only tools (web search, grep) as mutant tools (bash, write, edit, proof). A customer bringing a shipment of state modification tools with them is a strong indication of agentic work. But the heaviest signal — up to 30 points — is the presence of results from previous tools in the conversation. If the chat already contains blocks of tool_result, we are not predicting an agent loop: we are already inside one. Remapping the model at that point is to discard the context discipline that keeps the loop convergent.
We also analyzed linguistic patterns in the last message: phrases such as 'next', 'later', 'step 2', 'keep trying', 'debugging', 'make it work', 'on your own' activate different categories. A combination of 'implement' and 'verify' adds up to more than each word separately, because it indicates a build-check cycle. The depth of the conversation (more than fifteen messages) and the length of the prompt (the weakest signal, intentionally) complete the picture.
However, the detector found a humiliating false positive: all requests from certain environments (such as Claude Code) scored as agentic, even a simple 'hello'. The reason? Those environments attach their full arsenal of tools (read, write, bash, etc.) to every request, even a greeting. The detector worked technically, but it was useless in practice: everything was headed for expensive models. The solution was to introduce customer profiles: if we know the tool baseline of a given harness, we subtract those tools from the computation of the tool signals. Signs of previous results and language patterns remain intact. In addition, for unknown customers with more than ten tools, all standard, we directly override those signals to avoid the same bias. It is better to err on the side of under-detection and rely on uncontaminated signals.
This approach is an example of how well-designed AI needs to understand the user's context, not just the text. At Q2BSTUDIO, we apply similar principles when developing AI agents that integrate with enterprise systems. For example, when building a wizard that automates workflows, it's vital to distinguish between an informational query and an execution order. Our teams implement intent detectors that also rely on AWS and Azure cloud services to scale without losing accuracy. Cloud infrastructure allows these detectors to handle thousands of requests per second, keeping latency low and cost controlled.
The detector also has known limitations. It only reads the last message, so phrases like 'do what I told you before' lose the reference. Language patterns sometimes confuse mention with intent: 'why did the retry loop fail?' triggers the iterative signal even if it's just a reading question. In practice, over-routing to a more expensive model costs pennies; Under-routing can break the session. Therefore, calibration should favor the slight cost overrun over the catastrophic error.
The ability to inspect every decision is critical. Each detection returns its complete evidence: score, list of signals with weights, classification, and a note on possible baseline subtractions. Without that transparency, the router is a black box that is impossible to tune. At Q2BSTUDIO we particularly value cybersecurity and traceability in our AI systems. When we work on business intelligence services with power BI, for example, we ensure that every data transformation is recorded for auditing. Similarly, an intent detector must be auditable so that failures are turned into improvements.
Building an effective LLM routing system goes beyond comparing tokens. It requires understanding the semantics of the work, the tools of the environment, and the history of the conversation. If you're developing custom software to integrate artificial intelligence into your business, having a partner who is proficient in these technologies makes all the difference. At Q2BSTUDIO we offer complete AI solutions for companies, from intent detection to automating complex processes with AI agents.
The main lesson is: subtract the constant before reading the signal. Identify what is always present in your ecosystem (default tools, system messages, etc.) and remove it from the analysis. Separate tool-ready from what you're already using. And most of all, make your detector explain itself. Only then will you be able to fine-tune the threshold between cost and quality of service.
If you want to explore how to implement agent intent detectors in your own applications or need advice on AI architectures, do not hesitate to contact us. At Q2BSTUDIO we combine expertise in custom applications, AWS and Azure cloud services, and cybersecurity to deliver robust and scalable solutions. Visit our artificial intelligence section to learn more about how we integrate these principles into real projects. You can also check out our custom app development service to see how we apply these techniques in cross-platform environments.
