#AI Architecture
Articles about AI Architecture — exploring patterns, best practices, and real-world implementations in production systems.
22 posts tagged with ai architecture. ← All posts
The forward deployed engineer builds a win inside one customer. The Forward Deployed Architect makes that win survive the next ten — turning bespoke builds into reference architecture, passing security review, and deciding what's reusable. It's the missing layer between heroics and product.
The forward deployed engineer builds a win inside one customer; the Forward Deployed Architect makes it survive the next ten — reference architecture, security and governance that passes review, and the reuse decision that separates product from one-off. The missing layer between heroics and product.
A frontier model knows the public internet and nothing about the customer. All the value of an AI deployment comes from the opposite: making the model reason over the customer's own documents, records, and knowledge. Grounding is the technical heart of the AI forward deployed engineer's job — connecting a general model to a specific company's messy, permissioned, incomplete data so its answers are about their reality, not the model's imagination.
A frontier model knows the public internet and nothing about the customer. All the value of an AI deployment comes from the opposite: making the model reason over the customer's own documents, records, and knowledge. Grounding — retrieval-augmented generation over messy, permissioned, incomplete data — is the technical heart of the AI FDE's job. With an interactive reference-architecture diagram.
You cannot run your best, most expensive model on millions of items for every request — the latency and cost are impossible. The elegant, near-universal answer is to split recommendation into two stages: a cheap, fast net that narrows millions of items to a few hundred, followed by a precise, heavier model that ranks those few. This retrieve-then-rank structure is the single most important architectural pattern in production recommenders.
You can't run your best, most expensive model on millions of items per request. The near-universal answer is to split recommendation into two stages: a cheap, fast net that narrows millions to a few hundred, then a precise, heavier model that ranks those few. This retrieve-then-rank structure is the single most important architectural pattern in production recommenders — with an interactive pipeline diagram.
The cascaded pipeline — ASR, then LLM, then TTS — has powered voice agents for years, but it has an inherent ceiling: every stage adds latency and every conversion loses information. A newer architecture collapses the three models into one that goes from speech directly to speech, promising lower latency and richer understanding. This post compares the cascade with the emerging end-to-end approach, and where each fits.
The cascaded pipeline — ASR, then LLM, then TTS — has powered voice agents for years, but it has an inherent ceiling: every stage adds latency and every conversion loses information. A newer architecture collapses the three models into one that goes from speech directly to speech, promising lower latency and richer understanding. Comparing the cascade with the emerging end-to-end approach.
Put the pieces together and a modern frontier language model comes into focus: a deep transformer whose feed-forward blocks are Mixtures of Experts, whose attention is made efficient with grouped-query and FlashAttention, and which is engineered end to end around one goal — maximum capability per unit of compute and memory. This closing post assembles the anatomy and looks at where architecture is heading.
Put the pieces together and a modern frontier LLM comes into focus: a deep transformer whose feed-forward blocks are Mixtures of Experts, whose attention is made efficient with grouped-query and FlashAttention, engineered end to end around one goal — maximum capability per unit of compute and memory. This closing post assembles the anatomy and looks at where architecture is heading.
The two costs of long context — quadratic attention compute and linear KV-cache memory — each have a family of solutions, and together they're why modern models can handle context lengths that were impossible a few years ago. Grouped-query attention shrinks the KV cache; FlashAttention computes exact attention far faster; sliding-window and sparse patterns break the quadratic. This post covers the techniques that made long context practical.
The two costs of long context each have a family of solutions, and together they're why modern models handle context lengths that were impossible a few years ago. Grouped-query attention shrinks the KV cache; FlashAttention computes exact attention far faster; sliding-window and sparse patterns break the quadratic. The techniques that made long context practical, and which cost each attacks.
MoE scales a model's parameters cheaply. But there's a second scaling axis that matters just as much for modern LLMs: context length — how much text the model can attend to at once. Attention's cost grows with the square of the sequence, and the memory to run it grows linearly and relentlessly, which is why long context was hard and why so much architectural ingenuity has gone into it.
MoE scales a model's parameters cheaply, but there's a second axis that matters just as much: context length. Attention's compute grows with the square of the sequence, and the KV-cache memory grows linearly and relentlessly — two distinct costs, often confused, that make long context hard. Understanding both is the setup for the efficiency techniques that solved them.
Mixture of Experts saves compute but not memory — and that single fact reshapes everything about how these models are trained and served. All the experts must exist in memory even though only a few run per token, which turns MoE into a distributed-systems problem as much as a machine-learning one. This post covers the trade MoE actually makes and the parallelism it forces.
Mixture of Experts saves compute but not memory — and that single fact reshapes everything about how these models are trained and served. All the experts must exist in memory even though only a few run per token, which turns MoE into a distributed-systems problem as much as a machine-learning one. The trade MoE makes and the expert parallelism it forces.
The router is where Mixture of Experts succeeds or fails. Left to its own devices, it tends to collapse — sending most tokens to a handful of favorite experts while the rest starve, wasting the model's capacity. Making routing spread load evenly, without hurting quality, is the central engineering challenge of MoE, and the techniques for it are what separate a working sparse model from a broken one.
The router is where Mixture of Experts succeeds or fails. Left alone it tends to collapse — sending most tokens to a handful of favorite experts while the rest starve, wasting the model's capacity. Making routing spread load evenly, without hurting quality, is the central engineering challenge of MoE: load-balancing losses, expert capacity, and token dropping.
