Lately, I've been diving deep into LLMs, agents, and prompt engineering, experimenting with how these systems perform when integrated into my self-hosted setups. Something that really struck me is how monolithic LLM systems, which think holistically and operate without the constant back-and-forth typical of microservices, tend to finish tasks faster and more efficiently—especially when they understand the full context.
I couldn’t help but think about how this mirrors the way humans solve problems. If we don't have all the context, our brains naturally slow down, trying to fill in gaps, ask questions, and figure out what’s missing. When there’s sufficient context, whether it's in a conversation, a problem at work, or a decision we need to make, we act fast and confidently. I started wondering: What if LLMs can be designed to function more like this human problem-solving process?
Why Monolithic LLMs Outperform in Context-Rich Scenarios
One thing I noticed is that when a monolithic LLM system gets all the necessary context upfront, it solves problems quicker because it’s not fragmented into smaller tasks. In contrast, microservices excel at scaling and modularity but often face communication overhead that slows things down.
For example, in my projects, I’ve seen LLMs operating monolithically take full control of generating insights, analyzing complex data, or crafting detailed solutions, and they do this faster than a microservice-based system might. Why? Because there’s no need for back-and-forth communication between small, isolated components—the LLM has all the information in one place.
The Struggle with Microservices and Context Stitching
On the other hand, I’ve also worked with microservice architectures, which are great for scaling and distributing workloads across systems. But here’s the problem I kept facing: context stitching.
In a multi-agent or microservices environment, each agent or service has its own task to handle, and they need to communicate to stitch together the full context. The more complex the task, the more communication overhead is required, leading to inefficiencies. It’s like having a group of people trying to solve a puzzle, but no one person has all the pieces—they have to keep exchanging information, which takes time. This is where microservices get bogged down.
But here’s the catch: this isn’t just a tech problem. Humans face the same issue. When we lack context, our brain pauses to gather more information. Without a complete picture, we take longer to make decisions, ask more questions, and solve problems inefficiently. LLMs, I realized, experience a similar struggle.
Applying Human-Like Context Stitching to LLMs and Multi-Agents
Thinking about this, I wondered how we can combine the best of both worlds. In human teams, context stitching happens naturally through communication. We share information until we piece together the full picture. In LLMs and agents, I think a middle ground could involve creating a dynamic orchestration layer—a layer that intelligently knows when to keep context in the monolith (for fast problem-solving) and when to delegate specialized tasks to agents or microservices.
This is where prompt engineering plays a key role. By crafting prompts that help LLMs understand the full scope, we can ensure they solve problems faster without constantly passing information back and forth.
In this hybrid approach:
- LLMs act like human problem solvers, handling core tasks where having a complete picture is critical.
- Microservices or agents handle the specialized tasks, where scalability is necessary, such as fetching data, making calculations, or transforming inputs.
But, the magic happens in the orchestration layer. It ensures the LLM retains as much context as possible while delegating smaller tasks to agents that don’t require full context. This cuts down the communication time and minimizes the need for constant context stitching.
Visualization of the Hybrid System
Here’s a quick Plotly chart to visualize how this system performs compared to traditional monolithic and microservice-based architectures:
This chart shows how the hybrid system combines the efficiency of monolithic LLMs with the scalability of microservices, especially in complex tasks that require both fast context understanding and the ability to delegate smaller jobs.
Where I’m Heading Next
In my own projects, I’m actively exploring how to better stitch context across agents, just like how humans communicate to solve problems. The challenge is in finding the balance between the efficiency of a monolithic system and the flexibility of microservices, all while keeping communication overhead low.
And this journey has led me to consider Conway’s Law in a new light. Conway’s Law states that “organizations design systems that mirror their communication structure.” When teams are siloed, the systems they create often reflect that fragmentation. Similarly, in microservices, each service can become isolated, requiring constant communication to make sense of the whole. But with LLMs, we have the opportunity to create systems that mimic holistic human thinking—systems that can hold entire contexts and solve problems quickly and autonomously.
As I continue to experiment with agents, LLMs, and orchestration layers, I’m excited to see how we can push the boundaries of software design to not only be more efficient but also more human in the way it handles context.
It’s a new frontier, and I’m thrilled to be exploring it.
This is my evolving view on the combination of LLMs, microservices, and context stitching. Let me know if you'd like any changes to this blog post or further insights on the hybrid approach!