Sparking New Ideas
Route36 has been experimenting with language models in many different scenarios. Our projects have ranged from basic tasks solved with a single prompt to more flexible systems that handle unpredictable situations. One thing that kept showing up was how quickly complexity could spiral out of control when we let a model decide its own path. We kept returning to a simpler approach that often yielded better results.
Thinking About Workflows vs. Agents
Many tasks do not require agent capabilities. Plenty of goals can be tackled with a single, carefully crafted model prompt. If you only need a summary or a short answer, you might not need the added overhead of an agent. Agents become more appealing when the problem involves several steps, such as checking different data sources or making decisions based on new information. A system that can move beyond plain text generation is what we call agentic.
Though the term agent can sound intimidating, we see it as any setup that combines a language model with the ability to do things like query APIs or edit files. If you design a workflow that follows a strict script, the model has less freedom. If you let it decide which path to take, it starts to feel more like an agent. Each approach has a place, so it depends on the requirements at hand.
Points to Consider Before Adding Complexity
Extra layers can be appealing at first because they promise more flexibility or intelligence. The reality is that every new feature adds another place where things can go wrong. Debugging can turn messy if you hide details behind too many abstractions. We usually recommend beginning with a direct call to the language model. Code that you can read and modify is easier to trust. If you reach a point where you need a sophisticated tool, you will at least understand the core logic behind your system.
Setting Up Augmented Models
A language model by itself is limited. We often see better outcomes when it has extra abilities, like calling a search service, looking up internal data, or writing updates to a repository. This creates an augmented model. To make this work, the model needs clear instructions about how and when to use each function. If a tool is overly complicated or returns confusing data, the model might fumble. In our experience, simple interfaces and common data formats reduce errors.
Strategies That Seem to Work
Several patterns have shown up repeatedly in our projects. Some teams chain multiple prompts in a sequence. Step one might draft a plan, step two expands the sections, and step three polishes everything. Others route requests based on their type. A short query might be sent to a quick prompt, while a more involved query goes to a more advanced setup.
Parallelizing is another effective pattern. It splits a large task into segments or runs multiple model calls at once. The results can be merged or compared to find the most accurate response. Another option is to have a main model coordinate smaller helper models. This can feel more natural than a rigid script.
One more idea is to have an evaluator model that critiques the output of the main model. Both models can loop until the evaluator is satisfied. Tasks that benefit from multiple revisions, like translating or code generation, can see big improvements with this approach.
Handling Full Autonomy
Sometimes we give the model the freedom to keep going until it has finished the task. It might reach out to different data sources or attempt new strategies on its own. This can work well if the steps are difficult to predict in advance. It can also be risky. The model might drift off track, repeat itself, or accumulate significant costs through multiple calls. We try to prevent problems by limiting how many times the model can call a tool or by enforcing timeouts. Testing each part of the chain is also key.
Examples That Inspire
We have had success using agent-based systems for automated support. The model does more than just provide answers. It also retrieves account details and takes actions like issuing refunds. Another example is code generation. An agent writes code, runs tests, then refines the code until the tests pass. A human can step in when needed, but it speeds up the process for tasks that are clearly defined.
Building Tools That Make Sense
No matter how creative your agent is, its effectiveness depends on the tools you connect. Consistent and predictable interfaces help the model avoid mistakes. If your file paths keep causing confusion, a direct mapping to absolute paths might help. If the model has trouble parsing complicated data, switching to a simpler format like JSON can make a difference. Clarity is everything.
Choosing the Right Level of Complexity
Route36 believes that solutions should be as direct as possible. An advanced agent is only worth building if you can point to a real benefit. If a single prompt already meets your needs, there is no shame in using it. Testing and measuring your outcomes will show you if agent features bring genuine improvements. If you see clear gains, you can scale up. If not, a smaller approach might save time and resources.
Finding a Path Forward
Our journey with language model agents has led us to value clear steps, transparent tools, and just the right amount of autonomy. There are many ways to build these systems, and we suspect new techniques will emerge as more people experiment. For now, we think it is smart to focus on what actually works, measure results at every stage, and remember that simpler solutions often outperform more tangled ones. By paying attention to detail, you can harness the power of agents without drifting into chaos.