Now we open up the mechanism at the heart of modern LLMs. A Mixture-of-Experts layer replaces the transformer's single feed-forward block with many parallel "experts" and a router that sends each token to just a few of them. The result is a model with enormous total capacity that spends only a little compute on any given token — the decoupling the first post promised, made concrete.
A Mixture-of-Experts layer replaces the transformer's single feed-forward block with many parallel experts and a router that sends each token to just a few of them. The result is a model with enormous total capacity that spends only a little compute on any given token — the decoupling of capacity from per-token compute, made concrete. Experts, routing, and how outputs combine.
Mixture of Experts is a modification to the transformer, so you can't understand modern LLM architecture without a clear picture of the transformer itself. This post is a focused recap of the parts that matter for the rest of the series: attention as the mechanism that mixes information across tokens, the feed-forward block that MoE replaces, and how the pieces stack into the model everyone is now modifying.
Mixture of Experts is a modification to the transformer, so you can't understand modern LLM architecture without a clear picture of the transformer itself. This post is a focused recap of the parts that matter: attention as the mechanism that mixes information across tokens, the feed-forward block that MoE replaces, and how the pieces stack into the model everyone is now modifying.
The frontier language models of the last few years share a structural secret that isn't obvious from the outside: most of them aren't dense. They're sparse — built from Mixture-of-Experts layers that let a model have hundreds of billions of parameters while only using a fraction of them on any given token. Understanding why models moved from dense to sparse is the key to understanding how modern LLMs are actually built.
Frontier language models share a structural secret that isn't obvious from the outside: most aren't dense. They're sparse — built from Mixture-of-Experts layers that let a model have hundreds of billions of parameters while using only a fraction on any given token. Understanding why models moved from dense to sparse is the key to understanding how modern LLMs are built.
smolagents is the right choice when you value a small library you can fully understand, the code-agent approach fits your task, and you can execute code safely. It's the wrong choice when you need a big ecosystem, can't sandbox, or your tasks are simple isolated calls. This closing post gives the honest verdict and places smolagents in the landscape.
smolagents is the right choice when you value a small library you can fully understand, the code-agent approach fits your task, and you can execute code safely — and the wrong choice when you need a big ecosystem, can't sandbox, or your tasks are simple isolated calls.
Strands is the right framework when you want to trust a capable model to drive and get out of its way — and the wrong one when you need to guarantee a process. This closing post gives the honest verdict on when to reach for Strands, how it compares to its peers, and how the model-driven approach fits the wider agent landscape.
Strands is the right framework when you want to trust a capable model to drive and get out of its way — and the wrong one when you need to guarantee a process. The honest verdict on when to reach for Strands and how it compares.
The vector-storage decision has a boringly practical answer that cuts against the hype: for most systems, the database you already run with a vector extension beats adding a new specialized system — until scale or specific features force the upgrade.
A boringly practical answer that cuts against the hype: for most systems the database you already run with a vector extension beats adding a specialized system — until scale or specific features force the upgrade.
This is the classic fixed-versus-marginal decision, and it has a clean answer: managed APIs win until your volume is high and steady enough to keep expensive GPUs busy — which is a much higher bar than most teams assume.
The classic fixed-vs-marginal decision with a clean answer: managed APIs win until your volume is high and steady enough to keep expensive GPUs busy — a much higher bar than most teams assume.
The most common question about the two big agent protocols is which one to use — and the answer is almost always "both," because they solve different problems: MCP connects an agent to its tools, A2A connects an agent to other agents.
The most common question about the two big agent protocols is which to use — and the answer is almost always both, because MCP connects an agent to its tools and A2A connects an agent to other agents.
Model selection is the most commonly botched cost decision, because the intuitive answer — pick the cheaper model — is measured by the wrong number. The right number is cost per completed task, and by that measure the more capable model often wins. Paired with it is the least-known lever of all: auditing prompts written for an older model against your current one.
Model selection is the most commonly botched cost decision, because the intuitive answer — pick the cheaper model — is measured by the wrong number. The right number is cost per completed task, priced on the tail not the median.
The most common architecture mistake in applied AI is reaching for fine-tuning to fix a knowledge problem — so the single most useful rule here is that RAG is for knowledge and fine-tuning is for behavior, and long-context is a convenience, not a strategy.
The most common architecture mistake is reaching for fine-tuning to fix a knowledge problem — so the key rule: RAG is for knowledge, fine-tuning is for behavior, and long-context is a convenience, not a strategy.
The model platform decision is usually decided before you compare models at all — by which cloud you're already on, what governance you need, and whether you're renting inference or running it — and getting that framing right matters more than any benchmark.
The model-platform decision is usually settled before you compare models — by which cloud you're on, what governance you need, and whether you're renting inference or running it.
Four popular agent frameworks, four genuinely different philosophies — and the right choice is decided less by features than by how much control you want, how your team thinks, and what you're actually building.
Four popular agent frameworks, four genuinely different philosophies — the right choice is decided less by features than by how much control you want, how your team thinks, and what you're building.
Most AI architecture debates are settled by hype, familiarity, or whoever spoke last — this series settles them by requirements and trade-offs, starting with the meta-framework that every specific decision reduces to.
Most AI architecture debates are settled by hype or familiarity; this series settles them by requirements and trade-offs, starting with the meta-framework every specific decision reduces to.
All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.