My skepticism wasn't just theoretical. When I needed to process a large corpus of documents with Ollama, RAG was the only viable solution available. I found myself implementing it despite my reservations, and each time, it felt like I was applying a patch rather than solving the fundamental problem.
The issues with RAG run deeper than just implementation challenges. It's inherently blind to context in ways that concern me. What happens when different people use different terms to describe the same concept? How do we capture information that's relevant but not syntact
ically similar? These questions kept nagging at me, suggesting that we needed a fundamentally different approach. This got me thinking about a different approach, one that might seem a bit unconventional at first.
The idea hit me during breakfast one morning: what if we could create an architecture that mimics how we actually process information? Not the simplified "search-then-process" model of RAG, but something more organic, more distributed. I'm thinking of a system where multiple LLM instances work together, like separate areas of the brain working together, each with its own specialized neural network but collaborating to process information holistically.
I know this might sound ambitious, maybe even a bit naive. But bear with me as I explain the concept.
The Current State of Things
Let's be honest about RAG's limitations. It's not just about the context window constraints, though that's certainly part of it. Every time I implement RAG, I find myself wrestling with the same challenges: the computational overhead of repeated retrievals, the awkward separation between retrieval and generation, and the constant struggle to maintain coherent information synthesis across multiple queries.
These aren't just technical inconveniences - they're signs pointing to a deeper architectural limitation. It's like we're trying to force a rigid, mechanical process onto something that should be organic and fluid, more like how our minds naturally work with information.
A Different Way Forward
The architecture I'm envisioning is different. Instead of the traditional RAG approach, imagine a hierarchy of specialized agents, each with its own focus and capabilities. At the top, we have coordinator agents orchestrating the flow of information. Below them, multiple processing branches handle different aspects of the content, working in parallel, sharing insights, and building a more comprehensive understanding.
This isn't just theoretical - I've been experimenting with early implementations, and while they're still rough around the edges, the potential is exciting. The system can dynamically allocate resources, spawn new processing branches when needed, and synthesize information across multiple domains without the artificial constraints of a single context window.
The Technical Heart
At its core, this architecture operates on three simple principles: hierarchy, autonomy, and synthesis. The coordinator agents act like project managers, deciding how to break down complex tasks and distribute them across the system. The processing branches are the specialists, each focusing on their particular domain but maintaining communication with their peers.
What makes this different from existing approaches is its fluidity. Unlike traditional RAG or even Mixture of Experts systems, this architecture can reshape itself based on the task at hand. It's like having a team that can reorganize itself on the fly, bringing in exactly the expertise needed for each situation.
Implementation Realities
I won't pretend this is easy to implement. The challenges are significant: managing coordination overhead, maintaining consistency across distributed states, and optimizing resource allocation. In my early experiments, I've hit numerous roadblocks and had to rethink several aspects of the design.
But here's what keeps me going: working with LLM, particular writing "System Messages" for AI Agents has opened my eyes to an entirely new programming paradigm. Every time I can squeeze in some time to work on this project, I discover new ways of thinking about and approaching problems. It's like learning to program all over again, but with different rules and possibilities. Each interaction with the system teaches me something new about how we might structure AI systems in the future.
Looking Forward
As I continue to work on this concept, I'm increasingly convinced that this approach, or something like it, represents the future of how we'll handle large-scale information processing in AI systems. The economics are trending in our favor - computational costs are decreasing, and the need for more sophisticated information processing is only growing.
I'm sharing these ideas not because I have all the answers - far from it. I'm sharing them because I believe this is a conversation worth having in our community. Maybe you see flaws in my thinking, or perhaps you have insights that could help refine these concepts further. Either way, I'm excited to hear your thoughts.
The Path Ahead
What excites me most about this architectural approach is not just its technical potential but its alignment with how we naturally process information. As I continue to develop and refine these ideas, I'm increasingly convinced that the future of AI lies not in forcing our existing paradigms to scale, but in rethinking our fundamental approaches to match the natural flow of information processing.
I'm well aware that what I'm proposing here might seem overly ambitious, or perhaps even impractical given current constraints. But I believe that's exactly why we need to start thinking about it now. The limitations of current RAG implementations aren't going away, but both the demands and capabilities of our AI systems are only increasing.
As we look to the future, I see this hierarchical agent architecture not as a replacement for RAG, but as its evolution - the next step in our journey toward more capable, more efficient AI systems. It's a journey I'm excited to be part of, and one I hope others will join.
I'd love to hear your thoughts on this architectural approach. Have you encountered similar challenges with RAG? How do you see the future of information processing in AI systems?
I'm preparing a follow-up article diving into the economic implications of this architectural shift. We'll explore how decreasing computational costs and increasing model capabilities might make this approach not just technically feasible, but economically inevitable.
I'm particularly interested in your thoughts on this economic angle - what factors do you think will drive or hinder adoption of more sophisticated architectures?
Alternatively I can go deeper into the architecture that I present above, if there is interest.
Let me know your insights, as they will help shape the discussion.
