Why do so many AI workflows break down when a single model—usually a general-purpose LLM is used for everything from research and coding to image generation and multi-agent automation?
The answer is simple: Because not all AI tasks are equal.
Recent enterprise surveys show that as of 2024, around 65% of organizations report using generative AI in at least one business function. (Source)
Yet, simultaneously, studies reveal that when LLMs like ChatGPT (or similar models) are used for research-style literature synthesis, hallucinations and inaccurate references remain a serious concern.
For instance, a 2024 peer-reviewed study found a high rate of reference fabrication when LLMs were used to replicate systematic reviews. (Source)
In a multi-agent architecture - where different agents handle tasks like code generation, data retrieval, image generation, or content summarization, choosing a one-size-fits-all model effectively invites errors:
The result: Cascading failures, wasted compute, and unreliable outputs.
Throughout this blog, we’ll show
Think of an AI model like a tool you might pull from a toolbox.
Each tool works well for one purpose, but no tool works well for every job.
A general-purpose language model can interpret text, respond to questions, and create content, but it struggles when tasks demand precision, structure, or specialized reasoning.
Coding tasks require strict logic. Data tasks rely on verified sources. Visual tasks depend on spatial understanding.
When one model tries to handle all of these tasks, its limits show up fast.
These gaps appear because different tasks use different structures, different inputs, and different forms of reasoning. No single model carries all of them.
Now that we’ve seen why a single AI model breaks down across different tasks, the next step is understanding what makes the right model succeed.
In multi-agent systems, each agent must rely on a model that fits its job, its input type, and its reasoning style.
When every agent uses a model built for its task, the entire system becomes more stable, more accurate, and more predictable.
This is where model selection becomes the difference between a workflow that works and a workflow that fails.
A multi-agent system works best when each agent uses a model built for its task.
One agent may analyze text. Second, may write code. Third may retrieve data. Another may interpret images.
Each task needs a different model type. When the right model powers the right ai agent, the system can divide work cleanly. It can pass information without confusion. It can reduce errors that would appear if a single model tried to do everything at once.
Multi-agent systems rely on clear role boundaries.
Matching each agent with the correct model lets the system produce more reliable outputs in less time. It also prevents one agent from blocking or slowing down another. With accurate model selection, the system behaves more like a coordinated team and less like one overstressed model trying to fill every role.
Example
Picture a production team. A writer creates scripts, a developer builds tools, and a designer creates visuals.
Each person handles one part of the workflow. A multi-agent AI system works the same way. If a writing agent uses a language model and a coding agent uses a code model, each part of the pipeline stays clear and predictable.
But if the writing model tries to write code or the coding model tries to analyze images, the workflow slows down, and errors appear.
Since each agent in a multi-agent system performs better when matched with the model built for its role, the next question becomes simple:
The answer depends on the type of work, the level of precision required, and the constraints of the workflow. This is where understanding the strengths of specialized models compared to general LLMs becomes essential.
When choosing an AI model, the first step is defining what the task actually demands. Some tasks require strict accuracy. Others depend on pattern recognition. Others need memory, retrieval, or a visual understanding of objects and scenes.
A general LLM cannot adjust its internal structure to meet these different requirements. Specialized models, on the other hand, are built around one skill: some evaluate logic, some interpret vision, some retrieve facts, and some simulate structured reasoning.
Instead of asking, “Which AI model is strongest?” a better question is, “Which AI model understands this task the best?”
A data-heavy workflow calls for a retrieval model that can verify inputs. A software pipeline needs an AI-backed model that follows deterministic logic. A design task needs a model built to interpret visual intent.
The strength of the AI model is not in its size, but in how closely its architecture matches the shape of the problem.
Now that the process of choosing the right model is clear, it's time to look at what happens when these AI models work together instead of working alone.
This is where AI shifts from single-model output to coordinated intelligence. When multiple models operate side by side, each with a clear responsibility, the system gains capabilities no single model could achieve on its own. That shift marks the beginning of a new direction in AI development.
Multi-model orchestration shifts AI from single-turn responses to full workflow automation. Instead of relying on a single model’s internal reasoning, an orchestrated system arranges multiple models into a sequence where each model operates only when the workflow calls for its capabilities.
The system controls when a model is activated, how information moves between steps, and how results are verified before continuing.
This creates a structured pipeline where progress depends on coordination, not on the strength of any single model.
A key benefit of orchestration is workflow transparency. Each action can be tracked, audited, and adjusted without changing the rest of the pipeline.
If the workflow needs a new verification step, the system inserts a model designed for that role. If the workflow needs faster planning, the system swaps in a model optimized for decision routing.
This modular structure reduces friction and makes complex processes easier to refine over time. It also allows organizations to combine models from different providers, modalities, and reasoning styles without building new systems from scratch.
As multiple models come together in a coordinated pipeline, the final question becomes: Does that coordination actually deliver reliable results — or does mismatched AI model choice still undermine the whole system?
The difference between a stable, high-performance AI system and one that fails often lies in exactly that: whether each model was fit for its assigned role.
Recent enterprise data shows that using AI broadly is no longer optional — it’s becoming mainstream. According to the IBM Global AI Adoption Index 2023, about 42% of enterprise-scale companies (with 1,000+ employees) say they have actively deployed AI at scale, while many more are experimenting. (Source)
However, many organizations struggle because they rely on generic models for diverse tasks, and that’s where incorrect model-task pairing becomes a critical failure point.
Selecting the correct model for each task isn’t just a matter of performance — it’s about system reliability, maintainability, and scalability. When each agent uses a model optimized for its role, AI pipelines become easier to audit, easier to continue evolving, and far less likely to produce cascading errors. That alignment can be the difference between a high-impact, stable AI deployment and a costly, bug-ridden failure.
The future of AI belongs to systems that choose the right model at every step. Platforms like Knolli support structured orchestration across multiple agents and specialized models, showing how powerful this approach can be.
Instead of relying on a single model to manage everything, Knolli routes each task to the model best suited for its purpose. This ensures that coding steps use logic-driven models, research steps use fact-grounded retrieval models, and visual tasks use image-aware architectures — all working in a coordinated flow.
Modern AI deployments increasingly require this level of precision. In environments where outputs feed into multi-step pipelines, even a small error can create downstream issues.
Knolli reduces this risk by aligning every agent with the correct model, making the workflow more predictable and significantly more resilient. It also allows organizations to evolve their pipelines without rebuilding them: new models can be integrated, upgraded, or swapped into existing roles without disrupting the system.
In practice, the difference between a high-performing AI workflow and a fragile one comes down to this structure. When the right model powers the right task — and when a platform like Knolli ensures those decisions happen consistently - The path to reliable automation becomes clear. This alignment transforms AI from a tool that “responds” into a system that performs with intent.
Unlock the full power of AI orchestration—start building smarter workflows with Knolli today. Try now and feel the difference!!!
